Task Complexity as a Moderator of Goal Effects: A ...

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Task Complexity as a Moderator of Goal Effects: A Meta-Analysis Robert E. Wood

Anthony J. Mento

Australian Graduate School of Management University of New South Wales Kensington, New South Wales, Australia

Loyda College in Marjiand

Edwin A. Locke University of Maryland Much evidence exists that supports the use of goal setting as a motivational technique for enhancing task performance; howevei; little attention has been given to the role of task characteristics as potential moderating conditions of goal effects. Meta-analysis procedures were used to assess the moderator effects of task complexity for goal-setting studies conducted from 1966 to 1985 (n = 125). The reliability ofthe task complexity ratings was .92. Three sets of analyses were conducted: for goaldifficulty results (hard vs. easy), for goal specificity-difficulty (specific difficult goals vs. do-best or no goal), and for all studies collapsed across goal difficulty and goal specificity-difficulty. It was generally found that goal-setting effects were strongest for easy tasks (reaction time, brainstorming), d = .76, and weakest for more complex tasks (business game simulations, scientific and engineering work, faculty research productivity), d = .42. Implications for future research on goal setting and the validity of generalizing results are discussed.

Twenty years of empirical research has established that specific, challenging goals lead to higher levels of task perfortnance than no goals, vague goals, or easy goals (Locke, Shaw, Saari, & Latham, 1981; Pinder, 1984). Interest is now turning to identifyitig the theoretical limits of goal setting, for example, the variables that moderate the positive performance effects of goals (e.g., Austin & Bobko, 1984; Locke et al., 1981; Naylor & Ilgen, 1984). One variable of potential importance to the theory of goal setting is task complexity. Tasks are an integral part of all studies of human performance, and task characteristics have been suggested as moderators in a diverse range of areas, including job design (Hackman & Oldham, 1980), personnel selection (e.g., Peterson & Bownas, 1982), informatioti processing and decision making (e.g., Streufert & Streufert, 1978), and psychomotor activities (Fleishman, 1975). Locke et al. (1981) have speculated that goals will have less direct effects due to effort, attention, and persistence on complex tasks, and that indirect effects due to strategy development will become more important to performance. Further, Wood (1985) has argued that specific, challenging goals may lead to suboptitnal search procedures on complex tasks (e.g., Baumler, 1971; Hubei; 1985). From this literature we hypothesize that goals, on the average, will have less pronounced perfortnance effects on complex tasks than on simple tasks. The study of characteristics such as complexity has suffered Funding for this research was provided to the second author through a Summer Research Grant fhxn the Sellinger School of Business and Management at Loyola College. Correspondence conceming this article should be sent via air mail to Robert E. Wood, Australian Graduate School of Management, University of New South Wales, PO. Box 1, Kensington, New South Wales 2033, Australia.

from a lack of standardization in definition and a confounding of individual and task characteristics (Fleishman. 1975; Hackman, 1969; Weick, 1965; Wood, 1986). As a result, there has been a lack of integration ofthe evidence for task effects from sttidies in different areas and, in some cases, inconsistencies in results in a given area (Wood, 1986). The problems of inconsistent results are evident in goal-setting studies that have considered the effects of task characteristics. Frost and Mahoney (1976) found that challenging goals led to higher performance than did easy goals on a simple task but not on a complex task. Jackson and Zedeck (1982), howevei; reported no such differences for two tasks of differing complexity. Frost and Mahoney also found that subjects with specific, challenging goals outperformed subjects with nonspecific goals by more on the complex task than on the simple task. Baumler (1971), using a much more complex set of tasks, obtained exactly the opposite results. The inconsistencies in restilts between these studies are probably due, in part, to different manipulations of task axnplexity. These were path-goal multiplicity (Frost & Mahoney, 1976), manual versus cognitive tasks (Jackson & Zedeck. 1982). and interdependence of decisions (Baumlei; 1971). Moreovei; in two of these three studies, the tasks used in the complex condition were not vwy complex. In the Frost and Mahoney study, the complex task was a jigsaw puzzle with all pieces painted the same color. In Jackson and Zedeck's study, subjects performing the complex task had to calculate floor covering requirements from a plan for a single-level, three-room building. Neither of these tasks approach the complexity of tasks such as professional counseling, investment decision making, or air-traffic control (Wood. 1986). Therefore, the modoating eflfects of tasks may not have been adequately tested in either of these studies because of weak manipulations of complexity. Baumler (1971) used a more powerful manipulation of taik

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complexity; however, his study only examined one goal attribute (spedfidty). Also, becatise the levels of goals assigned to subjects were not reported, the possibility that the lower performance for specific goals in the complex condition was due to subjects setting lower goals, cannot be ruled out. Therefore, to this point, no adequate test of task complexity as a moderator of goal efiects has been conducted. Of course, goal setting has been found to have positive performance efiects on tasks of varying complexity, ranging from simple brainstorming tasks (e.g., Locke, 1967) to college course work (Locke A Bryan, 1968) to complex scientific work (Latham. Mitchell, & Dossett, 1978). However, this cannot be accepted as evidence that tasks do not moderate the goal performance effects of goals without considering the size of effects for tasks of differing complexity. It is possible, for the reasons mentioned earlier, that goals have a relatively large performance effect on simple tasks and a smaller, but significant, effect on complex tasks. If this were tht case, then the correct test for the moderator effects of task complexity would be a comparison of the magnitude of effects between goal setting sttidies that have used simple tasks and those that have used complex tasks. Metaanalysis is a technique that allows such a comparison. Within the last decade, meta-analysis techniques have been developed that permit the quantitative aggregation of results across studies (Hunter, Schmidt, & Jackson, 1982; Rosenthal, 1978). An advantage of meta-analysis for our current purposes is that the influence of moderator variables such as task complexity can be examined. This first requires that the results of all sttidies be converted to a common statistic, either d or rî, (Hunter et al., 1982). so that results can be cumulated across studies. After correcting for as many ofthe different sources of error variance as possible (i.e., sampling erroi; range restriction, reliability of measures, etc.), any remaining variance in the results can be tested to see if differences in size of effects between studies are due to the hypothesized moderator. If the majority ofthe variance in the results of studies is removed by the corrections fcM" errors, then the ai^gument for any moderator effect can be rejected without any further testing. Schmidt, Hunter, and Pearlman (1982) have suggested that if approximately 75% of the total variance across studies can be accounted for by sampling, then any apftarent moderator effect is most likely due to capitalization on chance. A test of the moderaUu- hypothesis is the strength ofthe relationship between the study statistic and the moderator variable. This can be established by regressing the moderator variable on the study statistic (Mabe & West, 1982; Steel & OvaUe, 1984). V^th the regression approach, testing the significance of the beta weight is a test of the moderator effect. Because it allows for tests to be based on the total sample, this approach is less susceptible to chance effects than the subgroup approach described by Hunter et al. (1982). Howevei; it does require at least an ordinal measure on the moderator for the purposes of regression analyses. Because task complexity is ordinal in nature, as distinct friMn other commonly used moderators—such as sex— which are nominal categories, this approach was considered apprqxiate for tbe moderator test in the current study. The validity of the lesulu also depends on the reliability ofthe measure

ofthe moderator variable. The reliability ofthe task-complexity scores in the present study was very high (r = .92). Thus, the purpose of the present sttidy was to examine the moderating effects of task complexity across existing goal-setting studies, using a meta-analysis approach. Based on earlier speculations (Locke et al., 1981; Wood, 1985), laiger effects should be found for studies using simple tasks and smaller effects for sttidies with more complex tasks. The specific hypotheses to be tested are as follows: Hypothesis 1. The positive performance effects of specific and difficult goals (vs. do-best goals) will be greater on simple tasks than on complex tasks. Hypothesis 2. The positive performance effects of difficult goals (vs. moderate or easy goals) will be greater on simple tasks than on complex tasks.

Method To identify studies appropriate for the meta-analyses, we manually searched the Psychological Abstracts and the Social Science Citation Index and systematically reviewed the Journal of Applied Psychology, Academy ofAfanagement Journal, Organizational Behavior and Human Performance, and Personnel Psychology from January 1966 to December 1984 (see Appendix). Studies were excluded if an effect size could not be calculated. In the goal-difficulty analysis, three studies were excluded because they contained an experimental artifact in the easy-goal condition that involved instructing subjects to stop working when the easy goal was reached. This instruction may serve to artifactually inflate the goaldifficulty-performance relationship. A number of studies were excluded from the goal difficulty and goal specificity-difficulty meta-analyses that used a within-subjects, as opposed to a between-subjects, experimental design. In discussing quantitative approaches to literature reviews. Green and Hall (1984) cautioned that it is incorrect and inappropriate to include data from a within-subjects design into a metaanalysis because effect sizes cannot be accurately computed. Unpublished studies known to the authors were included. A list of specific studies excluded from the meta-analyses can be found in Mento, Steel, and Karren (1987). The remaining studies available for the meta-analyses included 72 studies of goal-difficulty effects (difficult vs. moderate or easy) and 53 studies of goal specificity-difficulty effects (specific, difficult vs. doyour-best or no goal). Full details of studies from 1966 to August 1984 are reported in Tables I and 2 in the Mento et al. (1987) article. The unpublished studies that were added to the Mento et al. studies were Chesney (1986), Shaw (1984), and Smith, Locke, and Barry (1986). Studies included correlational and experimental designs as well as laboratory and field settings. Moderator analyses conducted by Mento et al. showed that the size of effects for goal difficulty and goal specificitydifficulty were not affected by study design. For experimental studies, results were converted to the effect-size statistic d. Results from correlational studies werefirstconverted to pointbiserial r (r^). These were then converted to an effect-size d to provide a common statistic for cumulating effect sizes across both correlational and experimental studies. Because the size ofthe point-biserial correlation is affected by the relative proportion of cases in the two treatment groups, effect sizes for all of the individual studies were corrected for differences in subgroup sample sizes when appropriate (Hunter et al., 1982). When cumulating results, effect sizes were weighted by the sample sizes for studies, as recommended by Hunter et al. (1982). Of particular importance to the analyses and to our later discussion of resulu were the reliabilities of measures used for the performance criteria (in all studies) and the predictor variables (in field studies that

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used a questionnaire measure of goal difficulty and goal specificity). The average reported reliabilities of criteria were .80 for studies using ratings of performance and .92 for studies with objective measures of performance. For the predictor variables in field studies, the average reliabilities were .72 (lowest .66) for goal difficulty and .81 (lowest .70) for goal specificity. These reliability estimates were used to correct effect-size statistics and variance estimates (si) for error of measurement (cf. Hunter et al. 1982; Mabe A West, 1982). Next, sampling error variance was calculated using the formulas for sampling error modified to take into account the effect of the corrections for erron in measurement on sampling error, according to Hunter et al., 1982. Finally, the remaining unexplained variance (5^) was determined after correcting for measurement error and sampling. For both the goal difficulty and the goal specificity-difficulty studies, the ratio of sampling error variance to total true variance (i.e., 5^) was less than the 75% cutoff suggested by Schmidt et al. (1982). Therefore, supplemental analyses to test for the moderator effects of task complexity were considered appropriate. Task-complexity scores for each ofthe 125 studies included in the meta-analyses were obtained through ratings ofthe tasks used. Descriptions ofthe study tasks were given by one ofthe present authors to the other two, who independently rated each of them on a 10-point complexity scale on the basis ofthe general definition of task complexity in Wood (1986). Complexity involves three aspects: component complexity (number of acte and information cues involved), coordinative complexity (type and number of relationships among acts and cues), and dynamic complexity (changes in acts and cues and the relationships among them). These were used to code the tasks used in goal-setting studies on a common scale of complexity. The correlation between the two independent sets of ratings was .92. For initial ratings that differed by 2 or more points on the iO-point scale, the raters discussed their differences and reached a consensus on the appropriate rating. This then became the complexity score that was used in the moderator analyses. For all other tasks, the complexity scores used in the analyses were the averages of two ratings. Examples ofthe complexity scores (rounded) assigned to different tasks from goalsetting studies are shown in Figure I. Note that the highest rating given on the IO-point scale was 7. The task-complexity scores were entered as the moderator variable into a regression analysis, using the corrected study sutistic (i.e., S3)

2- -

Low

Modarata

HiQh

Task Complaxity

Figure 3. Goal effect as a function of task complexity (separately by each set>

The magnitude of goal effects on performance was greater on simple tasks than on complex tasks, and these results were not an artifact of differences in the reliability ofthe criterion measures. Therefore, to this point, task complexity is the only variable that has been shown to have a significant and robust moderating effect on the performance gains that result from specific, difficult goals. Mento et al., (1987) found inconsistent evidence of a moderator effect for feedback. (This may have been due to the fact that most ofthe studies in which feedback was manipulated could not be included in their moderator analysis.) This finding complements the theoretical discussion and evidence for the strategy development effects of goals presented by Wood, Locke, and Smith (1986) and others (e.g.. Campbell, 1984; Locke et al., 1981) who have bûn to examine the processes by which goals affect performance on complex tasks. Of particular relevance to this study is the Wood et al. (1986) model, which predicts that the performance effects of goals will be lagged on complex tasks. This was based on studies by Shaw (1984) and Smith et al. (1986), who found that specific challenging goals lead to significant performance effects—but only in later trials—of a complex clerical task and a complex decision game, respectively. It is possible that differences in goal effects between complex and simple tasks may disappear over repeated performances of a task, as individuals develop effective strategies for the performance of complex tasks. Therefore, future research into the effects of goals on complex tasks should use multitrial or longitudinal methods that allow for the devekipment of lagged effects (e.g., Campbell. 1984; Ivancevich, 1976; Shaw, 1984; Smith etal., 1986). The benefits of more rigorous, standardized definitions of task characteristics, such as those advanced by Wood (1986), need to be recognized by goal researchers. The earlier review of goal-setting studies that examined task effects deaily demonstrated the problems associated with inadequate conceptualizations of and hypotheses about moderator effects. The coding of study tasks for the meta-analysis has allowed us to examine the previously uncontrolled sources of variation in goal-setting studies that were due to the task characteristic of cranplotity. Research is now needed to focus on how goal effects vary as a function of different types of complexity and the underiying processes by which goals affect performance on different types of tasks. For example, we could consider how goals aflfect perfbrmance on complex psychomotor tasks. Much ofthe discussion about the effects of goals on complex tasks is focused on cognitive

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Table 3 Meta-Analysis ds for Task Comple.xity Subgroups, hy Quintiles, for All Studies Combined* Task complexity rating 1.0-2.0 2.25-2.75

3.0-3.25 3.5-5.0 5.25-7.0

n 2.702 2,710 2,082 4,234 2,455

No. of studies 27 25 20 28

25

d" .Ibll 4485 4370 .4697 .4173

• Effects of goal specificity-difficulty and goal difficulty are combined. '' ds are meta-analytic corrected for measurement error.

processes, such as search, information processing, and strategy development, which are typically required in the performance of complex decision tasks (e.g., Campbell, 1984; Wood et al., 1986). However, these cognitive processes may have less influence on the motor aspects of tasks in which performance requires a highly programmed set of behaviors as well as goals that focus the person's attention on the outcomes to be achieved. In fact, without a set of well-learned and effective motor programs, the attentional demands of outcome goals ma\ undermine a person's execution of complex motor activities. Therefore, although the results would fit with our moderator hypothesis (i.e., outcome goals would lead to lower performance on more complex tasks), the underlying processes by which goals affect performance on complex psychomotor tasks may be quite different from those for more cognitive types of tasks. One final implication of our results concems the validity generalization arguments that Schmidt and Hunter (1977) have developed in relation to selection processes, but more recently have applied to other organization interventions, including goal setting (Hunter & Schmidt, 1983). There are two aspects to the Schmidt and Hunter arguments. Thefirstis the validity generalization thesis that observed effects (validity of selection tests, performance effects of goals, etc.) generalize across a variety of organizations, jobs, and tasks. The results reported here, and in Mento et al. (1987), clearly support the thesis that the positive performance effects of goal difficulty-specificit\ are highly generalizable. The second aspect ofthe Schmidt and Hunter (1977) arguments relates to the situational specificity hypothesis. That is, differences in situations will be associated with differences in the magnitude of effects. There is evidence that task complexity may be an important moderator across a range of performance determinants. For example. Hunter (198 3) found that job complexity afTected the selection validities for tests of cognitive abilities, with validities increasing with job complexity. This finding has been replicated by Gutenberg, Arvey, Osbum. and Jeanneret (1983), whose measure of job complexity was based on selected dimensions from the Position Analysis Questionnaire (PAQ). In both the Hunter and Gutenberg et al. studies, complexity was defined as the level of information-processing and problem-solving demands of the job. These task demands are products of the coordinative and dynamic types of task complexity defined by Wood (1986) and used to classify tasks in the present study.

In another area of selection research, tentative support has been found for the hypothesis that task complexity moderates the effectiveness of realistic job previews (RJPs) in reducing tumover (McEvoy & Cascio, 1985). The RJPs were found to be less effective in reducing tumover for entry-level nonmanagement jobs of low complexity than for more complex jobs. However, this result was based on a small number of studies for each level of task complexity, and the possibility of capitalization on chance could not be ruled out (McEvoy & Cascio, 1985, p. 349). There is an interesting complementarity between our findings and those in the selection research area. Tests of cognitive abilities and RJPs are most effective in selecting for complex jobs, in which goal setting is least effective, and least effective for less complex jobs, in which goal setting is most effective. There is evidence that the effects of goal setting are mediated by strategy development—information processing and problem solving—on complex tasks (Wood et al.. 1986). In the future, researchers need to consider the relative effects of cognitive abilities, information sharing (as in RJPs and participation) and goal setting, on strategy development. Our support for the situational specificity hypothesis has implications for generalizing about the size of effects or productivity gains from goal setting across different types of tasks. The average effect-size goals for all ofthe studies combined was d = .521, equivalent to a 10.39% increase in productivity (Mento et al.. 1987). For tasks coded at the low, moderate, and high levels of complexity, the equivalent productivity increases are 12.15%, 9.12%, and 7.79'' , respectively.

10 2 0 IN.271

2 25 •2 75 I N . 251

3 0 3 25 IN.201

3 55 0 IN.2)1

5 25-7 0 IN.24)

Task Complexly

Figure 4. Goal effect as a function of task complexity (combined studies) by quintiles.

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Locke, E. A., A Bryan, J, F, (1968). Grade goals as detcrminantt of academic achievement. Journal qf General PsychtAogy 79,217-228. Locke, E. A., Shaw, K. N,, Saari, L. M., A Latham, G. P. (1981). Goal setting and tuk performance: 1969-1990. Psychological Bulletin, 90, 125-152. Mabe, R A., A West, S, G. (1982). Vklidity of self-evaluation of ability: A review and meU-analyns. Journal qf Applied Psychology, 67, 280296. McEvoy, G. M., A Caado, W. F. (1985). Strategies for reducing employee turnover A meU-analysis. Journal ofApplied Psychology 70, 342-353. Mento, A. J., Steel, R. R, A Karren, R. J. (1987). A meta-analytic study ofthe effects ofgoal setting on task performance: 1966-) 984. Organizational Behavit>r and Human Decision Processes, 39, 52-83. Nayloi; J. C , A Ilgen, D. R. (1984). Goal-setting: A theoretical analysu of a motivational technology. In B. Staw A L. L. Cummings (Bis.). Research in organizational behavior {WcA. 6, pp. 95-140). Greenwich, CT JAI Press. Peterson, N. G., A Bownas, D. A. (1982). Skill, task structure and performance acquisition. In M. D. Dunnette A E. A. Fleishman (Eds.), Human performance and productivity (Vol. I, pp. 49-105). Hilhdak, NJ: Ertbaum. Pinder, C. (1984). Work motivation. Glenview, IL: Scott, Foresman. Rosenthal, R. (1978). Combining results from independent studies. Psychological Bulletin, 85, 185-193. Schmidt, F L., A Huntei; J. E. (1977). Devekipment of a general solution to the problem of validity generalization. Journal qf Applied Psychology, 62, 529-540. Schmidt, F. L., Hunter, J. E., A Peadman, K. (1982). Progress in validity generalization: Comments on Callender and Osbom and further developments. Journal (ÂppliedPsychology, 67,835-845. Shaw, K. N. (1984). A laboratory investigation ofthe relationships among goals, strategies, and task performance. Unpublished doctoral dissertation. University of Maryland. Smith, K. G., Locke, E. A., A Barry, D. (1986). Goal setting, planning and organizational performance: An experimental study Unpublished manuscript. University of Maryland. Steel, R. R, A Ovalle, N. K. (1984). A review and meu-analysis of research on the relationship between behavioral intentions and employee tumover. Journal ofApplied Psychology 69,673-686. Streufert, S., A Streufert, S. (1978). Behavior in the complex environwen/. New York: Wiley. Weick, K. E. (1965). Laboratory expedmenution with oiganizations. In J. G. March (Ed.), Handbook cf organizations. Chicago: RandMcNaUy. Wood, R. E. (1985, August). Task complexity and goal effects, ^vpa presented at the meeting ofthe National Academy of Management, San Diego, CA. Wood, R. E. (1986). Task complexity: Definition ofthe construct. Organizational Behavior and Human Decision Processes, 37,60-82. Wood, R. E., Locke, E. A., A Smith, K. (1986). Goal setting and strategy effects on complex tasks: A theoretical analysis. Unpublished manuscript. University of New South Wales, New South VSUes, Australia.

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Appendix References for the Meta Analysis The letters at the end of each reference mean the following: Correlati(»al studies are designated Cand experimental articles E. Those articles involving goal difficulty have a D and those comparing specific, difficult goals with do-best goals are designated SD. Andrews, F. M., A Farris, G. F. (1972). Time pressure and performance of scientists and engineers: A five year panel study. Organizational Behavior and Human Performance, 8, 185-200. C, D Bandura, A., A Cervone, D. (1983). Self-evaluative and self-efficacy mechanisms goveming the motivational effects of goal systems. Journal of Personality and Social Psychdogy, 45, 1017-1028. SD, E Bandura, A., A Schunk, D. H. (1981). Cultivating competence, selfefficacy, and intrinsic interest through proxinud self-motivation. Journal ofPersonality and Social Psychology 41, 586-598. SD, E Basset, G. A. (1979). A study of the effecte of task goal and schedule choice on work performance. Organizational Behavior and Human Performance. 24. 202-227. D, E Bavelas, J. B., A Lee, E. S. (1978). Effecte of goal level on performance: A tradeoff of quantity and quality. Canadian Journal of Psychology. i Z 219-240. D,E Becker, L. J. (1978). Joint effect of feedback and goal setting on performance: A field study of residential energy conservation. Journal of Applied Psychdogy, 63.428-433. D, E Blumenfeld, W. S., A Leidy, T. R. (1969). Effectiveness of goal setting as a management device: Research note. Psychological Reports. 24. 752.SD,C Burke, R. J., A Wilcox, D. S. (1969). Characteristics of effective employee performance review and development. Personnel Psychology. 22, 291-305. SD,C Campbell, D. J. (1984). The effect of goal contingent payment on the performance of a complex task. Personnel Psychology. 3 7. 23-40. D,E Campbell, D. J., A Ilgen, D. R. (1976). Additive effecte of task difficulty and goal setting on subsequent task performance. Journal of Applied Psychology 61. 319-324. D, E Chesney, A. (1986). An examination ofthe relation among goals, strategies, and performance: A simulation study. Unpublished doctoral dissertation. University of Maryland. D, E Dachler, H. P., A Mobley, W. H. (1973). Construct validation of an instrumentality-expectancy-task-goal model of work motivation: Some theoretical boundary conditions [Monograph]. Journal ofApplied Psychology, 58, 397-418. D, C Dossett, D. U, Latham, G. P, A MitcheU, T R. (1979). The effecte of assigned versus participatively set goals, KR, and individual differences when goal difficulty is held constant. J»umal of Applied Psy-

dmlogy 64,29l-29S.D,C Dooett, D. L., Latham, G. P, A Saari, L. M. (1980). The impact of goal setting on survey retums. Academy ofAfanagement Journal, 23, 561-567. SD,E Erez, M. (1977). Feedback: A necessary condition for the goal settingperformance relationship. Journal of Applied Psychology, 62, 624627. D , C Frost, P. J., & Mahoney, T. A. (1976). Goal setting and the task process: 1. An interactive influence on individual performance. Organizational Behavior and Human Performance, 17, 328-350. SD, E Garland, H. (1982). Goal levels and task performance: A compelling replication of lome compelling results. Journal of Applied Psychology 67,245-248. D, E Garland, H. (1983). Influence of ability, assigned goals, and normative infomution on pcnonal goals and performance: A challenge tojthe

goal attainability assumption. Journal of Applied Psychology, 68. 2 0 30. D, E Garland, H. (1984a, August). A cognitive mediation theory of task goals and human performance. Paper presented at the 44th Annual Convention ofthe Academy of Management, Boston. D, E Garland, H. (1984b). Relation of effort-performance expectancy to performance in goal-setting experimente. Journal of Applied Psychology, 69. 79-84. SD, E Hall, D. T, & Foster, L. W. (1977). A psychological success cycle and goal setting: Goals, performance, and attitudes. Academy ofAfanagement Journal. 20. 282-290. D, C Hall, D. T, & Hall, F S. (1976). The relationship between goals, performance, success, self-image, and involvement under different oiganizational climates. Joumal of Vocational Behavior, 9. 267-278. D, C Hamner, W. C , & Hamett, D. L. (1974). Goal-setting, performance and satisfaction in an interdependent task. Organizational Behavior and Human Performance. 12. 217-230. D, E Ivancevich, J. M. (1976). Effecte of goal setting on performance and job satisfaction. Journal ofApplied Psychology. 61. 605-612. SD, E Ivancevich, J. M. (1977). Different goal-setting treatmente and their effecte on performance and job satisfaction. Academy of Management Journal. 20, 406-419. SD, C Ivancevich, J. M. (1982). Subordinates' reactions to performance appraisal interviews: A test of feedback and goal'Setting techniques. Journal ofApplied Psychology. 67, 581-587. SD, E Ivaiicevich, J. M., & McMahon, J. T. (1977a). Black-white differences in a goal-setting program. Organizational Behavior and Human Performance. 20. 287-300. D, C; SD, C Ivancevich, J. M., & McMahon, J. T. (1977b). Education as a moderator oi %oaH-xnin%cStc\i\tnes&. Journal of Vocational Behavior. 11. Si94.D,C;SD,C Ivancevich, J. M., & McMahon, J. T. (1977c). A study of task-goal attributes, higher order need strength, and performance. Academy of Afanagement Journal. 20, 552-563. D, C; SD, C Ivancevich, J. M., & McMahon, J. T. (1982). The effecte of goal setting, extemal feedback, and self-generated feedback on outcome variables: A field experiment. Academy ofAfanagement Journal. 25, 359-372. SD,E Jackson, S. E., & Zedeck, S. (1982). Explaining performance variability: Contributions of goal setting, task characteristics, and evaluative contexte. Journal ofApplied Psychology. 67. 759-768. D, E Kaplan, R., A Rothkopf, E. Z. (1974). Instructional objectives as directions to leamers: Effect of passage length and amount of objectiverelevant content. Journal of Educational Psychology, 66. 448-456. SD,E Kim, J. S. (1984). Effect of behavior plus outcome goal setting and feedback on employee satisfaction and performance. Academy of Afanagement Journal. 27, 139-149. SD, E LaPorte, R. E., A Nath, R. (1976). Role of performance goals in prose leaming. Journal ofEducational Psychology, 68, 260-264. D, E Latham, G. P., A Kinne, S. B. III. (1974). Improving job performance through training in goal setting. Journal of Applied Psychology 59, 187-191. SD,E Latham, G. P., A Locke, E. A. (1975). Increa^ng productivity with decreasing time limits: A field replication of Parkinson's Law. Journal of Applied Psychology, 60, 524-526. SD, E Latham, G. P, A Marshall, H. A. (1982). The effecte of self-set, participatively set, and assigned goals on the performance of govemment employees. Personnel Psychology, 35, 399-404. D, C Utham, G. R, MitcheU, T R., & Dossett, D. L. (1978). The importance

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of participative goal setting and anticipated rewards on goal difficulty and job perfonnance. Journal of Applied Psychology, 63, 163-171. D,C Utham, G. R, A Saari, L. M. (1979a). The effects of holding goal difficulty constant on assigned and participatively set goals. Academy (Âianagement Journal, 22, 163-168. SD, E Utham, G. P, A Saari, L. M. (1979b), The imporUnce of supportive relationships in goal setting. Journal ofApplied Psychology, 64, 151156.D,C;SD,E Utham, G. P., & Steel, T. P (1983). The motivational effecte of participative versus assigned goal setting on performance. Academy ofAfanagement Journal, 26,406-417. D, C Utham, G. P, & Yukl, G. A. (1976). The effecte of assigned and participative goal setting on performance and job satisfaction. Journal of Applied Psychology, 6/. 166-171. SD, E Locke, E. A. (1966). The relationship of intentions to level of performance. Journal ofApplied Psychology, 50. 60-66. D, E Locke, E. A. (1967). The motivational effecte of knowledge of resulte: Knowledge or goal-setting? Journal of Applied Psychology, 51, 324329. SD, E Locke, E. A. (1968). The effecte of knowledge of results, feedback in relation to standards, and goals on reaction time performance. American Journal of Psychology 81. 566-574. D, E Locke, E. A. (1982). Relation of goal level to performance with a short work period and multiple goal levels. Journal of Applied Psychology. 67. 512-514. D,E Locke, E. A., & Bryan, J. F. (1966). Cognitive aspecte of psychomotor pefformance: The effects of performance goals on level of performance. Journal ofApplied Psychology, 50,417-420. SD, E Locke, E. A., A Bryan, J. F (1967). Performance goals as deteripinante of level of performance and boredom. Journal of Applied Psychology, 51. I2O-13O.D,C.SD,E Locke, E. A., & Bryan, J. F (1968). Grade goals as determinante of academic achievement, youma/o/Gen^ra/P5>c/io/o^, 79, 217-228. D,C Locke, E. A., & Bryan, J. F. (1969). The directing function of goals in task performance. Organizational Behavior arui Human Performance. 4, 35-42. D, E; SD, E Locke, E. A., Bryan, J. F, A Kendall, L. M. (1968). Goals and intentions as mediators ofthe effecte of monetary incentives on behavior. Journal of Apiîed Psychology, 52. 104-121. D, C Locke, E. A., Cartledge, N., & Knerr, C. (1970). Studies ofthe relationship between satisfaction, goal setting and performance. Organizational Behavior and Human Perfonnance, 5, 135-158. D, C Locke, E. A., Frederick, E., Lee, C , A Bobko, P. (1984). Effecte of selfefficacy, goals, and task strategies on task performance. Jourruil of Applied Psychology, 69, 241-251. D, C Locke, E. A., Mento, A. J., A Katcher, B. (1978). The interaction of ability and motivation in performance: An exploration ofthe meaning of moderators. Personnel Psychology 31,269-280. SD, E Locke, E. A., A Shaw, K. N. (1984). Atkinson's inverse-U curve and the missing cognitive variables. Psychological Reports, 55, 403-412. D,C London, M., A Oldham, G. R. (1976). Effecte of varying goal types and incentive systems on performance and satisfaction. Academy of Afanagement Journal, 19, 537-546. D, E Masters, J. C , Furman, W., A Barden, R. C. (1977). Effecte ofachievement standards, tangible rewards and self-dispensed achievement evaluations on children's task mastery. Child Development, 48, 217224. D, E Matsui, T , Okada, A., A Kakuyama, T (1982). Influence of achievement need on goal setting, performance, and feedback effectiveness. Journal of Applied Psychology 67,645-648. D. C McCaul, K. D., & Kcpp, J. X (1982). Effects of goal setting and commit-

ment on increasing metal recycling. Journal of Applied Psychology 67, 377-379. SD,E Mento, A. J., Cartledge, N. D., A Locke, E. A. (1980). Maryland vs. Michigan vs. Minnesota: Another kx>k at the relationship of expeotancy and goal difficulty to task performance. Organizatimuil Behavior and Human Performtmce, 25,419-440. D, E Mossholder, K. W. (1980). Effecte of extemaUy mediated goal setting on intrinsic motivation: A laboratory experiment. Journal cf Applied Psychology 65, 202-210. SD, E Motowidlo, S., Loehi; V., A Dunnette, M. D. (1978). A laboratory study ofthe effecte of goal specificity on the relationship between probability of success and performance. Journal cf Applied Psychology 23, 172-179. D,E Mowen, J. C , Middlemist, R. D., & Luther, D. (1981). Joint effects of assigned goal level and incentive structure on task performance: A laboratory study. Journal qf Applied Psychology, 66, 598-603. D, E Nemeroff, W F, A Cosentino, J. (1979). Utilizing feedback and goal setting to increase performance appraisal interviewer skiUs of managers. Academy ofAfanagement Journal. 22, 566-576. SD, E Oldham, G. R. (1976). The motivational strategies used by supervisors: Relationships to effectiveness indicators. Organizational Behavior and Human Performance, 15,66-86. D, C Oigan, D. W. (1977). Intentional vs. arousal effecte of goal setting. Organizational Behavior and Human Performance, 18, 377-389. D, E Peters, L. H., Chassie, M. B., Lindholm, H. R., O'Connor, E. J., A Kline, C. R. (1982). The joint influence of situational constrainte and goal setting on perfonnance and affective outcomes. Journal ofAfanagement. 8, 7-20. D, E Pritchard, R. D., A Curtis, M. I. (1973). The influence of goal setting and financial incentives on task performance. Organizational Behavior and Hunum Performartce. 10, 175-183. D, E Rakestraw, T L. Jr., A Weiss, H. M. (1981). The interaction of social influence and task experience on goals, perfonnance, and performance satisfaction. Organizational Behavior and Human Performance. 27,126-lM. D.C Ronan, W. W, Utham, G. P, & Kinne, S. B. (1973). Effecte of goal setting and supervision on worker behavior in an industrial situation. Journal of Applied Psychology, 58, 302-307. SD, C Rosswork, S. G. (1977). Goal setting: The effecte on an academic task with varying magnitudes of incentive. Journal of Educational Psychology, 69,1\O-1\5.SD,E Rothkopf, E. Z., A BiUington, M. J. (1975). A two-factor model ofthe effect of goal-descriptive directions on leaming from text Journal of Educational Psychology 67, 192-204. D, E; SD, E Rothkopf, E. Z., A BiUington, M. J. (1979). Goal-guided leaming from text: Inferring a descriptive processing model from inspection times utdcytmovcmenis. Journal of Educational Psychology 71,310-327. SD,E Rothkopf, E. Z., A Kaplan, R. (1972). Exploration ofthe effect of density and specificity of instructional objectives on leaming from text Journal of Educational Psychology 63,295-302. SD, E Sales, S. M. (1970). Stxne effecte of role oveHoad and role underload. Organizational Behavior and Human Performance, 5,592-608. D,E Shaw, K. N. (1984). A laboratory investigation cfthe relationships among goals, strategies, and task performance. Unpublished doctoral dissertation, Univenity of Maryland, D, E Smith, K. G., Locke, E. A., A Barry, D. (1986). God setting, planning and organizational perfonnance: An experimental study Unpublished manuscript. University of Maryland. D, E Steen, R. M. (1975). Task-goal attributes, achievement and supervisory performance. Organizational Behavior and Human Performance, 13, 392-403. D, Q SD. C Strang, H. R., Uwrence, E C , A Fowtei; P. C (1978). Eficts of as-

TASK COMPLEXITY signed goal level and knowledge of results on arithmetic computation: A laboratory study. Journal ot \pplied Psvchoioay. 63. 29-39. D. E Taylor, M S.. Locke, E. A., Lee, C . & Gist. M. (1984). Type A behavior and facult) research productivity: What are the mechanisms? Organizational Behavior and Human Performance. 34. 402-418. D,C Terborg, J. R. (1976). The motivational componente of goal setting. Journal ofApplied Psychology. 61. 613-621. D. C Terborg, J R.,& Miller. H. E.(I978). Motivation, behavior and performance: A closer examination of goal settingand monetary incentives. Journal ofApplied Psychology. 63. 2*^-39. SD, E limstot, D. D., Bell, C. H., Jr., & Mitchell, T R. (1976) Effects of job enrichment and task goals on satisfaction and productivity: Implications for job design. Journal of Applied Psychology: 61. 379-394. SD, E Wexley, K N., & Nemeroff. W F. (1475). Effectiveness of positive reinforcement and goal setting as methods of management development. Journal ofApplied Psychology. 60. 446-450. SD, \

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White. S.I Mitcfiell. I R , & Bell. C. H..Jr.(lSi77).Goal setting evaluation apprehension, and social cues as determinante of job performance and job satisfaction in a simulated organization. Journal o/ Applied Fsn h,>l«i;\. 62. 665-673. SD. E Wofford. J. C (1982) Experimental tests ofthe goal-encrgy-effort requirement theory of work motivation. I'sychalnKii-al Reports. 50. 1259-1273. SD.E Wood. R. E . & Locke. I A. (1 "iXA). The effects ol self-efficacy on academic pcrtornuirhc Unpublished manuscript. University of New Soutfi VValL-s, New South Wales, Australia. D. C Yukl. G. A., & Latham, G P (1978). Interrelationships among employee participation, individual differences, goal difficulty, goal acceptance, goal instrumentality and performance. Pfr\i>nnei Psvchoio^v. i / , 305-32.1 D. C

Received July 14, 1986 Revision received February 5, 1987 Accepted December 12. 1986 •

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