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Cognitive models of motivation, expectancy theory and effort: An analysis and empirical test
ORGANIZATIONAL 3EHAVIOR AND HUMAN PERFORMANCE
20, 129-148 (1977)
Cognitive Models of Motivation, Expectancy Theory and Effort: An Analysis and Empirical Test LAWRENCE H. PETERS University of Texas at Dallas
Using a General Cognitive Model which specifies that environmental conditions affect behavior only through the perceptions that persons have of their environment, three linkages were defined and tested with respect to expectancy theory predictions of effort expenditure. These linkages specified that environmental conditions in accord with expectancy theory predictions should affect effort expenditure; that these same conditions should affect persons' perceptions of the situation; and that the perceptions, in turn, should be predictive of el'fort expenditure. In order to test these predictions, expectancies and instrumentalities, in addition to valences, were experimentally manipulated in a completely crossed design in which a measure of effort was the dependent variable. Further, perceptual measures of each of the expectancy theory variables were assessed. The results of this investigation supported each of the links in the General Cognitive Model and are interpreted within that model.
The assumption that an individual is influenced by his beliefs about the situation in which he finds himself and by his beliefs about the consequences of his actions is not new to students of organizational behavior. Expectancy theory of motivation (Vroom, 1964) represents such a cognitive approach to understanding human behavior in organizations. Underlying cognitive models of motivation are two assumptions, only one of which is systematically treated in the research literature on expectancy theory. The first assumption is that behavior is rooted in the belief system of a person. With respect to expectancy theory, this involves beliefs about being able to attain various behavioral alternatives (expectancy beliefs) and beliefs about the anticipated consequences upon attaining these behavioral alternatives (instrumentality beliefs). The second assumption is that these beliefs are based, at least in part, on the actual environment in which one finds himself. This is to say no more than one's work-related beliefs are influenced by relevant characteristics of his work situation. It is assumed that for beliefs to effectively guide behavior in a particular
I would like to acknowledge Daniel R. Ilgen, David L. Ford, and Rabi S. Bhagat for their critical review and suggestions on earlier drafts of this manuscript. This research was supported by the Army Research Institute for the Behavioral Sciences under Grant No. DAHC 19-74-G-002, Daniel R. Ilgen, Principal Investigator. Requests for reprints can be addressed to the School of Management and Administration, The University of Texas at Dallas, Richardson, Texas., 75080. 129 Copyright © 1977 by Academic Press, Inc. All rights of reproduction in any form reserved.
ISSN 0030-5073
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work situation, they must bear some substantial relationship to reality (Vroom, 1964). Taken together, environmental circumstances are incorporated into one's belief system and these beliefs are then processed according to a cognitive model to influence behavioral choice. Both assumptions underlying cognitive models of motivation represent necessary conditions for an expectancy model to predict rational behavior in ongoing situations. The expectancy theory model itself specifies the hypothesized process by which beliefs are translated into behavioral responses (Assumption No. 1). To date, the literature has focused almost entirely on whether or not the specific mental processes which define expectancy models (Force = EY~ IV) are valid. Little in the way of systematic research on expectancy theory has been done to explore the degree to which one's actual environmental conditions influence one's beliefs. In short, the second assumption is merely assumed to be true. The research reported in this article deals with each of the links suggested by the two underlying assumptions of cognitive models of motivation. An additional link implied by these assumptions also was explored. These links are given in Fig. 1 (adopted from Ilgen, Peters, & Campbell, Reference Note 1) and discussed below with respect to research on expectancy theory. Link N o . 1: Environmental Conditions ~ Behavioral R e s p o n s e
The first link represents the effects of actual environmental conditions as specified by the expectancy theory model on behavioral responses. Thus, it telescopes over both assumptions. Five studies have been reported which specifically deal with the effects of such environmental variation on behavioral response. These studies involved the experimental manipulation of either one or two of the three major expectancy theory variables and, thus, represent attempts to assess the causal nature of specific expectancy theory variables on behavior. Graen (1969) and Jorgenson, Dunnette, and Pritchard (1973) manipulated the instrumentality variable; Motowidlo, Loehr, and Dunnette (1972) manipulated the expectancy variable; Arvey (1972) manipulated both the expectancy and instrumentality variables; and Pritchard and Deleo manipulated both the instrumentality and valence variables. Across (1) Actua'l
/
Environment-Behavior Link
# CONDITIONS
Perceptual Link
~
"Force = E~IV
~
RESPONSES
FIG. 1. General cognitive model; assumptions underlying cognitive theories of motivation.
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these five studies, little firm support is offered for the causal effects of the experimental manipulations of the expectancy theory variables on behavioral response. While significant effects were observed, 1 other predicted effects were not observed. For example, Jorgenson et al. (1973) predicted, but did not find, a main effect for the instrumentality variable when perceived effort was their dependent variable. Two other studies manipulated expectancy theory variables in a conceptually imprecise manner. Arvey (1972) and Motowidlo et al. (1972) manipulated the expectancy variable by instructing their subjects that either 20, 50, or 75% of them would succeed on the experimental task. This manipulation corresponded more closely to a task difficulty manipulation than to a manipulation of the expectancy term (see Ilgen and Peters, Note 2, for a discussion of this issue). Finally, significant effects which were contrary to expectancy theory predictions have been observed. Pritchard and De Leo (1973) reported a significant instrumentality x valence interaction using performance as the dependent variable in which the cell means clearly did not conform to the predicted pattern. Using an effort measure as the dependent variable, a significant main effect for valence was observed in which the cell means again were opposite to predictions made by expectancy theory. To summarize these five experimental studies, both supportive and nonsupportive data have been offered for the notion that environmental conditions, as specified by expectancy theory, have behavioral effects. Further, those studies which employed an experimental methodology did not look at all three expectancy theory variables simultaneously. Link No. 2: Environmental Conditions -~ B e l i e f S y s t e m s The second link represents the degree to which one's beliefs are based on actual environmental conditions. Hackman (1969a, b) has addressed this issue with respect to the "redefinition" of tasks in the conduct of research. Hackman and Lawler (1971) provided data which supported such an isomorphic correspondence. Their data indicated a strong convergence on five of six task dimensions among the ratings of subordinates, their supervisor.s, and raters who were members of the research team. The substantial convergent validity is indicative of isomorphic perceptions of the objective task situation. On the other hand, little agreement was found on the amount of performance feedback subordinates received about their own performance. In like manner, Lawler (1967) found that
1 The results of the five experimental studies indicated support for the instrumentality variable as a determinant of both work performance (Graen, 1969; Jorgenson et al., 1973; Pritchard & DeLeo, 1973) and effort (Pritchard & DeLeo, 1973). Support for the expectancy term is reported by Arvey (1972) and Motowidlo et al. (1972).
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large discrepancies existed between organizational members' perceptions of pay policies and the actual policy. Likewise, Georgopolous, Mahoney, and Jones (1957) noted that only one-third of their subjects accurately assessed the organizational pay policies. It would seem that one's perceptions of environmental practices and procedures need not be isomorphic. Embedded in the experimental procedures of the five expectancy theory studies reported above were manipulation checks to assess the effects of the treatment conditions on subjects' perceptions. Data from these studies bear on the second link with respect to expectancy theory cognitions, and these data support the notion that perceptions of environmental conditions mirror those conditions. However, across these five studies, little firm support was offered for the causal effects of the experimental manipulations of expectancy theory variables on behavioral responses. Clearly, if persons respond to their own redefinition of their task situation, then it is important to assess the extent to which such a redefinition mirrors the objective situation. Hackman and Lawler (1971) did exactly this in their study and, overall, found support for a realistic redefinition. The present investigation will assess this issue with respect to expectancy theory variables. Link No. 3: Belief System --~ Behavioral Response The third link represents the translation of one's beliefs into behavioral responses. It is this link which is typically studied with respect to expectancy theory. That is, the interest of the researcher has been to demonstrate that expectancy theory beliefs bear a lawful relationship to behavioral response. Typically, studies done to demonstrate the existance of a systematic relationship between expectancy theory variables and behavioral response have employed a survey methodology and correlational design. The major components of the model are perceptually measured using questionnaires, the scores are combined according to the dictates of an expectancy model, and the resultant composites are then correlated with measures of work behavior. This strategy has resulted in correlations between motivation (Force) as defined by perceptually measured expectancy theory variables and performance or effort which have ranged from 0 to around .40 (cf. Galbraith & Cummings, 1967; Hackman & Porter, 1968). With few exceptions, most current research based on some modifications of the original expectancy theory model has failed to improve upon the strength of the motivation-behavior relationship when motivation is measured by the model and when performance and effort are based upon some criteria other than the participant's own subjective estimate of his behavior level. The consistency of these results over many work settings, subject populations, operationalizations of the major variables, dependent variables, and statistical analyses provide support for
EXPECTANCY THEORY VARIABLES AND EFFORT
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the cognitive processes specified by expectancy theory. However, the consistent low-grade correlations usually obtained in these studies fail to cast aside doubts about the theoretical utility of the model (cf. Behling, Schriesheim, & Tolliver, 1973; Campbell & Pritchard, 1976; and Mitchell, 1974 among others for a critique of the theory and research). In summary, the supportive evidence for the third link must be regarded as minimal, although consistent across many studies. The present research looked at each of these linkages within an experi.mental design. Expectancies and instrumentalities, in addition to valences, were experimentally manipulated in a completely crossed design in which a measure of work effort was the dependent variable. Further, perceptual measures of each of the expectancy theory variables were assessed. J
METHOD
Subjects Eighty-nine female undergraduates participated voluntarily in this study. All subjects were students in an introductory psychology course at a large midwestern university and received course credit for their participation. It was decided to limit participation in the experiment to females since practically all the participants in the pilot research conducted during the previous semester were female. This decision better insured the meaningfulness of the pilot data. Task The task involved spelling recognition. Each separate task contained 25 lines with three words per line. Subjects were instructed to search through the list of words and indicate which words were spelled incorrectly. Performance scores on this task were determined by the percentage of lines, not words, which contained no errors. Performance feedback based on this scoring procedure was provided to subjects. However, all feedback was independent of actual performance. Pilot data indicated that this scoring procedure was effective in providing false feedback which was both realistic and believable. Procedure Subjects were randomly assigned to the experimental sessions. Each session contained from 8 to 15 persons and lasted up to 2 hr depending on how quickly subjects finished the experimental tasks. In addition, all subjects met a second time as one large group for a debriefing session and for the announcement of winners in the record lottery (see Valence manipulation). The general procedure involved having subjects (1) complete a music preference questionnaire, (2) work on six timed trials of the spelling tasks
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LAWRENCE H. PETERS
(to establish an expectancy belief), (3) listen to the instructions designed to induce instrumentality and valence beliefs, and (4) complete a short questionnaire designed to assess the impact of the experimental manipulations. All subjects were then given up to 1 hr to work on a packet containing 10 additional spelling tasks. They were instructed that upon completion of the 10 tasks, they would complete a short questionnaire and then be able to leave the study. The sooner they finished all 10 spelling tasks, the sooner they would be able to leave. This resulted in a measure of total amount of time spent working. It was felt that time spent working was an individually determined decision based, at least in part, on motivation. This measure was then adjusted by a measure of normal working speed to result in the measure of effort used in this study. Each session contained one subject who was a confederate. Her major job was to work on the set of 10 experimental tasks for 20 rain, turn in her work to the experimenter and pick up a postexperimental questionnaire, and then work on this questionnaire for an additional 15 rain. When completed, she turned in her materials and left the room. Thus, she was used to model two types of behavior. First, she demonstrated that it was legitimate to stop working on the task before the hour was up. Second, she demonstrated her willingness to spend a considerable amount of time responding to the postexperimental questionnaire.
Experimental Manipulations Expectancy manipulation. The expectancy term was conceptualized as a perceived correlation between one's effort expenditure and one's performance (see Ilgen & Peters, Note 2; Campbell & Pritchard, 1976). A high effort-performance expectancy is one for which increasing amounts of effort are believed to result in increasing levels of performance. Likewise, a low effort-performance expectancy is one for which increasing amounts of effort are believed to result in either a constant or random level of performance. This conceptualization of the expectancy variable is different from the previous ones (e.g., Vroom, 1964). Earlier conceptualizations regarded the probability that high effort would result in high performance as indicative of a high expectancy belief. Such a conceptualization ignores the subjective probability estimate that little effort expenditure also might result in the attainment of high performance. If this were the case, then there would be no reason for a person to put forth a great deal of effort when little effort expenditure is believed to result in the same consequence. Thus, for example, if a person believed that the probability that high effort would lead to high performance was .9 and that the probability that little effort would lead to high performance also was .9, different predictions of this person's expectancy belief would be made. Earlier
EXPECTANCY THEORY VARIABLES AND EFFORT
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conceptualizations of this construct would indicate that a strong expectancy existed; the present conceptualization would argue for a low expectancy belief (see Ilgen & Peters, Note 2, for a complete discussion of this issue). This research was designed to incorporate the newer conceptualization of the expectancy variable. As such, an expectancy belief was conceptualized and manipulated as a correlational construct. For purposes of manipulating this variable, effort was operationalized as the total amount of time subjects spent working on a task. Thus, a subject is said to put forth more total effort working on a task for 4 min than when working on an equal length task for only 2 min. In order to vary effort, subjects were given six trials with the spelling tasks prior to the experimental trials. Subjects worked on these tasks under time limits of 2, 3, and 4 rain, two trials at each time limit, and were instructed to check back over their work if they finished a task before time was called. In order to vary performance, false feedback was given. Immediately upon completion of a spelling task, "graders" collected the task, ostensibly scored them, and then gave subjects feedback on their performance. The pattern of' false feedback given to subjects allowed for the manipulation of the relationship between a subject's effort and her performance on the spelling tasks. Two levels of the expectancy term were experimentally created. In the "strong" expectancy condition, the pattern of performance feedback was designed to show increasing performance as subjects worked for longer periods of time. Statistically, this feedback presented subjects with effort-performance correlations approaching r = 1.00. Conceptually, it was hoped that subjects would believe they could affect their performance systematically by varying the amount of time spent working on the tasks. In the " w e a k " expectancy condition, the pattern of feedback was designed to present a random pairing of time limits and performance scores. That is, the bivariate distribution was designed to present no systematic linear trends in the feedback data, a pattern which presented subjects with effort-performance correlations approaching r = 0.00. Conceptually, it was hoped that subjects would believe that their performance could not be affected systematically by the amount of time spent working on the spelling tasks. Pilot research indicated the effectiveness of this manipulation using self-report measures of perceived effort-performance correlations as a criterion. Further, it should be noted that the feedback given to subjects in both the strong and weak expectancy conditions differed only with respect to the pattern of feedback scores. The mean feedback scores given to both conditions were equivalent. Thus, the expectancy manipulation was not confounded by a concomitant task difficulty manipulation.
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LAWRENCE H. PETERS
Instrumentality manipulation. Like the expectancy term, an instrumentality was operationalized as a perceived correlation. Here the perceived correlation was between one' s performance and the likelihood of attaining an outcome. In this study, outcomes consisted of stereo record albums. Subjects were informed that approximately half of them would win one stereo album for their participation in this study and that the winners would be determined by a lottery. The manipulation of the instrumentality variable involved controlling the relationship between a subject's performance and her chances of winning a record album. Two levels of this variable were experimentally created, corresponding to performance - o u t c o m e correlations which approached r = 1.00 or approached r = 0.00. Following the first six trials, subjects were instructed that they would work on 10 additional spelling tasks. In the high instrumentality condition, the receipt of lottery tickets was made contingent on work performance on these 10 trials. Higher levels of performance resulted in more lottery tickets; the more lottery tickets earned, the better would be the chances of winning a record album. In the low instrumentality condition, the performance-outcome correlation was set at zero. That is, the receipt of lottery tickets was made noncontigent on performance. Here, increasing one's performance left the odds of winning an album unchanged. Valence manipulation. The final variable manipulated was the valence of the outcome (stereo record albums). Pilot data were collected the previous semester on the desirability of various " t y p e s " of music (e.g., top 40, jazz, country/western, folk music). These data indicated a very strong preference for one type and an equally strong rejection of/indifference toward another type. Based on these data, two levels of the valence term were experimentally created. In the high valence condition, subjects were told that if they were to win in the lottery, they could choose any stereo album from a long list of "preferred" record albums. In the low valence condition, the winners were able to choose from a long list of "nonpreferred" albums. Valence refers conceptually to the satisfaction (+ 1.0), indifference (0.0), or dissatisfaction ( - 1.0) felt from the anticipation of obtaining an outcome. In the present study, however, outcomes were chosen such that the valence manipulation would result in positive affect at one extreme and affect approaching indifference at the other. Since the music types chosen for this study varied greatly in terms of their actual desirability, it was felt that each would elicit divergent affect as anticipated outcomes. If a person was offered a preferred album, the affect generated should be positive. A preferred album should be desirable as a potential outcome because listening to it should result in a satisfying experience and subjects should have been willing to listen to it. On the other hand, subjects offered
EXPECTANCY THEORY VARIABLES AND EFFORT
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a nonpreferred album in the lottery should be indifferent to its attainment. Even though the music was undesirable in itself, subjects had the choice of listening or not listening to it. In this respect, they should not care whether or not they won it because the unpleasant experience of listening to the nonpreferred album did not automatically follow from its attainment. The type of music (preferred or nonpreferred) offered to subjects in the lottery was randomly assigned. The assignment of subjects to either the high or low valence condition, however, was based not only on the type of music offered but also on the desirability of that type of music to the subjects. In order to assess the desirability of the music types, each subject completed a questionnaire indicating her own music preferences. This questionnaire was administered at the beginning of the study, before subjects learned which music type they would be eligible to win. These data indicated that six subjects assigned to the nonpreferred music condition liked this type of music, and two persons assigned to the preferred albums disliked them. The former subjects were assigned to the high valence condition (the outcome was desirable to them) and the latter subjects were assigned to the low valence condition (the outcome was undesirable to them). All analyses reported here involving subjects in the high and low valence conditions were based on this subjective placement of subjects into valence conditions. While this translates this study out of a strict experimental design and into one which involves an organismic variable, it preserves the conceptual meaning of the valence variable.
Perceptual Measures Expectancy measurement. Three items were written to assess perceived effort-performance correlations. Each of the three items required subjects to estimate the relationship between how long they worked on the spelling tasks and their performance levels. The average correlation among these items, corrected by the Spearman-Brown prophesy formula, was r = .89 (p < .001). As a result, the three items were averaged, resulting in one measure of the expectancy term (E). Instrumentality measurement. T w o items were written to assess perceived performance-outcome attainment correlations. These items required subjects to estimate the relationship between how well they performed on the spelling tasks and their chances of winning an album in the record lottery. The corrected correlation between these items was-F= .89 (p < .001). These items also were averaged, resulting in one measure of instrumentality (I). Valence measurement. Valence was measured on an l 1-point scale ranging from e×tremely desirable (11) to extremely undesirable (1). All subjects rated each of the seven music types before being informed of the
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LAWRENCE H. PETERS
one type of music album they could win in the lottery. The valence score assigned each subject was the desirability rating which corresponded with the type of music offered in the lottery.
Force The Force acting on a person to direct her efforts toward performance was based on the above ratings. For each subject, expectancy, instrumentality, and valence ratings were combined multiplicatively. With only one outcome, Force = E x I x V.
Dependent Variable Since expectancy theory is most concerned with predicting effort (Mitchell, 1974), care was taken to provide an objective measure of this construct. To date, the published literature on expectancy theory contains no study with such an objective measure of effort. This measure was defined as the amount of time subjects worked on the set of 10 spelling tasks when allowed to work on them as long as they felt necessary, adjusted by a measure of their normal working speed. In this manner, effort was operationalized as the extra time spent working above and beyond that needed to complete the set of ten experimental tasks. A measure of "normal working speed" was derived from how quickly subjects worked on the first two trials of the spelling tasks (those used in the expectancy manipulation described above). Very few subjects were able to complete all 25 lines of the spelling task within the 2-rain time limit allowed for the first two tasks. Thus, it was possible to determine each subject' s average time per line by dividing the total number of lines completed into the time allowed for the tasks. From these data, a predicted time to complete the entire set of 10 tasks was calculated. The measure of derived effort used in this study, thus, was the difference between the predicted and actual time for completion of the set of 10 experimental tasks.
Manipulation Check An attempt was made to check on the effectiveness of the experimental manipulations by a short questionnaire administered after all inductions had taken place. Subjects responded to three questions, one for each of the experimental manipulations. Subjects indicated whether or not their performance on the spelling tasks improved with more time to work on them (expectancy check), whether or not their odds of winning an album were affected by their performance on the tasks (instrumentality check), and which of seven types of music they were allowed to choose from if a winner in the lottery (valence check). In all cases, the data strongly supported the effectiveness of the manipulations (p < .001 for each compariSOD).
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RESULTS
Link No. 1: Environmental Conditions --~ Behavioral Response This link was tested by analyzing a measure of work effort as the dependent variable in a 2 x 2 × 2 ANOVA. The factors and levels corresponded to (1) high versus low valence, (2) contingent versus noncontingent instrumentality, and (3) strong versus weak expectancy conditions. Only the condition defined by a "high" level on all three of the expectancy theory variables should foster a choice to exert increasingly greater levels of effort expenditure. The other seven conditions contained at least one " l o w " value on the expectancy theory variables. With only one outcome, if either the instrumentality or the valence of the outcome approaches zero, then the valence of performance will approach zero. This, in turn, will result in a low force score, regardless of how strong an expectancy a person might have. Likewise, if a person doesn't believe that his effort is related to his performance (that is, the expectancy term approaches zero), the result will be a low force score regardless of the valence of performance. Within an Analysis of Variance design, these predictions about the pattern of cell means translate into a significant three-way interaction, significant two-way interactions for all pairs of expectancy theory variables, and significant main effects for each variable. Each prediction is attributable to the effort expenditure of subjects in the high expectancy-high instrumentality-high valence condition. These subjects are predicted to exert significantly more effort than subjects in each cf the other seven conditions. Table 1 contains means and standard deviations for the derived effoi-t measure, and 'Fable 2 summarizes the analysis of variance. Figure 2 displays these means graphically. The variance estimates were based on the ~o2 statistic (Hays, 1963). This analysis resulted in significant main effects for the expectancy and valence terms. As predicted, subjects in the high expectancy conditions spent significantly more time on the task than was necessary in comparison to those subjects in the low expectancy conditions (XHighE = 10.33 rain; XLow E = 6.98 rain; p < .05). Likewise, subjects in the high valence condition worked_ significantly longer than subjects in the low valence condition (Xmgh v = 10.12 min; X L o w V = 7.18 rain; p < .05). The main effect for the instrumentality variable and the three-way interaction both approached traditional levels of statistical significance (p < .06 for the instrumentality main effect andp < . 10 for the three-way interaction). The pattern of means for both effects was in the predicted direction. Finally, none of the two-way interactions was significant. Figure 2 shows that the data generally conform to the predictions made about the pattern of cell means for the three-way interaction. The three-
TABLE 1
Instrumentality
Low Low High High Low Low High High
Expectancy
Low Low Low Low High High High High
Treatment Condition
Low High Low High Low High Low High
Valence 13 10 8 17 9 9 13 10
N
3.57 7.22 7.89 9.22 9.26 9.10 8.01 14.93
X
4.10 7.33 4.93 6.35 11.59 5.48 6.53 4.71
SD
Derived Effort
4.69 5.97 4.21 4.51 9.22 9.22 10.I0 10.14
X
E
2.99 2.08 2.49 2.75 2.36 2.24 1.89 1.45
SD
.96 1.35 8.00 6.76 2.00 1.89 8.69 8.80
X
1
2.28 2.29 2.15 2.87 2.61 2.64 3.02 2.28
SD
8.53 77.58 92.71 302.53 62.80 190.37 214.70 952.37
X
V
14.07 160.83 105.86 294.51 147.21 279.82 169.76 352.12
SD
Force = E x l x
MEANS AND STANDARD DEVIATIONS FOR THE DEI~IVED EFFORT, E, 1, AND FORCE = EE 1V MEASURES
H t~
>
*p < .10 **p < .05 ***p < .01
Expectancy (E) Instrumentality (1) Valence (V) E × 1 E x V I x V E x 1 x V Error
Source
df
1 1 1 1 1 1 1 81
236.3 156,0 181.4 4.0 4.2 30.0 116.1 43.5
MS 5.43** 3.58* 4.17"* <1.00 <1.00 <1.00 2.67*
F
Derived effort
.05 .03 .03 .00 .00 .00 .02
o02 490.5 .0 3.4 18.3 3.1 1.2 1.3 5.7
MS 86.88*** < 1.00 <1.00 3.25 <1.00 <1.00 <1.00
F
E
.49 .00 .00 .00 .00 .00 .00
co2
Dependent variables
24.4 893.5 1.0 1.7 .9 2.6 4.5 6.7
MS 3.66 134.00"** <1.00 <1.00 <1.00 <1.00 <1.00
F
1
.00 .60 .00 .00 .00 .00 .00
o02
1,160,037.3 1,968,479.3 1,722,594.8 481,302.3 452,445.5 741,958.0 289,888.3 48,924.2
MS
Force = E
TABLE 2 ANALYSIS OF VARIANCE SUMMARY FOR THE DERIVED EFFORT, E , 1, AND FORCE = E X I V MEASURES
V
23.71"** 40.23*** 35.21"** 9.84*** 9.25*** 15.17"** 5.93**
F
x Ix
.10 .18 .16 .04 .04 .06 .02
coz
q
©
Z
O~ t" o3
0
Z
142
LAWRENCE H. PETERS Low
Expectancy
High
-g
Expectancy
15 .,4
m i0 jJ
v
~5 f
~g ,~ o
I
J I J
I
f
o
5
>
•~ 0 i
Low
i
i
i
High
Low
High
Instrumentality
FIG. 2. Derived effort means, by treatment condition. High valence (
Instrumentality
); low valence (-- -).
way interaction was predicted to occur because of the significantly greater effort exerted by subjects in the cell with " h i g h " levels on all three variables as compared with subjects in all other conditions. Planned comparisons (Hays, 1963) indicated that subjects in the High E - H i g h / - H i g h V cell did spend significantly more time working on the spelling tasks than subjects in the other seven cells combined [t (81) = 2.55; p < .01] and that subjects in the L o w E - L o w / - L o w V condition spent significantly less time working than subjects in the combined remaining cells [t (81) = 2.09; p < .05]. While the predicted interaction only approached significance, it should be noted that the general pattern of these data conform to the predictions. High E - H i g h / - H i g h V subjects worked significantly longer than L o w E-Low/-Low V subjects with cell means for the other conditions between these extremes. Subjects in the High E - H i g h / - H i g h V condition worked from one and one-half to over four times longer than did subjects in the other seven conditions. In total, the effects reported accounted for 13% of the variance in the derived effort measure. Although it was predicted that the means of the seven conditions which contained at least one " l o w " value on the expectancy theory variables should not differ, this was not the case. This effect follows if the " l o w " value on each variable were experimentally set at zero. H o w e v e r , the experimental manipulations in this study were ordinal and only insured that one level was low relative to the other. This is supported by the data in Table 1 and reported below in the link No. 2 analyses. As such, these data are not out of line with what one might expect. Link No. 2: Environmental Conditions --~ Belief System In the present study, the accuracy of the perceptual measures of expectancies and instrumentalities was assessed. The second link was tested by separately analyzing these perceptual measures in the 2 × 2 x 2 A N O V A
EXPECTANCY
THEORY
VARIABLES AND EFFORT
143
described above. Since valence was an organismic variable in this study, it was not analyzed. It was predicted that a significant main effect for the expectancy variable only would result when the expectancy measure (E) was used as the dependent variable in this analysis, and that a significant main effect for the instrumentality variable only would result when the instrumentality measure (I) was used. In addition, Force composites were computed for each person by multiplying responses to measures of the expectancy, instrumentality, and valence variables (i.e., Force = E × I x V for one outcome). Since force is said to underlie behavioral choice, these scores were predicted to be distributed in a manner similar to the effort measure itself. Thus, significant main effects and interactions were again predicted with the effects due to the subjects in the cell marked by a " h i g h " level on all three expectancy theory variables. The basic data for these analyses are presented in Tables 1 and 2. Table 1 contains means and standard deviations for each variable and Table 2 summarizes the analyses of variance. Variance estimates again are based on the o)2 statistic. Expectancy measure. Using the perceptual measure of the e x p e c t a n c y term, the only significant effect found was for the expectancy variable. This is as predicted. Subjects in the high expectancy conditions indicated a greater perceived relationship between their effort and thei_fr performance than did subjects in the low expectancy condition (Xmgh ~ = 9.67; XLow E = 4.84; p < .001). This main effect accounted for 49% of the response 'variance. Instrumentality measure. The only significant effect found was for the instrumentality variable. Subjects in the high instrumentality condition indicated a greater perceived relationship between their performance and their chances of obtaining the outcome than did subjects in the low instrumentality condition (XHigh I = 8.06; XLow i = 1.55; p < .001). This main effect accounted for 60% of the response variance. Force measure. For the measure of Force all main and interaction effects were found to be significant. Subjects in the high expectancy condition, the high instrumentality condition, and the high valence condition each had significantly greater force scores than subjects in the low expectancy, low instrumentality, and low valence conditions, respectively: ( X H i g h E ~--- 355.06;XLow E = 120.34;p_p < .00l) ( X H i g h I : 390.38; XLow I = 84.32; p < .001) (XHigh v = 380.71; X~ow v = 94.69; p < .001). Further, the pattern of means, as expressed by the interactions, corresponded to predictions. Exploration of the three-way interaction indicated that the simple instrumentality × valence interaction was nonsignificant for subjects in the low expectancy condition [F (1,81) = 1.12; n.s.] but was significant for subjects in the high expectancy condition IF (1, 81) -19.06; p < .001]. Further exploration of this simple interaction was done
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by comparing the high and low valence conditions within each level of instrumentality. The results of these analyses indicated no difference between valence conditions at the low instrumentality level IF (1, 81) = 1.50; n.s.]. Within the high instrumentality condition, however, subjects in the high valence conditi__onhad greater force scores than subjects in the low valence condition [Xnigh V = 952.37; Xgow V = 214.70; F(1,81) = 62.87; p < .001]. Simple main effects analyses of all two-way interactions further supported the predictions. For each two-way interaction, subjects in cells defined by a "high" level on both variables had significantly greater Force scores than did subjects in cells in which at least one variable was set at its " l o w " level (p < .001 for all comparisons). In all, the pattern of cell means and supporting statistical analyses clearly support the notion that subjects can accurately perceive their situation as reflected in their Force scores. These significant effects accounted for 60% of the variance in the force scores.
Link No. 3: Belief System --->Behavioral Response The third link was tested in the typical manner. Force composites based on perceptual measures of expectancy theory variables (Force = E x I x V) were correlated with a measure of effort. As predicted, the results of this analysis indicated a significant Force-effort correlation of r = .38 (p < .001). Correlations also were computed between each component of the expectancy model and effort. The correlations with effort were .28 for the expectancy measure, .23 for the instrumentality measure, and .29 for the valence measure (p < .05 for all correlations). Each component, thus, contributed to the variance in effort, but none predicted effort as well as the force composite itself. DISCUSSION In the present investigation, subjects were presented with a behavioral choice to make. In essence, the choice was either to remain working on the experimental tasks or to leave the study. The study itself involved setting up experimental conditions to influence their decision to stay. Within the expectancy theory framework, seven of the cells were designed to result in a low Force toward staying. The eighth condition was designed to provide such a Force and to make staying a reasonable alternative to leaving. The data supported the effects of the varying conditions. Not only were two of the three main effects significant and the third nearly so (p < .06), but the pattern of cell means conformed to the predictions. This latter conclusion is supported by the results of the simple effects analyses. In all, it would seem that subjects in a low expectancy condition were less willing to exert effort than those subjects in a high
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expectancy condition, who, in turn, were willing to do so only when a high instrumentality-high valence situation also was present. Analyses a;lso were conducted using actual performance (the percentage of lines correct on the I0 experimental spelling tasks) as the dependent variable. Whereas performance was only marginally correlated with effort (r = . 16, p < .06) and uncorrelated with motivational Force (r = .04, n.s.), it was highly correlated (r = .84, p < .001) with a crude measure of skill level (the', actual percentage of lines correct on the first two pretrial spelling tasks). In effect, good spellers performed consistently better than poor spellers. Of importance theoretically, the Force-effort correlation (r = .38, p < .001) is significantly greater than the Force-performance correlation (r = .04, n.s.) (Z = 2.65, p < .01, Glass & Stanley, 1970). Thus, motivational determinants are more closely linked to effort than to performance. These data underscore the importance of using actual effort expenditure as the appropriate dependent variable in expectancy theory research rather than a measure of performance, a conclusion shared with Campbell and Pritchard (1976) and Mitchell (1974). Since expectancy theory, in all its formulations, is a within-persons theory (Mitchell, 1974), it is somewhat surprising that the present study, which used a between-persons design, found some support for its predictions. In the present instance, this may very well have occurred due to the type of decision which was required of the participants. Subjects had to choose between staying and leaving the experimental situation. It is reasonable to assume that all subjects in this study had a strong Force toward leaving the situation; their presence in some sort of research project was a mandatory course requirement. If this were the case, then each of the experimental conditions provided a counteractive Force toward staying, and the decision to stay and work or to leave would reduce to a decision based on counteractive forces where the Force to leave was reasonably strong across all subjects. At the very least, the Force to leave should have been constant across conditions since subjects were randomly assigned to those conditions. Such an assumption, although weaker, still explains the data. The data from this investigation also supported the intervening links between environmental conditions and the occurrence of behavior. Clearly, perceptions of the effort-performance and performance-outcome relationships were accurately reflected. Force scores also were distributed in a manner consistent with expectancy theory. These data not only confirmed the predictions, but did so strongly. Vroom (1964) pointed to the importance of the perceptual link and indicated that it was a necessary condition for the model to predict behavior. In fact, it is a necessary
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assumption of the model at a conceptual level. This assumption, however, has not been systematically studied for its implications to cognitive models. If behavior is a partial function of one's beliefs about their environment, then accurately perceiving that environment is a necessary precondition for adaptive behavior. While the present investigation indicates that perceptions of environmental conditions can be accurate, it does not address an equally important problem of the causes and consequences of inaccurate perceptions. Interventions based on an expectancy theory model designed to increase the valence of performance or to enhance expectancy beliefs or both may fail to show substantial behavioral effects not so much due to a weak intervention or a weakness in the theory per se but because of perceptual distortion which fails to translate these changes accurately into one's belief system. The process by which one's beliefs influence behavioral choice is the province of expectancy theory and is represented by Link No. 3 in the General Cognitive Model. Most of the research on expectancy theory has been aimed at demonstrating the existance of this link. To date, the literature is consistently supportive and the data from the present study concur. Unlike the typical survey design, however, the present study provided a backdrop against which to judge the Force-effort correlation. Survey design studies must assume that environmental conditions foster the choice of effort expenditure according to expectancy theory predictions. That is, such studies must assume that the dependent variable is, indeed, predictable based on expectancy theory in that particular situation. Since the present data indicated that effort was predictable from the environmental conditions (Link No. 1 analyses), it should follow that perceptual composites based on these environmental conditions likewise should provide this support. This was the case. However, the strength of the Force-behavior relationship in the present study, as in previous research, was not strong (r = .38). While the magnitude of this relationship is far from strong, it should be noted that the variance accounted for by this analysis is comparable to that accounted for by the overall ANOVA. Seemingly, there was little more variance in effort which was predictable by expectancy theory variables in the present situation. More important, however, the variance in effort should be more attributable to the variance in beliefs than to the variance in the environmental conditions. This is, in essence, a defining characteristic of a cognitive model. To test this implication, a covariance analysis was done. If expectancy theory beliefs (Force scores) are immediate determinants of behavioral choice, and if these beliefs were to be partialled out of the effort data, then the resultant data should fail to show the pattern predicted in the overall Analysis of Variance. Such an analysis was performed by partialling Force out of the derived effort score. The residual
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d a t a , a m e a s u r e o f effort n o t p r e d i c t a b l e f r o m k n o w l e d g e o f e x p e c t a n c y t h e o r y b e l i e f s , w a s a n a l y z e d in t h e 2 x 2 x 2 A N O V A . F o r t h e s e d a t a , t h e F r a t i o f o r t h e o v e r a l l t r e a t m e n t e f f e c t w a s n o n s i g n i f i c a n t ( F < 1.00, n.s.). T h e s e d a t a i n d i c a t e t h a t b e h a v i o r s e e m s m o r e c l o s e l y r e l a t e d to o n e ' s b e l i e f s t h a n to t h e e n v i r o n m e n t in w h i c h t h e s e b e l i e f s a r e a n c h o r e d . I n c o n c l u s i o n , t h e d a t a f r o m t h e p r e s e n t s t u d y s u p p o r t e a c h link in t h e General Cognitive Model. These data indicate that expectancy theory v a r i a b l e s a r e c a u s a l l y r e l a t e d to effort e x p e n d i t u r e t h r o u g h t h e p e r c e p t i o n s w h i c h p e r s o n s h o l d . A s t h e t h e o r y p r e d i c t s , s u b j e c t s in this s t u d y w e r e o n l y willing to w o r k l o n g e r t h a n n e e d e d to c o m p l e t e a t a s k u n d e r a s p e c i f i c c o n d i t i o n . T h i s c o n d i t i o n w a s d e f i n e d e x p e r i m e n t a l l y as o n e in w h i c h i n c r e a s e s in effort w e r e a s s o c i a t e d w i t h i n c r e a s e s in p e r f o r m a n c e , a n d i n c r e a s e d p e r f o r m a n c e r e s u l t e d in t h e g r e a t e r l i k e l i h o o d o f a t t a i n i n g a valued outceme.
REFERENCES Arvey, R. Task performance as a function of perceived effort-performance and performance -reward contingencies. Organizational Behavior and Human Performance, 1972, 8, 423 -433. Behling, O., Schreisheim, C., & Tolliver, J. Present trends and directions in theories of work effort. Journal Supplement Abstract Service, Ms. No. 67, American Psychological Association, 1973. Campbell, J., & Pritchard, R. Human motivation. In Dunnette, M. (Ed.), Handbook of industrial and organizational psychology. Chicago: Rand McNally, 1976. Gailbraith, J., & Cummings, L. An empirical investigation of the motivational determinants of task performance: Interactive effects between instrumentality-valence and motivation--ability. Organizational Behavior and Human Performance, 1967, 2, 237-257. Georgopolous, B., Mahoney, G., & Jones, N. A path-goal approach to productivity. Journal of Applied Psychology, 1957, 41, 345-353. Glass, G., & Stanley, S. Statistical methods in education and psychology. Englewood Cliffs, N. J.: Prentice-Hall, 1970. Graen, G. Instrumentality theory of work motivation: Some experimental results and suggested modifications. Journal of Applied Psychology Monograph, 1969, 53 (2, P. 2). Hackman, J. R. Nature of the task as a determiner of job behavior. Personnel Psychology, 1969, 22, 435-444. (a) Hackman, J. R. Toward understanding the role of tasks in behavioral research. Acta Psychologica, 1969, 31, 97-128. (b). Hackman, J. R., & Porter, L. Expectancy theory predictions of work effectiveness. Organizational Behavior and Human Performance, 1968, 3,417-426. Hackman, J. R., & Lawler, E. Employee reactions to job characteristics. Journal of Applied Psychology Monograph, 1971, 55, 259-286. Hays, W. L. Statistics for psychologists. New York: Holt, Rinehart, and Winston, 1963. Jorgenson, D. O., Dunnette, M. D., & Pritchard, R. D. Effects of a performance-reward contingency on behavior in a simulated work setting. Journal of Applied Psychology, 1973, 57, 271-280. LaMer, E. Secrecy about management compensation: Are there hidden costs? Organizational Behavior and Human Performance, 1967, 2, 182-189.
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Mitchell, T. R. Expectancy models of job satisfaction, occupational preference and effort: A theoretical, methodological, and empirical appraisal. Psychological Bulletin, 1974, 81, 1053-1077. Motowidlo, S. J., Loehr, V., & Dunnette, M. D. The effect of goal specificity on the relationship between expectancy and task performance (Tech. Rep. No. 4008). Minneapolis, University of Minnesota, 1972. Pritchard, R., & DeLeo, P. An experimental test of the interactive relationship between valence of job outcomes and performance-outcome instrumentality. Journal of Applied Psychology, 1973, 57, 264-270. Vroom, V. Work and motivation. New York: Wiley, 1964.
REFERENCE NOTES 1. Ilgen, D. R., Peters, L. H., & Campbell, D. J. A systematic study of the sources and effects of work expectations (Tech. Rep. No. 3). Conducted for the Army Research Institute for the Behavioral Sciences, U.S. Army, under Grant No. DAHC 19-74-G0002, April 1976. 2. Ilgen, D, R., & Peters, L. H. Boundary conditions and operationalizations of expectancy theory variables. Unpublished manuscript, Purdue University, West Lafayette, Ind., 1975. RECEIVED: October 7, 1976
20, 129-148 (1977)
Cognitive Models of Motivation, Expectancy Theory and Effort: An Analysis and Empirical Test LAWRENCE H. PETERS University of Texas at Dallas
Using a General Cognitive Model which specifies that environmental conditions affect behavior only through the perceptions that persons have of their environment, three linkages were defined and tested with respect to expectancy theory predictions of effort expenditure. These linkages specified that environmental conditions in accord with expectancy theory predictions should affect effort expenditure; that these same conditions should affect persons' perceptions of the situation; and that the perceptions, in turn, should be predictive of el'fort expenditure. In order to test these predictions, expectancies and instrumentalities, in addition to valences, were experimentally manipulated in a completely crossed design in which a measure of effort was the dependent variable. Further, perceptual measures of each of the expectancy theory variables were assessed. The results of this investigation supported each of the links in the General Cognitive Model and are interpreted within that model.
The assumption that an individual is influenced by his beliefs about the situation in which he finds himself and by his beliefs about the consequences of his actions is not new to students of organizational behavior. Expectancy theory of motivation (Vroom, 1964) represents such a cognitive approach to understanding human behavior in organizations. Underlying cognitive models of motivation are two assumptions, only one of which is systematically treated in the research literature on expectancy theory. The first assumption is that behavior is rooted in the belief system of a person. With respect to expectancy theory, this involves beliefs about being able to attain various behavioral alternatives (expectancy beliefs) and beliefs about the anticipated consequences upon attaining these behavioral alternatives (instrumentality beliefs). The second assumption is that these beliefs are based, at least in part, on the actual environment in which one finds himself. This is to say no more than one's work-related beliefs are influenced by relevant characteristics of his work situation. It is assumed that for beliefs to effectively guide behavior in a particular
I would like to acknowledge Daniel R. Ilgen, David L. Ford, and Rabi S. Bhagat for their critical review and suggestions on earlier drafts of this manuscript. This research was supported by the Army Research Institute for the Behavioral Sciences under Grant No. DAHC 19-74-G-002, Daniel R. Ilgen, Principal Investigator. Requests for reprints can be addressed to the School of Management and Administration, The University of Texas at Dallas, Richardson, Texas., 75080. 129 Copyright © 1977 by Academic Press, Inc. All rights of reproduction in any form reserved.
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work situation, they must bear some substantial relationship to reality (Vroom, 1964). Taken together, environmental circumstances are incorporated into one's belief system and these beliefs are then processed according to a cognitive model to influence behavioral choice. Both assumptions underlying cognitive models of motivation represent necessary conditions for an expectancy model to predict rational behavior in ongoing situations. The expectancy theory model itself specifies the hypothesized process by which beliefs are translated into behavioral responses (Assumption No. 1). To date, the literature has focused almost entirely on whether or not the specific mental processes which define expectancy models (Force = EY~ IV) are valid. Little in the way of systematic research on expectancy theory has been done to explore the degree to which one's actual environmental conditions influence one's beliefs. In short, the second assumption is merely assumed to be true. The research reported in this article deals with each of the links suggested by the two underlying assumptions of cognitive models of motivation. An additional link implied by these assumptions also was explored. These links are given in Fig. 1 (adopted from Ilgen, Peters, & Campbell, Reference Note 1) and discussed below with respect to research on expectancy theory. Link N o . 1: Environmental Conditions ~ Behavioral R e s p o n s e
The first link represents the effects of actual environmental conditions as specified by the expectancy theory model on behavioral responses. Thus, it telescopes over both assumptions. Five studies have been reported which specifically deal with the effects of such environmental variation on behavioral response. These studies involved the experimental manipulation of either one or two of the three major expectancy theory variables and, thus, represent attempts to assess the causal nature of specific expectancy theory variables on behavior. Graen (1969) and Jorgenson, Dunnette, and Pritchard (1973) manipulated the instrumentality variable; Motowidlo, Loehr, and Dunnette (1972) manipulated the expectancy variable; Arvey (1972) manipulated both the expectancy and instrumentality variables; and Pritchard and Deleo manipulated both the instrumentality and valence variables. Across (1) Actua'l
/
Environment-Behavior Link
# CONDITIONS
Perceptual Link
~
"Force = E~IV
~
RESPONSES
FIG. 1. General cognitive model; assumptions underlying cognitive theories of motivation.
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these five studies, little firm support is offered for the causal effects of the experimental manipulations of the expectancy theory variables on behavioral response. While significant effects were observed, 1 other predicted effects were not observed. For example, Jorgenson et al. (1973) predicted, but did not find, a main effect for the instrumentality variable when perceived effort was their dependent variable. Two other studies manipulated expectancy theory variables in a conceptually imprecise manner. Arvey (1972) and Motowidlo et al. (1972) manipulated the expectancy variable by instructing their subjects that either 20, 50, or 75% of them would succeed on the experimental task. This manipulation corresponded more closely to a task difficulty manipulation than to a manipulation of the expectancy term (see Ilgen and Peters, Note 2, for a discussion of this issue). Finally, significant effects which were contrary to expectancy theory predictions have been observed. Pritchard and De Leo (1973) reported a significant instrumentality x valence interaction using performance as the dependent variable in which the cell means clearly did not conform to the predicted pattern. Using an effort measure as the dependent variable, a significant main effect for valence was observed in which the cell means again were opposite to predictions made by expectancy theory. To summarize these five experimental studies, both supportive and nonsupportive data have been offered for the notion that environmental conditions, as specified by expectancy theory, have behavioral effects. Further, those studies which employed an experimental methodology did not look at all three expectancy theory variables simultaneously. Link No. 2: Environmental Conditions -~ B e l i e f S y s t e m s The second link represents the degree to which one's beliefs are based on actual environmental conditions. Hackman (1969a, b) has addressed this issue with respect to the "redefinition" of tasks in the conduct of research. Hackman and Lawler (1971) provided data which supported such an isomorphic correspondence. Their data indicated a strong convergence on five of six task dimensions among the ratings of subordinates, their supervisor.s, and raters who were members of the research team. The substantial convergent validity is indicative of isomorphic perceptions of the objective task situation. On the other hand, little agreement was found on the amount of performance feedback subordinates received about their own performance. In like manner, Lawler (1967) found that
1 The results of the five experimental studies indicated support for the instrumentality variable as a determinant of both work performance (Graen, 1969; Jorgenson et al., 1973; Pritchard & DeLeo, 1973) and effort (Pritchard & DeLeo, 1973). Support for the expectancy term is reported by Arvey (1972) and Motowidlo et al. (1972).
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large discrepancies existed between organizational members' perceptions of pay policies and the actual policy. Likewise, Georgopolous, Mahoney, and Jones (1957) noted that only one-third of their subjects accurately assessed the organizational pay policies. It would seem that one's perceptions of environmental practices and procedures need not be isomorphic. Embedded in the experimental procedures of the five expectancy theory studies reported above were manipulation checks to assess the effects of the treatment conditions on subjects' perceptions. Data from these studies bear on the second link with respect to expectancy theory cognitions, and these data support the notion that perceptions of environmental conditions mirror those conditions. However, across these five studies, little firm support was offered for the causal effects of the experimental manipulations of expectancy theory variables on behavioral responses. Clearly, if persons respond to their own redefinition of their task situation, then it is important to assess the extent to which such a redefinition mirrors the objective situation. Hackman and Lawler (1971) did exactly this in their study and, overall, found support for a realistic redefinition. The present investigation will assess this issue with respect to expectancy theory variables. Link No. 3: Belief System --~ Behavioral Response The third link represents the translation of one's beliefs into behavioral responses. It is this link which is typically studied with respect to expectancy theory. That is, the interest of the researcher has been to demonstrate that expectancy theory beliefs bear a lawful relationship to behavioral response. Typically, studies done to demonstrate the existance of a systematic relationship between expectancy theory variables and behavioral response have employed a survey methodology and correlational design. The major components of the model are perceptually measured using questionnaires, the scores are combined according to the dictates of an expectancy model, and the resultant composites are then correlated with measures of work behavior. This strategy has resulted in correlations between motivation (Force) as defined by perceptually measured expectancy theory variables and performance or effort which have ranged from 0 to around .40 (cf. Galbraith & Cummings, 1967; Hackman & Porter, 1968). With few exceptions, most current research based on some modifications of the original expectancy theory model has failed to improve upon the strength of the motivation-behavior relationship when motivation is measured by the model and when performance and effort are based upon some criteria other than the participant's own subjective estimate of his behavior level. The consistency of these results over many work settings, subject populations, operationalizations of the major variables, dependent variables, and statistical analyses provide support for
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the cognitive processes specified by expectancy theory. However, the consistent low-grade correlations usually obtained in these studies fail to cast aside doubts about the theoretical utility of the model (cf. Behling, Schriesheim, & Tolliver, 1973; Campbell & Pritchard, 1976; and Mitchell, 1974 among others for a critique of the theory and research). In summary, the supportive evidence for the third link must be regarded as minimal, although consistent across many studies. The present research looked at each of these linkages within an experi.mental design. Expectancies and instrumentalities, in addition to valences, were experimentally manipulated in a completely crossed design in which a measure of work effort was the dependent variable. Further, perceptual measures of each of the expectancy theory variables were assessed. J
METHOD
Subjects Eighty-nine female undergraduates participated voluntarily in this study. All subjects were students in an introductory psychology course at a large midwestern university and received course credit for their participation. It was decided to limit participation in the experiment to females since practically all the participants in the pilot research conducted during the previous semester were female. This decision better insured the meaningfulness of the pilot data. Task The task involved spelling recognition. Each separate task contained 25 lines with three words per line. Subjects were instructed to search through the list of words and indicate which words were spelled incorrectly. Performance scores on this task were determined by the percentage of lines, not words, which contained no errors. Performance feedback based on this scoring procedure was provided to subjects. However, all feedback was independent of actual performance. Pilot data indicated that this scoring procedure was effective in providing false feedback which was both realistic and believable. Procedure Subjects were randomly assigned to the experimental sessions. Each session contained from 8 to 15 persons and lasted up to 2 hr depending on how quickly subjects finished the experimental tasks. In addition, all subjects met a second time as one large group for a debriefing session and for the announcement of winners in the record lottery (see Valence manipulation). The general procedure involved having subjects (1) complete a music preference questionnaire, (2) work on six timed trials of the spelling tasks
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(to establish an expectancy belief), (3) listen to the instructions designed to induce instrumentality and valence beliefs, and (4) complete a short questionnaire designed to assess the impact of the experimental manipulations. All subjects were then given up to 1 hr to work on a packet containing 10 additional spelling tasks. They were instructed that upon completion of the 10 tasks, they would complete a short questionnaire and then be able to leave the study. The sooner they finished all 10 spelling tasks, the sooner they would be able to leave. This resulted in a measure of total amount of time spent working. It was felt that time spent working was an individually determined decision based, at least in part, on motivation. This measure was then adjusted by a measure of normal working speed to result in the measure of effort used in this study. Each session contained one subject who was a confederate. Her major job was to work on the set of 10 experimental tasks for 20 rain, turn in her work to the experimenter and pick up a postexperimental questionnaire, and then work on this questionnaire for an additional 15 rain. When completed, she turned in her materials and left the room. Thus, she was used to model two types of behavior. First, she demonstrated that it was legitimate to stop working on the task before the hour was up. Second, she demonstrated her willingness to spend a considerable amount of time responding to the postexperimental questionnaire.
Experimental Manipulations Expectancy manipulation. The expectancy term was conceptualized as a perceived correlation between one's effort expenditure and one's performance (see Ilgen & Peters, Note 2; Campbell & Pritchard, 1976). A high effort-performance expectancy is one for which increasing amounts of effort are believed to result in increasing levels of performance. Likewise, a low effort-performance expectancy is one for which increasing amounts of effort are believed to result in either a constant or random level of performance. This conceptualization of the expectancy variable is different from the previous ones (e.g., Vroom, 1964). Earlier conceptualizations regarded the probability that high effort would result in high performance as indicative of a high expectancy belief. Such a conceptualization ignores the subjective probability estimate that little effort expenditure also might result in the attainment of high performance. If this were the case, then there would be no reason for a person to put forth a great deal of effort when little effort expenditure is believed to result in the same consequence. Thus, for example, if a person believed that the probability that high effort would lead to high performance was .9 and that the probability that little effort would lead to high performance also was .9, different predictions of this person's expectancy belief would be made. Earlier
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conceptualizations of this construct would indicate that a strong expectancy existed; the present conceptualization would argue for a low expectancy belief (see Ilgen & Peters, Note 2, for a complete discussion of this issue). This research was designed to incorporate the newer conceptualization of the expectancy variable. As such, an expectancy belief was conceptualized and manipulated as a correlational construct. For purposes of manipulating this variable, effort was operationalized as the total amount of time subjects spent working on a task. Thus, a subject is said to put forth more total effort working on a task for 4 min than when working on an equal length task for only 2 min. In order to vary effort, subjects were given six trials with the spelling tasks prior to the experimental trials. Subjects worked on these tasks under time limits of 2, 3, and 4 rain, two trials at each time limit, and were instructed to check back over their work if they finished a task before time was called. In order to vary performance, false feedback was given. Immediately upon completion of a spelling task, "graders" collected the task, ostensibly scored them, and then gave subjects feedback on their performance. The pattern of' false feedback given to subjects allowed for the manipulation of the relationship between a subject's effort and her performance on the spelling tasks. Two levels of the expectancy term were experimentally created. In the "strong" expectancy condition, the pattern of performance feedback was designed to show increasing performance as subjects worked for longer periods of time. Statistically, this feedback presented subjects with effort-performance correlations approaching r = 1.00. Conceptually, it was hoped that subjects would believe they could affect their performance systematically by varying the amount of time spent working on the tasks. In the " w e a k " expectancy condition, the pattern of feedback was designed to present a random pairing of time limits and performance scores. That is, the bivariate distribution was designed to present no systematic linear trends in the feedback data, a pattern which presented subjects with effort-performance correlations approaching r = 0.00. Conceptually, it was hoped that subjects would believe that their performance could not be affected systematically by the amount of time spent working on the spelling tasks. Pilot research indicated the effectiveness of this manipulation using self-report measures of perceived effort-performance correlations as a criterion. Further, it should be noted that the feedback given to subjects in both the strong and weak expectancy conditions differed only with respect to the pattern of feedback scores. The mean feedback scores given to both conditions were equivalent. Thus, the expectancy manipulation was not confounded by a concomitant task difficulty manipulation.
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Instrumentality manipulation. Like the expectancy term, an instrumentality was operationalized as a perceived correlation. Here the perceived correlation was between one' s performance and the likelihood of attaining an outcome. In this study, outcomes consisted of stereo record albums. Subjects were informed that approximately half of them would win one stereo album for their participation in this study and that the winners would be determined by a lottery. The manipulation of the instrumentality variable involved controlling the relationship between a subject's performance and her chances of winning a record album. Two levels of this variable were experimentally created, corresponding to performance - o u t c o m e correlations which approached r = 1.00 or approached r = 0.00. Following the first six trials, subjects were instructed that they would work on 10 additional spelling tasks. In the high instrumentality condition, the receipt of lottery tickets was made contingent on work performance on these 10 trials. Higher levels of performance resulted in more lottery tickets; the more lottery tickets earned, the better would be the chances of winning a record album. In the low instrumentality condition, the performance-outcome correlation was set at zero. That is, the receipt of lottery tickets was made noncontigent on performance. Here, increasing one's performance left the odds of winning an album unchanged. Valence manipulation. The final variable manipulated was the valence of the outcome (stereo record albums). Pilot data were collected the previous semester on the desirability of various " t y p e s " of music (e.g., top 40, jazz, country/western, folk music). These data indicated a very strong preference for one type and an equally strong rejection of/indifference toward another type. Based on these data, two levels of the valence term were experimentally created. In the high valence condition, subjects were told that if they were to win in the lottery, they could choose any stereo album from a long list of "preferred" record albums. In the low valence condition, the winners were able to choose from a long list of "nonpreferred" albums. Valence refers conceptually to the satisfaction (+ 1.0), indifference (0.0), or dissatisfaction ( - 1.0) felt from the anticipation of obtaining an outcome. In the present study, however, outcomes were chosen such that the valence manipulation would result in positive affect at one extreme and affect approaching indifference at the other. Since the music types chosen for this study varied greatly in terms of their actual desirability, it was felt that each would elicit divergent affect as anticipated outcomes. If a person was offered a preferred album, the affect generated should be positive. A preferred album should be desirable as a potential outcome because listening to it should result in a satisfying experience and subjects should have been willing to listen to it. On the other hand, subjects offered
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a nonpreferred album in the lottery should be indifferent to its attainment. Even though the music was undesirable in itself, subjects had the choice of listening or not listening to it. In this respect, they should not care whether or not they won it because the unpleasant experience of listening to the nonpreferred album did not automatically follow from its attainment. The type of music (preferred or nonpreferred) offered to subjects in the lottery was randomly assigned. The assignment of subjects to either the high or low valence condition, however, was based not only on the type of music offered but also on the desirability of that type of music to the subjects. In order to assess the desirability of the music types, each subject completed a questionnaire indicating her own music preferences. This questionnaire was administered at the beginning of the study, before subjects learned which music type they would be eligible to win. These data indicated that six subjects assigned to the nonpreferred music condition liked this type of music, and two persons assigned to the preferred albums disliked them. The former subjects were assigned to the high valence condition (the outcome was desirable to them) and the latter subjects were assigned to the low valence condition (the outcome was undesirable to them). All analyses reported here involving subjects in the high and low valence conditions were based on this subjective placement of subjects into valence conditions. While this translates this study out of a strict experimental design and into one which involves an organismic variable, it preserves the conceptual meaning of the valence variable.
Perceptual Measures Expectancy measurement. Three items were written to assess perceived effort-performance correlations. Each of the three items required subjects to estimate the relationship between how long they worked on the spelling tasks and their performance levels. The average correlation among these items, corrected by the Spearman-Brown prophesy formula, was r = .89 (p < .001). As a result, the three items were averaged, resulting in one measure of the expectancy term (E). Instrumentality measurement. T w o items were written to assess perceived performance-outcome attainment correlations. These items required subjects to estimate the relationship between how well they performed on the spelling tasks and their chances of winning an album in the record lottery. The corrected correlation between these items was-F= .89 (p < .001). These items also were averaged, resulting in one measure of instrumentality (I). Valence measurement. Valence was measured on an l 1-point scale ranging from e×tremely desirable (11) to extremely undesirable (1). All subjects rated each of the seven music types before being informed of the
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one type of music album they could win in the lottery. The valence score assigned each subject was the desirability rating which corresponded with the type of music offered in the lottery.
Force The Force acting on a person to direct her efforts toward performance was based on the above ratings. For each subject, expectancy, instrumentality, and valence ratings were combined multiplicatively. With only one outcome, Force = E x I x V.
Dependent Variable Since expectancy theory is most concerned with predicting effort (Mitchell, 1974), care was taken to provide an objective measure of this construct. To date, the published literature on expectancy theory contains no study with such an objective measure of effort. This measure was defined as the amount of time subjects worked on the set of 10 spelling tasks when allowed to work on them as long as they felt necessary, adjusted by a measure of their normal working speed. In this manner, effort was operationalized as the extra time spent working above and beyond that needed to complete the set of ten experimental tasks. A measure of "normal working speed" was derived from how quickly subjects worked on the first two trials of the spelling tasks (those used in the expectancy manipulation described above). Very few subjects were able to complete all 25 lines of the spelling task within the 2-rain time limit allowed for the first two tasks. Thus, it was possible to determine each subject' s average time per line by dividing the total number of lines completed into the time allowed for the tasks. From these data, a predicted time to complete the entire set of 10 tasks was calculated. The measure of derived effort used in this study, thus, was the difference between the predicted and actual time for completion of the set of 10 experimental tasks.
Manipulation Check An attempt was made to check on the effectiveness of the experimental manipulations by a short questionnaire administered after all inductions had taken place. Subjects responded to three questions, one for each of the experimental manipulations. Subjects indicated whether or not their performance on the spelling tasks improved with more time to work on them (expectancy check), whether or not their odds of winning an album were affected by their performance on the tasks (instrumentality check), and which of seven types of music they were allowed to choose from if a winner in the lottery (valence check). In all cases, the data strongly supported the effectiveness of the manipulations (p < .001 for each compariSOD).
EXPECTANCY THEORY VARIABLES AND EFFORT
139
RESULTS
Link No. 1: Environmental Conditions --~ Behavioral Response This link was tested by analyzing a measure of work effort as the dependent variable in a 2 x 2 × 2 ANOVA. The factors and levels corresponded to (1) high versus low valence, (2) contingent versus noncontingent instrumentality, and (3) strong versus weak expectancy conditions. Only the condition defined by a "high" level on all three of the expectancy theory variables should foster a choice to exert increasingly greater levels of effort expenditure. The other seven conditions contained at least one " l o w " value on the expectancy theory variables. With only one outcome, if either the instrumentality or the valence of the outcome approaches zero, then the valence of performance will approach zero. This, in turn, will result in a low force score, regardless of how strong an expectancy a person might have. Likewise, if a person doesn't believe that his effort is related to his performance (that is, the expectancy term approaches zero), the result will be a low force score regardless of the valence of performance. Within an Analysis of Variance design, these predictions about the pattern of cell means translate into a significant three-way interaction, significant two-way interactions for all pairs of expectancy theory variables, and significant main effects for each variable. Each prediction is attributable to the effort expenditure of subjects in the high expectancy-high instrumentality-high valence condition. These subjects are predicted to exert significantly more effort than subjects in each cf the other seven conditions. Table 1 contains means and standard deviations for the derived effoi-t measure, and 'Fable 2 summarizes the analysis of variance. Figure 2 displays these means graphically. The variance estimates were based on the ~o2 statistic (Hays, 1963). This analysis resulted in significant main effects for the expectancy and valence terms. As predicted, subjects in the high expectancy conditions spent significantly more time on the task than was necessary in comparison to those subjects in the low expectancy conditions (XHighE = 10.33 rain; XLow E = 6.98 rain; p < .05). Likewise, subjects in the high valence condition worked_ significantly longer than subjects in the low valence condition (Xmgh v = 10.12 min; X L o w V = 7.18 rain; p < .05). The main effect for the instrumentality variable and the three-way interaction both approached traditional levels of statistical significance (p < .06 for the instrumentality main effect andp < . 10 for the three-way interaction). The pattern of means for both effects was in the predicted direction. Finally, none of the two-way interactions was significant. Figure 2 shows that the data generally conform to the predictions made about the pattern of cell means for the three-way interaction. The three-
TABLE 1
Instrumentality
Low Low High High Low Low High High
Expectancy
Low Low Low Low High High High High
Treatment Condition
Low High Low High Low High Low High
Valence 13 10 8 17 9 9 13 10
N
3.57 7.22 7.89 9.22 9.26 9.10 8.01 14.93
X
4.10 7.33 4.93 6.35 11.59 5.48 6.53 4.71
SD
Derived Effort
4.69 5.97 4.21 4.51 9.22 9.22 10.I0 10.14
X
E
2.99 2.08 2.49 2.75 2.36 2.24 1.89 1.45
SD
.96 1.35 8.00 6.76 2.00 1.89 8.69 8.80
X
1
2.28 2.29 2.15 2.87 2.61 2.64 3.02 2.28
SD
8.53 77.58 92.71 302.53 62.80 190.37 214.70 952.37
X
V
14.07 160.83 105.86 294.51 147.21 279.82 169.76 352.12
SD
Force = E x l x
MEANS AND STANDARD DEVIATIONS FOR THE DEI~IVED EFFORT, E, 1, AND FORCE = EE 1V MEASURES
H t~
>
*p < .10 **p < .05 ***p < .01
Expectancy (E) Instrumentality (1) Valence (V) E × 1 E x V I x V E x 1 x V Error
Source
df
1 1 1 1 1 1 1 81
236.3 156,0 181.4 4.0 4.2 30.0 116.1 43.5
MS 5.43** 3.58* 4.17"* <1.00 <1.00 <1.00 2.67*
F
Derived effort
.05 .03 .03 .00 .00 .00 .02
o02 490.5 .0 3.4 18.3 3.1 1.2 1.3 5.7
MS 86.88*** < 1.00 <1.00 3.25 <1.00 <1.00 <1.00
F
E
.49 .00 .00 .00 .00 .00 .00
co2
Dependent variables
24.4 893.5 1.0 1.7 .9 2.6 4.5 6.7
MS 3.66 134.00"** <1.00 <1.00 <1.00 <1.00 <1.00
F
1
.00 .60 .00 .00 .00 .00 .00
o02
1,160,037.3 1,968,479.3 1,722,594.8 481,302.3 452,445.5 741,958.0 289,888.3 48,924.2
MS
Force = E
TABLE 2 ANALYSIS OF VARIANCE SUMMARY FOR THE DERIVED EFFORT, E , 1, AND FORCE = E X I V MEASURES
V
23.71"** 40.23*** 35.21"** 9.84*** 9.25*** 15.17"** 5.93**
F
x Ix
.10 .18 .16 .04 .04 .06 .02
coz
q
©
Z
O~ t" o3
0
Z
142
LAWRENCE H. PETERS Low
Expectancy
High
-g
Expectancy
15 .,4
m i0 jJ
v
~5 f
~g ,~ o
I
J I J
I
f
o
5
>
•~ 0 i
Low
i
i
i
High
Low
High
Instrumentality
FIG. 2. Derived effort means, by treatment condition. High valence (
Instrumentality
); low valence (-- -).
way interaction was predicted to occur because of the significantly greater effort exerted by subjects in the cell with " h i g h " levels on all three variables as compared with subjects in all other conditions. Planned comparisons (Hays, 1963) indicated that subjects in the High E - H i g h / - H i g h V cell did spend significantly more time working on the spelling tasks than subjects in the other seven cells combined [t (81) = 2.55; p < .01] and that subjects in the L o w E - L o w / - L o w V condition spent significantly less time working than subjects in the combined remaining cells [t (81) = 2.09; p < .05]. While the predicted interaction only approached significance, it should be noted that the general pattern of these data conform to the predictions. High E - H i g h / - H i g h V subjects worked significantly longer than L o w E-Low/-Low V subjects with cell means for the other conditions between these extremes. Subjects in the High E - H i g h / - H i g h V condition worked from one and one-half to over four times longer than did subjects in the other seven conditions. In total, the effects reported accounted for 13% of the variance in the derived effort measure. Although it was predicted that the means of the seven conditions which contained at least one " l o w " value on the expectancy theory variables should not differ, this was not the case. This effect follows if the " l o w " value on each variable were experimentally set at zero. H o w e v e r , the experimental manipulations in this study were ordinal and only insured that one level was low relative to the other. This is supported by the data in Table 1 and reported below in the link No. 2 analyses. As such, these data are not out of line with what one might expect. Link No. 2: Environmental Conditions --~ Belief System In the present study, the accuracy of the perceptual measures of expectancies and instrumentalities was assessed. The second link was tested by separately analyzing these perceptual measures in the 2 × 2 x 2 A N O V A
EXPECTANCY
THEORY
VARIABLES AND EFFORT
143
described above. Since valence was an organismic variable in this study, it was not analyzed. It was predicted that a significant main effect for the expectancy variable only would result when the expectancy measure (E) was used as the dependent variable in this analysis, and that a significant main effect for the instrumentality variable only would result when the instrumentality measure (I) was used. In addition, Force composites were computed for each person by multiplying responses to measures of the expectancy, instrumentality, and valence variables (i.e., Force = E × I x V for one outcome). Since force is said to underlie behavioral choice, these scores were predicted to be distributed in a manner similar to the effort measure itself. Thus, significant main effects and interactions were again predicted with the effects due to the subjects in the cell marked by a " h i g h " level on all three expectancy theory variables. The basic data for these analyses are presented in Tables 1 and 2. Table 1 contains means and standard deviations for each variable and Table 2 summarizes the analyses of variance. Variance estimates again are based on the o)2 statistic. Expectancy measure. Using the perceptual measure of the e x p e c t a n c y term, the only significant effect found was for the expectancy variable. This is as predicted. Subjects in the high expectancy conditions indicated a greater perceived relationship between their effort and thei_fr performance than did subjects in the low expectancy condition (Xmgh ~ = 9.67; XLow E = 4.84; p < .001). This main effect accounted for 49% of the response 'variance. Instrumentality measure. The only significant effect found was for the instrumentality variable. Subjects in the high instrumentality condition indicated a greater perceived relationship between their performance and their chances of obtaining the outcome than did subjects in the low instrumentality condition (XHigh I = 8.06; XLow i = 1.55; p < .001). This main effect accounted for 60% of the response variance. Force measure. For the measure of Force all main and interaction effects were found to be significant. Subjects in the high expectancy condition, the high instrumentality condition, and the high valence condition each had significantly greater force scores than subjects in the low expectancy, low instrumentality, and low valence conditions, respectively: ( X H i g h E ~--- 355.06;XLow E = 120.34;p_p < .00l) ( X H i g h I : 390.38; XLow I = 84.32; p < .001) (XHigh v = 380.71; X~ow v = 94.69; p < .001). Further, the pattern of means, as expressed by the interactions, corresponded to predictions. Exploration of the three-way interaction indicated that the simple instrumentality × valence interaction was nonsignificant for subjects in the low expectancy condition [F (1,81) = 1.12; n.s.] but was significant for subjects in the high expectancy condition IF (1, 81) -19.06; p < .001]. Further exploration of this simple interaction was done
144
LAWRENCE H. PETERS
by comparing the high and low valence conditions within each level of instrumentality. The results of these analyses indicated no difference between valence conditions at the low instrumentality level IF (1, 81) = 1.50; n.s.]. Within the high instrumentality condition, however, subjects in the high valence conditi__onhad greater force scores than subjects in the low valence condition [Xnigh V = 952.37; Xgow V = 214.70; F(1,81) = 62.87; p < .001]. Simple main effects analyses of all two-way interactions further supported the predictions. For each two-way interaction, subjects in cells defined by a "high" level on both variables had significantly greater Force scores than did subjects in cells in which at least one variable was set at its " l o w " level (p < .001 for all comparisons). In all, the pattern of cell means and supporting statistical analyses clearly support the notion that subjects can accurately perceive their situation as reflected in their Force scores. These significant effects accounted for 60% of the variance in the force scores.
Link No. 3: Belief System --->Behavioral Response The third link was tested in the typical manner. Force composites based on perceptual measures of expectancy theory variables (Force = E x I x V) were correlated with a measure of effort. As predicted, the results of this analysis indicated a significant Force-effort correlation of r = .38 (p < .001). Correlations also were computed between each component of the expectancy model and effort. The correlations with effort were .28 for the expectancy measure, .23 for the instrumentality measure, and .29 for the valence measure (p < .05 for all correlations). Each component, thus, contributed to the variance in effort, but none predicted effort as well as the force composite itself. DISCUSSION In the present investigation, subjects were presented with a behavioral choice to make. In essence, the choice was either to remain working on the experimental tasks or to leave the study. The study itself involved setting up experimental conditions to influence their decision to stay. Within the expectancy theory framework, seven of the cells were designed to result in a low Force toward staying. The eighth condition was designed to provide such a Force and to make staying a reasonable alternative to leaving. The data supported the effects of the varying conditions. Not only were two of the three main effects significant and the third nearly so (p < .06), but the pattern of cell means conformed to the predictions. This latter conclusion is supported by the results of the simple effects analyses. In all, it would seem that subjects in a low expectancy condition were less willing to exert effort than those subjects in a high
EXPECTANCY THEORY VARIABLES AND EFFORT
145
expectancy condition, who, in turn, were willing to do so only when a high instrumentality-high valence situation also was present. Analyses a;lso were conducted using actual performance (the percentage of lines correct on the I0 experimental spelling tasks) as the dependent variable. Whereas performance was only marginally correlated with effort (r = . 16, p < .06) and uncorrelated with motivational Force (r = .04, n.s.), it was highly correlated (r = .84, p < .001) with a crude measure of skill level (the', actual percentage of lines correct on the first two pretrial spelling tasks). In effect, good spellers performed consistently better than poor spellers. Of importance theoretically, the Force-effort correlation (r = .38, p < .001) is significantly greater than the Force-performance correlation (r = .04, n.s.) (Z = 2.65, p < .01, Glass & Stanley, 1970). Thus, motivational determinants are more closely linked to effort than to performance. These data underscore the importance of using actual effort expenditure as the appropriate dependent variable in expectancy theory research rather than a measure of performance, a conclusion shared with Campbell and Pritchard (1976) and Mitchell (1974). Since expectancy theory, in all its formulations, is a within-persons theory (Mitchell, 1974), it is somewhat surprising that the present study, which used a between-persons design, found some support for its predictions. In the present instance, this may very well have occurred due to the type of decision which was required of the participants. Subjects had to choose between staying and leaving the experimental situation. It is reasonable to assume that all subjects in this study had a strong Force toward leaving the situation; their presence in some sort of research project was a mandatory course requirement. If this were the case, then each of the experimental conditions provided a counteractive Force toward staying, and the decision to stay and work or to leave would reduce to a decision based on counteractive forces where the Force to leave was reasonably strong across all subjects. At the very least, the Force to leave should have been constant across conditions since subjects were randomly assigned to those conditions. Such an assumption, although weaker, still explains the data. The data from this investigation also supported the intervening links between environmental conditions and the occurrence of behavior. Clearly, perceptions of the effort-performance and performance-outcome relationships were accurately reflected. Force scores also were distributed in a manner consistent with expectancy theory. These data not only confirmed the predictions, but did so strongly. Vroom (1964) pointed to the importance of the perceptual link and indicated that it was a necessary condition for the model to predict behavior. In fact, it is a necessary
146
LAWRENCE H. PETERS
assumption of the model at a conceptual level. This assumption, however, has not been systematically studied for its implications to cognitive models. If behavior is a partial function of one's beliefs about their environment, then accurately perceiving that environment is a necessary precondition for adaptive behavior. While the present investigation indicates that perceptions of environmental conditions can be accurate, it does not address an equally important problem of the causes and consequences of inaccurate perceptions. Interventions based on an expectancy theory model designed to increase the valence of performance or to enhance expectancy beliefs or both may fail to show substantial behavioral effects not so much due to a weak intervention or a weakness in the theory per se but because of perceptual distortion which fails to translate these changes accurately into one's belief system. The process by which one's beliefs influence behavioral choice is the province of expectancy theory and is represented by Link No. 3 in the General Cognitive Model. Most of the research on expectancy theory has been aimed at demonstrating the existance of this link. To date, the literature is consistently supportive and the data from the present study concur. Unlike the typical survey design, however, the present study provided a backdrop against which to judge the Force-effort correlation. Survey design studies must assume that environmental conditions foster the choice of effort expenditure according to expectancy theory predictions. That is, such studies must assume that the dependent variable is, indeed, predictable based on expectancy theory in that particular situation. Since the present data indicated that effort was predictable from the environmental conditions (Link No. 1 analyses), it should follow that perceptual composites based on these environmental conditions likewise should provide this support. This was the case. However, the strength of the Force-behavior relationship in the present study, as in previous research, was not strong (r = .38). While the magnitude of this relationship is far from strong, it should be noted that the variance accounted for by this analysis is comparable to that accounted for by the overall ANOVA. Seemingly, there was little more variance in effort which was predictable by expectancy theory variables in the present situation. More important, however, the variance in effort should be more attributable to the variance in beliefs than to the variance in the environmental conditions. This is, in essence, a defining characteristic of a cognitive model. To test this implication, a covariance analysis was done. If expectancy theory beliefs (Force scores) are immediate determinants of behavioral choice, and if these beliefs were to be partialled out of the effort data, then the resultant data should fail to show the pattern predicted in the overall Analysis of Variance. Such an analysis was performed by partialling Force out of the derived effort score. The residual
EXPECTANCY THEORY VARIABLES AND EFFORT
147
d a t a , a m e a s u r e o f effort n o t p r e d i c t a b l e f r o m k n o w l e d g e o f e x p e c t a n c y t h e o r y b e l i e f s , w a s a n a l y z e d in t h e 2 x 2 x 2 A N O V A . F o r t h e s e d a t a , t h e F r a t i o f o r t h e o v e r a l l t r e a t m e n t e f f e c t w a s n o n s i g n i f i c a n t ( F < 1.00, n.s.). T h e s e d a t a i n d i c a t e t h a t b e h a v i o r s e e m s m o r e c l o s e l y r e l a t e d to o n e ' s b e l i e f s t h a n to t h e e n v i r o n m e n t in w h i c h t h e s e b e l i e f s a r e a n c h o r e d . I n c o n c l u s i o n , t h e d a t a f r o m t h e p r e s e n t s t u d y s u p p o r t e a c h link in t h e General Cognitive Model. These data indicate that expectancy theory v a r i a b l e s a r e c a u s a l l y r e l a t e d to effort e x p e n d i t u r e t h r o u g h t h e p e r c e p t i o n s w h i c h p e r s o n s h o l d . A s t h e t h e o r y p r e d i c t s , s u b j e c t s in this s t u d y w e r e o n l y willing to w o r k l o n g e r t h a n n e e d e d to c o m p l e t e a t a s k u n d e r a s p e c i f i c c o n d i t i o n . T h i s c o n d i t i o n w a s d e f i n e d e x p e r i m e n t a l l y as o n e in w h i c h i n c r e a s e s in effort w e r e a s s o c i a t e d w i t h i n c r e a s e s in p e r f o r m a n c e , a n d i n c r e a s e d p e r f o r m a n c e r e s u l t e d in t h e g r e a t e r l i k e l i h o o d o f a t t a i n i n g a valued outceme.
REFERENCES Arvey, R. Task performance as a function of perceived effort-performance and performance -reward contingencies. Organizational Behavior and Human Performance, 1972, 8, 423 -433. Behling, O., Schreisheim, C., & Tolliver, J. Present trends and directions in theories of work effort. Journal Supplement Abstract Service, Ms. No. 67, American Psychological Association, 1973. Campbell, J., & Pritchard, R. Human motivation. In Dunnette, M. (Ed.), Handbook of industrial and organizational psychology. Chicago: Rand McNally, 1976. Gailbraith, J., & Cummings, L. An empirical investigation of the motivational determinants of task performance: Interactive effects between instrumentality-valence and motivation--ability. Organizational Behavior and Human Performance, 1967, 2, 237-257. Georgopolous, B., Mahoney, G., & Jones, N. A path-goal approach to productivity. Journal of Applied Psychology, 1957, 41, 345-353. Glass, G., & Stanley, S. Statistical methods in education and psychology. Englewood Cliffs, N. J.: Prentice-Hall, 1970. Graen, G. Instrumentality theory of work motivation: Some experimental results and suggested modifications. Journal of Applied Psychology Monograph, 1969, 53 (2, P. 2). Hackman, J. R. Nature of the task as a determiner of job behavior. Personnel Psychology, 1969, 22, 435-444. (a) Hackman, J. R. Toward understanding the role of tasks in behavioral research. Acta Psychologica, 1969, 31, 97-128. (b). Hackman, J. R., & Porter, L. Expectancy theory predictions of work effectiveness. Organizational Behavior and Human Performance, 1968, 3,417-426. Hackman, J. R., & Lawler, E. Employee reactions to job characteristics. Journal of Applied Psychology Monograph, 1971, 55, 259-286. Hays, W. L. Statistics for psychologists. New York: Holt, Rinehart, and Winston, 1963. Jorgenson, D. O., Dunnette, M. D., & Pritchard, R. D. Effects of a performance-reward contingency on behavior in a simulated work setting. Journal of Applied Psychology, 1973, 57, 271-280. LaMer, E. Secrecy about management compensation: Are there hidden costs? Organizational Behavior and Human Performance, 1967, 2, 182-189.
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Mitchell, T. R. Expectancy models of job satisfaction, occupational preference and effort: A theoretical, methodological, and empirical appraisal. Psychological Bulletin, 1974, 81, 1053-1077. Motowidlo, S. J., Loehr, V., & Dunnette, M. D. The effect of goal specificity on the relationship between expectancy and task performance (Tech. Rep. No. 4008). Minneapolis, University of Minnesota, 1972. Pritchard, R., & DeLeo, P. An experimental test of the interactive relationship between valence of job outcomes and performance-outcome instrumentality. Journal of Applied Psychology, 1973, 57, 264-270. Vroom, V. Work and motivation. New York: Wiley, 1964.
REFERENCE NOTES 1. Ilgen, D. R., Peters, L. H., & Campbell, D. J. A systematic study of the sources and effects of work expectations (Tech. Rep. No. 3). Conducted for the Army Research Institute for the Behavioral Sciences, U.S. Army, under Grant No. DAHC 19-74-G0002, April 1976. 2. Ilgen, D, R., & Peters, L. H. Boundary conditions and operationalizations of expectancy theory variables. Unpublished manuscript, Purdue University, West Lafayette, Ind., 1975. RECEIVED: October 7, 1976