Task Complexity Effects on Non-task-related ... - Science Direct

ORGANIZATIONAL

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Task Complexity

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45, 209-231 (1990)

Effects on Non-task-related A Test of Activation Theory

Movements:

DONALDG.GARDNER University of Colorado at Colorado Springs The present experiment tested the activation theory-based predictions that variation in task complexity affects experienced activation level, which in turn affects performance, satisfaction, and the number of non-task-related movements made by task performers. Subjects performed both a low-complexity and a moderate-complexity task. It was predicted that subjects would make more non-task-related movements on the low-complexity task than on the moderate-complexity task, to increase experienced activation levels that were depressed by performance of the low-complexity task. It was also hypothesized that there would be inverted-U relationships between experienced activation level and number of non-task-related movements. Finally, it was predicted that the number of non-task-related movements would be inversely related to performance. Results provided modest support for the hypotheses. Implications for theory and work design are discussed. o 1990 Academic

Press, Inc.

It has long been known that variation in task complexity has effects on affective, cognitive, physiological, and behavioral responses of task performers (e.g., Simonson & Weiser, 1976). For the purposes of this study, task complexity is defined as the degree to which a task provides a variety of stimulation to the task performer, in terms of number of distinguishable and dissimilar elements present in the task-based stimulation, as well as the degree to which the stimulation is variable or novel (cf. Berlyne, 1960; Scott dz Erskine, 1980). There are many different explanations for the effects of task complexity on task performer responses (cf. Steers & Mowday, 1977). Activation theory represents one such explanation for these effects (cf. Fiske & Maddi, 1961; Gardner & Cummings, 1988; and Scott 1966, 1967, for extensive discussions). Yet, only in recent years have activation theory predictions about effects of task complexity on task performers been rigorously examined in applied research (e.g., Gardner, 1986a; Huber, 1985). The purpose of the present study is to broaden this line of activation theory research by The research reported here was funded by the University of ColoradcXolorado Springs Committee for Research and Creative Work. Send requests for reprints to Donald G. Gardner, College of Business and Administration, University of Colorado at Colorado Springs, Box 7150, Colorado Springs, CO 80933-7150. 209 0749-5978190$3.00 Copyri&t Q 1990 by Academic Press, 11% All rights of reproduction in any form reserved.

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examining rarely-tested predictions about the causes and consequences of behaviors that are exhibited during task performance, yet make no direct contribution to task performance levels. These behaviors, henceforth termed non-task-related movements (NTRMs), have been rarely studied in explicit tests of activation theory. Activation Theory There have been several different presentations of activation theory (e.g., Fiske & Maddi, 1961; Gardner & Cummings, 1988; Scott, 1966). The theme that unites these different presentations is that behavior is influenced to a large extent by changes in experienced activation levels (viz., the level of neural activity in the reticular activation system of the central nervous system). Activation level is postulated to be a monotonic function of the total stimulation impacting a person at any given moment, including that stimulation which arises from performing a task. Stimulation may arise from any number of sources, including external (e.g., temperature), internal (e.g., gastric activity), and cerebral (e.g., thoughts) sources. Activation levels are predicted to be curvilinearly related to three major outcomes from task performance: task performance level, task satisfaction level, and frequency of stimulation-modifying behaviors. Moderate activation levels are predicted to result in maximum task performance, all else (e.g., ability) constant. That is, activation theory predicts an inverted-U relationship between activation level and performance (see Fig. 1). There are several reasons why extremes in activation level impede task performance. From a strictly physiological perspective, it is known that low and high activation levels lower sensory receptor sensitivity (e.g., Duffy, 1972), cortical response efficiency (e.g., Lindsley, 1957), and general human body efficiency (e.g., Corlett & Mahadeva, 1970), all of which may directly impair task performance. For example, a task performer may miss task-related perceptual cues necessary for effective performance when activation levels are very low or very high. From a cognitive perspective, it has been theorized that extremes in activation level affect processing of task-based information. For example, moderate activation levels are associated with optimal use of short-term memory and sustained information transfer within memory (Humphreys & Revelle, 1984), as well as maximum use of rehearsal and storage of task-relevant information (e.g., Eysenck, 1985). And, from an operant conditioning perspective, the left side of the hypothesized inverted-U relationship may be explained if task-related responses increase activation levels to moderate levels (Scott & Erskine, 1980). Such task-related responses are reinforced and become more likely

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MOVEMENTS

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FIG. 1. Theoretical relations between experienced activation levels and performance and/ or satisfaction.

to be performed. Stated differently, if task-based stimulation is made contingent upon task performance behaviors, or if task performance behaviors themselves cause moderate activation levels, then activation theory predicts that an individual will be motivated to perform those behaviors. Stimuli that cause moderate activation levels can reinforce task behaviors, resulting in high task performance levels. Activation theory predicts that moderate-complexity jobs cause greater performance levels than low-complexity jobs because moderate activation levels reinforce task performance, in addition to any other nontask reinforcers present in the task situation (e.g., compensation). Moderate activation levels are also predicted to cause maximum positive affect (see Fig. 1). The reasoning that underlies the hypothesized inverted-U relationship between activation level and affect is less compelling than the one for task performance. Nevertheless, the relationship has been observed in a number of contexts (see Maddi, 1961, for one

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review). At this time, it appears that low and high activation levels cause negative affect because of complex relationships between the reticular activating system and other brain structures, especially the limbic structures (cf. Larson & Diener, 1987; Routtenberg, 1968; Scott & Erskine, 1980; Weil, 1974; and Zuckerman, 1979; for extensive discussions). Generally speaking, the limbic brain structures are hypothesized to mediate the experience of pain and pleasure, and are activated by levels of neural activation in the reticular activating system. The exact nature of the relationship between the reticular and limbic systems is not yet wellunderstood, but provides some basis for the relationship illustrated in Fig. 1. Finally, moderate activation levels are predicted to lessen the need for an individual to engage in behaviors that modify stimulation and result in an increase or decrease in experienced activation levels. That is, activation theory predicts a U-shaped relationship between activation levels and stimulation-modifying behaviors (see Fig. 2). Stimulation-modifying

High

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FIG. 2. Theoretical relations between experienced activation levels and stimulationmodifying behaviors and/or non-task-related movements.

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behaviors directly affect the activation level of the individual by changing the external, internal, and/or cerebral sources of stimulation to the reticular activating system. Individuals seek to maintain moderate levels of activation (Fiske & Maddi, l%l), and if a particular environment causes significant deviations from moderate levels, then the individual will initiate behaviors that increase or decrease stimulation, until a moderate activation level is reached (Fiske & Maddi, l%l; Scott, 1966, 1967). Support for the hypothesis that activation levels are curvilinearly related to stimulation-modifying behaviors is rather sparse in organizational behavior research. Scott (1969) discussed one study in which incidents of stimulation-modifying behaviors were related to inferred (but not directly measured) decreases in activation level. In contrast, the study of stimulation-modifying behaviors during task performance has been studied quite extensively by researchers in industrial engineering and ergonomics, behaviors which they generally term non-task-related movements (NTRMs). Non-task-related movements, which are the main focus of this research, are “observable body movements which occur during work, but do not directly contribute to the performance of a task” (Suominen, Basila, Salvendy, 8z McCabe, 1980). Industrial engineering and ergonomic research on NTRMs has focused mostly on the relationships between NTRMs and machine- versus self-paced work (e.g., Kishida, 1973; Suominen et al., 1980). The goal of this research is to design work such that NTRMs are minimized, resulting in high performance levels. Overall, however, industrial engineering research on NTRMs tends to suffer from at least two major flaws. First, research designs are often inadequate, resulting in problems of internal and external validity (e.g., use of single observers to record NTRMs; extremely small sample sizes; cf. Gardner, 1986b). Second, results tend to be highly descriptive, neither invoking nor developing theories about the causes of NTRMs. Researchers generally conclude that allowing freedom to initiate NTRMs is desirable vis-a-vis performance level and satisfaction, yet try to design work that minimizes the incidence of NTRMs. They fail to explain why this should be the case in any theoretically relevant way. The present experiment overcomes these deficiencies with an experimental design, direct measures of activation, and strong theory. In the present study, NTRMs are considered to be dependent variables that are within the nomological network that describes activation theory. It can be hypothesized that the number of NTRMs an individual engages in is a function of the deviation of experienced activation level from moderate levels. Stated differently, NTRMs may be special cases of stimulation-modifying behaviors that operate in a predictable fashion. Lowcomplexity tasks that cause negative deviations from moderate levels (i.e., depressed activation levels) would increase motivation to engage in

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behaviors (e.g., stretching) that raise activation levels. Highly complex tasks that cause positive deviations from moderate levels (i.e., elevated activation levels) would increase motivation to engage in behaviors (e.g., daydreaming) that lower activation levels. Moderate-complexity tasks should cause moderate activation levels, and in turn the fewest numbers of NTRMs (see Fig. 2). And, as indicated above, NTRMs are likely to be reinforced and repeated if they alter activation levels toward moderate levels. Initiating NTRMs is not the only possible response to declining activation levels that result from performing a low-complexity task. Instead, an individual might work at a faster pace to offset declines in activation levels (O’Hanlon, 1981). However, because of habituation processes in the reticular activating system (cf. Scott, 1%6), this would not likely be effective for long. Repeated exposure to nonvarying stimuli ultimately causes an individual to habituate to the stimuli, that is, become unreactive. The increased proprioceptive (muscular) stimulation caused by a faster work pace would lose its activating potential as the reticular activating system became increasingly unreactive to that proprioceptive stimulation. This would further reinforce the enactment of a variety of other, non-task-related behaviors, to which the individual had not yet habituated. By definition NTRMs are non-task-related, that is, do not directly facilitate task performance. It can be predicted that the more an individual engages in such behaviors, the fewer behaviors will be directed toward performance of the task. So, an inverse relation can be expected between NTRMs and task performance level. NTRMs are important dependent variables because of their direct effects on performance, as well as being indicants that a task performer is experiencing a dysfunctional activation level (vis-a-vis moderate levels) as a result of performing a task. Once a moderate activation level is achieved, NTRMs should decrease in frequency and performance level should increase. But, there should be a negative relationship between concomitant measures of NTRMs and performance levels. As long as a task performer engages in NTRMs to offset extreme activation levels, performance level is predicted to be depressed. Hypotheses Task complexity is monotonically related to activation level because the more complex a task is, all else constant, the more sensory pathways are stimulated, and thus the greater the resulting activation levels (Gardner & Cummings, 1988; Scott, 1966). It is this relationship between task complexity and activation levels that forms the basis for most activation

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theory predictions about task design effects. The present experiment examines two levels of task complexity: low and moderate.’ It is hypothesized that: HI. A low-complexity task will cause lower activation levels than a moderate-complexity task. And, consistent with the basic activation theory postulates discussed above, task complexity will have effects on task satisfaction and NTRMs through the intervening variable of activation level. These hypothesized effects are described in the next two hypotheses: H2. A low-complexity task will cause lower task satisfaction than a moderate-complexity task. H3. A low-complexity task will result in more NTRMs than a moderate-complexity task. But, it is not task complexity per se that determines task performer responses. Activation level mediates effects of task complexity. Task performers that exhibit the highest number of NTRMs on a task are predicted to do so because they experience larger deviations of their experienced activation levels from moderate levels, regardless of task. Low activation levels cause negative deviations and stimulation-enhancing NTRMs. High activation levels cause positive deviations and stimulation-reducing NTRMs. The present experiment is not designed to create excessively high activation levels. However, to the extent that at least some subjects experience activation levels even modestly above moderate levels, it may be hypothesized that: H4. There will be U-shaped relationships between measures of activation level and NTRMs. And, because of the direct inverse relationship between NTRMs and task-related behaviors, it is hypothesized that: HS. There will be negative relationships between the number of NTRMs a task performer exhibits and task performance levels. Hypotheses 4 and 5, which are within-task predictions, assume that because of individual differences (cf. Gardner, 1986b), there is variance in experienced activation levels across people performing a given task. That is, because people differ on a number of dimensions (e.g., ability, sensi’ For ethical reasons a moderately complex task versus a very high, and perhaps stressful, task was examined in this experiment.

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tivity to stimulation) they will vary in the degree to which they experience a task as stimulating. This variation in experienced activation levels should reliably relate to differences in NTRMs which, in turn, should reliably relate to performance for reasons discussed above. METHOD

Subjects Subjects were 28 students randomly selected for participation in the experiment from sign-up sheets circulated in an introductory psychology class and a graduate business (MBA) class at a commuter university in a Rocky Mountain state. Half of the subjects were female, 64% worked at least part-time at an outside job, and 50% were over 21 years old (average age was 23 years, and the range of ages was 18 to 39). Participation was voluntary. However, the experimental design required subjects to commute to the university campus three times for a total of approximately six hours. Subjects were therefore compensated ($50) for their participation. Procedure The present laboratory experiment is a one-way within-subjects design with two levels of task. The two tasks used were chosen on the basis of several criteria: they required almost identical physical movements and no advanced training; provided objective measures of performance; and allowed subjects to work alone and to have some degree of external validity. The tasks differ primarily in the degree to which subjects had to cognitively process task-based information. That is, by virtue of gross differences in the degree to which subjects had to make decisions about task performance, one task was much less complex than the other. One task, henceforth referred to as the low-complexity task, required subjects to repeatedly perform a single operation. Subjects were provided with a box containing 1000 wires (7.5 cm long) labeled “STOCK,” an empty box labeled “PASS,” a third empty box labeled “FAILED,” and a small circuit board (15 x 20 cm) which contained four background colors, 41 coiled terminals, a press-key, and a red light-emitting-diode (LED). Subjects read instructions on how to perform the task, were given a physical demonstration by the experimenter, allowed to ask questions about how to perform the task, and then actually performed the task for 45 min. The task required subjects to take wires, one at a time, from the stock box, put an end into each of two designated coiled terminals, press the key, and determine if the LED illuminated. After the LED illumi-

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nated, subjects removed the wire and put it in the pass box (all wires passed). Subjects then repeated the operation, uninterrupted, until the experimenter returned from an adjoining room. No rewards or punishments were explicitly or implicitly tied to performance of the task. The other task is moderately high on the complexity dimension. This task, henceforth termed the complex task, required subjects to build a series of distinct “electronic components.” It differs from the lowcomplexity task in that subjects were provided with: (1) five different lengths of wire, each color coded; (2) a much larger (292% larger; 25 x 35 cm) circuit board that contained 56 coiled terminals, a variety of electronic parts (e.g., capacitors), and 16 different background colors; and (3) a detailed, bound instruction manual that provided directions on how to assemble each of 18 different components (e.g., a circuit with resistance, a flash light, a light dimmer switch, a magnetic pulse generator). After an initial physical demonstration on how to perform the task, subjects read through the manual, connected coiled terminals according to explicit directions (terminals were numbered), and made the electronic components in the order specified in the manual. Except for the task instructions and the task itself, conditions were virtually identical to those for the lowcomplexity task. The experiment was conducted in three phases. In the first phase, subjects reported individually to a laboratory in which a polygraph, a flicker photometer, a table, and a chair were set up. Subjects were briefed on the general purpose and procedure of the study and the equipment to be used (but not the tasks). Subjects were then told to relax so that resting measures of arousal/activation could be made. The laboratory contained a television set and five current popular magazines, and subjects were told that they could read, watch television, or “just sit back” to relax. Before subjects relaxed, the experimenter told them he would be in the adjoining room. The two rooms had a one-way mirror connecting them. Subjects were told that the experimenter could observe them (and not vice-versa), but that he would be “catching up on some reading” and that they should knock on the mirror to draw his attention if necessary. No further mention of the one-way mirror was made. Subjects then rested for 20 mitt, after which they were measured for critical fusion frequency, and 10 min of skin resistance level. Following this, subjects completed selfreport measures of extraversion and tolerance of ambiguity.2 Finally, subjects were scheduled for two return times and dates to complete the

’ Skin-resistance level, extraversion, and tolerance of ambiguity data were collected to test hypotheses about sensitivity to stimulation. Results with these individual differences were generally nonsignificant, and are reported elsewhere (Gardner, 1986b).

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second and third phases of the experiment. All three phases of the experiment were conducted at the same time of day to provide some control for circadian variation in activation. In the second and third phases, subjects reported to the laboratory at their prescheduled times and were immediately measured for critical fusion frequency. Following this, subjects began the task portion of the treatments as detailed above. Task order was counterbalanced: half the subjects were randomly assigned the low-complexity task in the second phase, while the other half received the complex task. Subjects performed the complementary task in the third phase. NTRMs were recorded throughout the 45min task-performance time (see below). After completing the tasks, subjects were measured for critical fusion frequency, and then completed self-report measures of task satisfaction, perceived task characteristics, and self-perceived activation. They were encouraged to consider only the task just performed in completing these questionnaires. They were then debriefed, given pay vouchers, and dismissed. Measures Activation level. Accurate measurement of activation level is critical in tests of activation theory. In the present experiment, two measures of activation level were used. The first was critical fusion frequency, a measure of central nervous system activation (Baschera & Grandjean, 1979; Ginsburg, 1970; Weber, Fussler,,O’Hanlon, Gierer, & Grandjean, 1980). The flicker photometer used was a Lafayette Instruments Model 12025 with a Model 12024viewing chamber. Critical fusion frequency measures were obtained before and after task-performance phases of the experiment. Only post-task performance measures were used in analyses because they reflect the effects of task performance on activation levels, while pretask performance measures reflect activation levels of subjects when they entered the laboratory.3 Ginsburg (1970) provides an extensive bibliography on the reliability and validity of critical fusion frequency. The second measure of activation was a four-item semantic differential scale developed by Scott (1967; Scott & Rowland, 1970). Measures of physiological activation often correlate with their self-report counterparts. For example, Kishida (1973) found that measures of critical fusion frequency and self-reports on a “dull-drowsy” questionnaire paralleled 3 The pretask measures were obtained for three reasons. First, they allowed the experimenter to reacquaint subjects with the equipment and the setting. Second, they allowed subjects time to calm down after any activating experiences they might have had prior to coming to the laboratory. Third, the pretest measures allowed for the possibility of calculating residualized gain sources for the post-task measures. The latter did not produce any new findings.

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one another in a field study of factory workers. Weber et al. (1980) obtamed similar results in a laboratory experiment. Both physiological and self-report measures of activation are believed to index, in different ways, the same activation level construct. Neither type of activation level measure (physiological versus selfreport) is inherently better. Both are capable of producing acceptable levels of reliability (test-retest and internal consistency) and construct validity. Physiological measures are often preferred because they bypass problems associated with self-reports of psychological states (cf. Nisbett & Wilson, 1977, for an extensive discussion of problems with selfreports). Most research subjects cannot distort their physiological responses to experimental treatments, as they can do with verbal reports. On the other hand, a multitude of stimuli affect physiological measures of activation, and thus reflect more than what a researcher is controlling (task complexity in the instant case). Some research indicates that selfreport activation levels are superior to any single physiological measure (cf. Thayer, 1978). Given that neither type of measure has demonstrated universal psychometric superiority, it is best to obtain both types of measures in an attempt to operationalize the activation level construct (much like researchers will use more than one operationalization of variables like job satisfaction). Perceived task complexity. The perceived task stimulation scale developed by Gardner (1982; 1986a)was used to measure perceived task complexity. This 20-item scale results in an overall complexity/stimulation score (based on perceptions of intensity, complexity, meaningfulness, novelty, and variety of stimulation). This measure also provides a score for freedom to initiate stimulation-modifying behaviors, subjects’ selfperceived ability to control their activation levels (e.g., stretching) while performing the tasks. Appendix A shows the items that make up this measure. Task satisfaction. Task satisfaction was measured with the 20-item semantic differential scale developed by Stone (1977; also see Ganster, 1980, and Gardner, 1986afor evidence of construct validity). Appendix B shows the items that make up this measure. Non-task-related movements (NTRMs). Two observers simultaneously watched subjects through the one-way mirror over the entire 45min taskperformance periods.4 Both observers were blind to the subjects’ standings on the individual differences measures, and one observer was blind

’ One of the observers was not present for two of the 56 task performance phases. NTFMs were not analyzed for those two sessions, resulting in an N of 27 for NTRMs for the low-complexity and moderate-complexity tasks.

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to all experimental hypotheses. The observers were seated approximately 1.5 m apart separated by a divider, and independently recorded the following NTRMs: (1) stretching, (2) gazing away from the task, (3) blocking (staring at the blank wall in front of them for at least 2 s), (4) yawning, (5) head/neck movements, (6) torso movements, (7) arm movements, and (8) leg/foot movements. Because the tasks used were designed to produce, at most, moderate activation levels, the NTRMs observed are largely activation-enhancing behaviors that increase low experienced activation levels. Principal components analyses of the averaged data revealed that the eight NTRM categories could not be combined to form more succinct indices (average intercorrelation of NTRMs was .16 within each of the two tasks, equivalent to a coefftcient (Yof 58). The eight categories were therefore analyzed separately, but since there were no incidents of blocking for the complex task, that variable was dropped from some analyses. Performance. Performance level on the low-complexity task was operationalized as the number of wires tested for conductance during the 45min period.5 Performance on the complex task was operationalized as the number of components completed in the 45-min period. Data Analyses

Data were analyzed with a variety of techniques. Hypotheses 1, 2, and 3 were tested with within-subjects MANOVA and orthonormalized contrasts, as recommended by O’Brien and Kaiser (1985). Hypothesis 4 was tested with polynomial regression (Cohen 8z Cohen, 1975). Hypothesis 5 was tested with product-moment correlations. Winer (1971, p. 13) long ago noted that “Too much emphasis has been placed upon the level of significance of a test and far too little upon the power of the test.” The process of a priori selecting an alpha level for assessing the statistical significance of tests of hypotheses involves more than uncritically accepting conventional levels (and this study is not a blanket call for rejection of such conventions). In designing the present experiment, an unconventional .10 (Y level was chosen for tests of hypotheses.6 This represents a compromise a level that considers such 5 Five subjects’ performance data were dropped from the low-complexity task performance analyses because they did not insert the ends of wires completely into the coiled terminals of the circuit board. 6 The power of the F-test increases from .46 to .60 for a between-subjects design with two levels of treatment and an n of 28, when one uses an alpha of. 10instead of the conventional .05 (Cohen, 1%9). This is a conservative estimate of the increase in the present experiment because a within-subjects design is used. As it turns out, most of the effects reported below (14 out of 21) are significant at p < .05.

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issues as the relatively low reliability of physiological measures (cf. Duffy, 1972; Freixa i Bacque, 1982); the costs involved in obtaining a larger sample; small expected effect sizes; the degree to which the hypotheses have been previously developed and tested; the consequences of falsely rejecting the null hypothesis; and potential contributions to our knowledge about human performance that otherwise might not be made (Labovitz, 1970; see also Cohen, 1969, and Morrison dz Henkel, 1970). Finally, it should be noted that a levels often overestimate the actual probability of making Type I errors (Pollard & Richardson, 1987). RESULTS

Table 1 presents descriptive statistics and intercorrelations of experimental variables. The self-report measure of freedom to modify stimulation (measure #4 in Table 1) produced evidence of construct validity on non-self-report measures for the low-complexity task, but not for the complex task. That is, self-reported freedom to modify stimulation correlated with five out of eight of the observed stimulation-modifying behaviors (NTRMs), meaning that subjects who reported greater freedom to initiate stimulation-modifying behaviors (e.g., NTRMs) did in fact do so more than subjects who reported less freedom. Manipulation

Check

In the present experiment it is critical that the complexity levels of the tasks differed as expected (McGrath, 1976). Inspection of the means on the perceived task complexity measure indicates that the low-complexity task was perceived as such (X = 1.83 on a 7-point scale), while the moderate complexity task was also perceived as intended (y = 3.73). Within-subjects MANOVA indicated that these differences are statistically significant (F = 98.64, p < .05). Tests of Hypotheses

Hypotheses 1, 2, and 3 predicted that the low-complexity task would result in lower activation (Hl) and satisfaction (H2) levels, and more NTRMs (H3), than the complex task. This hypothesis was tested with within-subjects MANOVAs (in order) on the two activation measures, the satisfaction scale, and the seven complementary sets of NTRMs. As predicted, the low-complexity task was significantly lower in self-report activation (F = 93.76, p < .05), critical fusion frequency (F = 4.69, p < .05), and task satisfaction (F = 135.50,p < .05). Thus the low-complexity task resulted in lower satisfaction and activation levels than did the complex task. These results support the hypothesis that the low-complexity task would cause greater negative deviations of experienced activation level from moderate levels than the complex task. Because both tasks

X’

SD

(.21) - .25*

.56**

.69**

- .28* .79**

.24

- .02

.22

-.09 -.I9 -.24 - 49

-.ll

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- .13

- .07

- .05

.05

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.02

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- .34**

.33**

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.24

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44

- .36**

.09

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- .Ol

BE

TABLE

I

.02

.22

.32*

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.56**

.35**

.61**

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.03

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4

5

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- .32*

.34**

- .23

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-.31*

- .07

(.33**)

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.74**

.86**

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- .08

.27*

-46

.Ol

.I6

.73**

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.06

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OF STUDY

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AND INTERCORRELATIONS

10

11

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.49**

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- .02

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.03

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38

(NA)

NA

NA

NA

NA

NA

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9

VAIUABLES

12

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.03

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,288

.23

NA

.08

-.22

.61**

- .07

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- .05

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36

13

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.42**

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NA

.24

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.05

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14

(27)

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.20

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Notes. (1) Descriptive statistics in parentheses are for the complex task. Correlations in parentheses oh diagonal are across tasks. Correlations above diagonal are for complex task; (2) Reliability estimates are coefficient alphas for self-report measures and interrater for non-task-related movements; (3) CFF is an abbreviation for critical fusion frequency. *pi .lO: ** p < .05 (one-tailed).

.94 (97) .92 (93) .I36 00) .a7 (24) .81 (NA) .75 (27) .92 (34) .98 C.94) .62 (.95) NA (NA)

.63**

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z (94) .I6 (.91) .76 (.63)

.10

2

- .14

1

(.56**)

3

STATISTICS

NA

Reliability2

1. CFF’-post 36.92 2.84 task (38.70) (5.24) 2. Activation2.61 1.45 self-report (5.67) (1.22) 3. Task 1.83 .51 complexity (3.73) (1.02) 4. Freedom to 3.55 1.51 modify (3.46) (1.21) stimulation 2.64 1.02 5. Task (5.31) (1.07) satisfaction 11.76 11.59 6. Gazing (1.07) (2.00) .83 1.43 7. Stretching W’) 628) 4.32 4.82 8. Yawning (2.04) (3.50) .91 2.30 9. Blocking (0) (0) 3.74 4.00 10. Head (.33) (54) 10.82 9.56 11. Torso (6.11) (7.00) 12. Arm 17.07 14.10 (28.72) (15.62) 13. Leg 29.22 21.50 (29.94) (28.85) 14. Perfor347.35 83.31 mance (14.46) (1.80)

Variable

DESCRIPTIVE

E

B

P

E;

s

8

s

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were self-paced with no performance expectations, and thus caused few restrictions on stimulation-modifying behaviors, there was also no significant difference in perceived ability to modify stimulation across tasks. Results with NTRMs were similarly supportive. Significant effects were found on gazing (F = 21.37, p < .05), stretching (F = 7.69, p < .OS), yawning (F = 4.68, p < .05), arm movements (F = 22.77, p < .05), torso movements (F = 6.07, p < .05), and head movements (F = 17.56, p < .05) NTRMs, with all except the arm movements being more numerous while performing the low-complexity task than the complex task, thus supporting Hypothesis 3. Subjects made more NTRM arm movements performing the complex task than the low-complexity task, and leg movement NTRMs were not significantly different across tasks. To check for effects of task order, MANOVAs were computed again with task-order entered as a between-subjects factor. These analyses indicated a significant task complexity-by-task order effect on head movements (F = 2.88, p < .05). When cells were examined, however, they still indicated that subjects made far more head movements performing the low-complexity task than the complex task. The effect was such that subjects who (randomly) received the low-complexity task second made more head movements while performing that task (x = 4.88) than subjects who received that task first (x = 2.50). Means on the complex task were almost indistinguishable (.46 and .23), but still significantly lower than the means for the low-complexity task (F = 18.89, p < .05). Overall, Hypothesis 3 received moderate support. Hypothesis 4 predicted U-shaped relationships between the activation measures and NTRMs. Three significant quadratic effects (tested hierarchically) of critical fusion frequency were obtained, on the lowcomplexity task for head movement NTRMs (AII* = . 10,p < . lo), and for gazing (AI?* = .16, p < .05) and leg movements (A/Z* = .lO, p < .lO) NTRMs on the complex task. Significant effects were interpreted by plotting predicted values for low, average, and high (sample) activation levels. The first effect supported Hypothesis 4, indicating a reflected-J shaped (mostly U-shaped) relation between critical fusion frequency and head movements. The remaining two interactions contradicted Hypothesis 4, reflecting monotonic relations that asymptote approximately one standard deviation above the critical fusion frequency mean. Five significant quadratic effects were obtained with the self-report measure of activation, on the low-complexity task for head movements (AZ?* = .15, p < .05) and torso movements (AR* = .13, p < .05) NTRMs, and on the complex task for gazing (AI?* = .14, p < .05), yawning (AIZ* = .07, p < .lO), and leg movements (m* = .30, p < .05) NTRMs. When plotted, all five reflected U-shaped relations, as hypothesized. Thus, Hypothesis 4

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tended to be supported with the self-report activation measure, but not with the physiological measure. Hypothesis 5 predicted negative relationships between the numbers of NTRMs and task performance level. Hypothesis 5 was supported only on the low-complexity task. Performance correlated significantly with gazing (r = -.31, p < .lO), head movements (r = -.35, p < .lO), torso movements (r = - .30, p < .lO), and arm movements (r = - .32, p < .lO) NTRMs. As predicted, subjects who made the highest number of NTRMs tended to perform worse than subjects who made fewer NTRMs. Thus, the performance aspect of Hypothesis 5 also received consistent support, but only for the low-complexity task, and only at high (Ylevels. DISCUSSION

A major goal of the present study was to test activation theory predictions about effects of task complexity on worker behaviors. Experimental results, although mixed and based on a liberal experimental OL,generally provide support for the activation theory prediction that when task performers’ activation levels deviate from moderate ranges it affects their reactions to the task. In the present study, a task designed to create experienced activation levels well below moderate levels resulted in low activation and satisfaction levels, and frequent stimulation-enhanced movements (NTRMs), when compared to a task designed to cause moderate experienced activation levels. The hypothesis that variation in experienced activation levels are associated with NTRMs received support. Critical fusion frequency predicted NTRMs for the low-complexity task, while self-reported activation predicted NTRMs for both tasks. Again, like the task main effects, it would appear that low experienced-activation levels increase NTRMs. The fact that results were more consistent with the self-report activation measure than the physiological measure is problematic but common in research on activation levels, in which different measures of the global construct operationalize different specific referents (e.g., Goldwater, 1987; Levenson, 1983; Levis & Smith, 1987). As Duffy (1972) notes, “errors of [activation] measurement must inevitably obscure relationships that may exist, rather than produce erroneous evidence of nonexistent relationships” (p. 590). It might be premature to reject all of the above results because of a lack of convergence of the two activation level measures. Non-task-related movements were consistent, albeit weak, predictors of performance, and only for the low-complexity task. Significant effects were obtained at well above chance rates for the low-complexity task (four out of eight significant at p < .lO), but correlations were rather modest in magnitude. One possible explanation for this result is that

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subjects’ NTRMs were not particularly obstructive to effective task performance. That is, subjects were able to simultaneously engage in both task-related and non-task-related behaviors at the same time. Another possible explanation for the weak correlations might be that NTRMs are indirect indexes of activation levels and would logically have weaker relationships with task performance than the more direct physiological measure (note that critical fusion frequency in contrast accounted for up to 14% of low-complexity task performance variance). Deviation of experienced activation levels from moderate levels may best predict performance, with concomitant NTRMs operating as a rough operational index of the degree of deviation. The lack of replication of the NTRM-performance correlations for the complex task may best be explained by reference to Figs. 1 and 2. The complex task may have created moderate ranges of experiencedactivation levels that, across subjects, resulted in deviations from moderate levels that were too small to produce reliable effects on performance and NTRMs. The relationship between NTRMs and activation levels is predicted to be rather flat at moderate levels of the latter (see Fig. 2). This would result in near-zero correlations of the variables in the moderatecomplexity condition if the moderate-complexity task did in fact produce moderate activation levels. Such effects will always be the case in research on these curvilinear relations unless a researcher is willing to knowingly and purposely overstimulate subjects to examine the right side of the inverted-U. The results have implications for industrial engineering research on task design. The oft-replicated finding that machine-paced work is more stressful than self-paced work (Salvendy, 1982) can be explained with activation theory. Machine-paced work has at least three characteristics that are likely to cause low experienced-activation levels: (1) lack of variation in stimulation, (2) low-complexity of task-based stimulation and worker responses, and (3) restrictions on the degree to which workers can engage in stimulation-enhancing behaviors. Low experienced-activation levels relative to moderate levels may cause the documented stress effects. Self-paced work, while not inherently more stimulating, provides workers some degree of freedom to control their experienced activation levels (e.g., working faster or slower, taking breaks). The more that this control allows workers to maintain moderate experienced-activation levels, the less they will manifest stress effects. Thus, differences found between machine- and self-pacing may be mediated by the pacing-induced worker activation levels. Future research might examine whether type of pacing produces effects on performance and satisfaction after controlling for experienced activation level. Similarly, experimenters might want to test whether increased control over the pace of work reliably relates to

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experienced-activation levels, and ultimately performance and satisfaction. The results from the present experiment, as well as activation theory in general, have implications for current theories of job design, especially the job characteristics model (Hackman & Oldham, 1980). Many of the key variables in the job characteristics model may be explained by activation theory constructs (cf. Gardner 8zCummings, 1988,for an extended discussion; see also Farh & Scott, 1983). Perceptions of job characteristics, key constructs in the job characteristics model, may be determined by activating and reinforcing properties of job-based stimulation. For example, perceptions of job autonomy may arise from an ability to maintain a moderate activation level while performing a job, as opposed to conventional explanations that attribute such perceptions to volition and discretion. Constructs like growth need strength, which are based in psychological need theories of motivation, may in part be manifestations of differences in sensitivity to job content and job context stimulation (Gardner & Cummings, 1988). It would seem that a better understanding of the observed effects of variation in task complexity on task performers might be obtained by simultaneously examining activation theory and job characteristics model explanations for such observed effects. APPENDIX A Perceived Task Stimulation Scale This section requires you to describe the task you performed in terms of the responses you made, the thoughts required, and the degree to which the task was stimulating. There are also some questions about the degree of control you had over the task. Some of the questions are difficult to answer. People are not used to describing tasks in terms of the degree to which they are stimulating or not. Nevertheless, please try to answer the questions as best as you can. If you have any problems, please ask the experimenter for help. Also, please indicate your answers by circling the appropriate number on each scale. 1. How much effort was required to perform this task? 1 ________________ 2 ________________ 3 ________________ 4 ________________ 5 ________________ (j ________________ 7

Very much Some effort effort 2. How complex were the required responses in performing this task?

Very little effort

1 ________________ 2 ________________ 3 ________________ 4 ________________ 5 ________________ fj ________________ 7

Very simple

Somewhat Very complex complex 3. How similar were the thought processes required for this task to thought processes you needed for other jobs that you have now or had in the past? 1 ________________ 2 ________________ 3 ________________ 4 ________________ 5 ____________-___ 6 _--_____-_____-7

Very similar

Some similarity

Not similar at all

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4. How frequently were there times when you did not feel stimulated at all by the task? 1 ________________ 2 ________________ 3 ________________ 4 ________________ 5 ________________ 6 ________________ 7 Often Continuously Seldom 5. How much variety in stimulation was inherent in performing this task? 5 ________________ 6 ________________ 7 1 ________________ 2 ________________ 3 _______________ -4 ________________ Some A great deal Very little of variety variety variety 6. Did you feelfree to do other things (like stretching, taking a break, or simply resting to collect your thoughts) while performing this task? 1 ________________ 2 ________________ 3 ________________ 4 -----_________-5 ---___________-6 -___________---7 Somewhat Not free Completely at all free free 7. How hard did this task make you think? , ________________ 2 ________________ 3 ________________ 4 ________________ 5 ________________ 6 ________________ 7 Somewhat Very hard Not hard hard at all 8. How complex were the required thought processes in performing this task? 1 ________________ 2 ________________ 3 ________________ 4 -----___________ 5 ---_____________ 6 -_______________ 7 Somewhat Very Very complex complex simple 9. How similar were the responses required for this task to responses you made on other jobs what you have now or have had in the past? 1 ________________ 2 ________________ 3 ________________ 4 ________________ 5 ________________ 6 ________________ 7 Not similar Somewhat Very at all similar similar IO. Howfrequently did you have to make some sort of response while performing this task? 1 _____--_--______ 2 ________-_______ 3 ________________ 4 -----___________ 5 ________________ 6 ________________ 7 Continuously Often Seldom 1I. How many diierent types of thought processes did you use in performing this task? 1 --__-------_____ 2 ________-_______ 3 ________________ 4 -----___________ 5 ________________ 6 ________________ 7 Very few Several Very many 12. To what extent must your vision be focused on the task materials in performing this task? 1 ---_-------_____ 2 _______--_______ 3 ________________ 4 -----___________ 5 ________________ 6 ________________ 7 Rarely Sometimes Constantly focused focused 13. How intense was the stimulation inherent in this task? 1 -----------_____ 2 _______ --_______ 3 __-__-__________ 4 -----___________ 5 ________________ 6 ________________ 7 Not intense Somewhat Very at all intense intense 14. How much did the stimulation from this task remind you of other jobs you have now or have had in the past? 1 -----------_____ 2 _-_-_--_-_-----3 _------_________ 4 -----___________ 5 ________________ 6 ________________ 7 Not at Somewhat Very much all 15. How frequently did you have to think about what you were doing? 1 ----------------2 ---------------- 3 ------ ________ -4 -----___________ 5 ________________ 6 ________________ 7 Continuously Often Seldom 16. Did you feel free to vary or change the rate at which you performed this task? 1 -----------_____ 2 _______ --_______ 3 _____-_________ 4 -----___________ 5 ________________ 6 ________________ 7 Not free Somewhat Very free at all free

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17. How complex was the stimulation inherent in performing this task? 1 ________________ 2 ________________ 3 ________________ 4 ________________ 5 ________________ 6 ________________ 7 Somewhat Very complex Very simple 18. To what extent did you make a variety of responses in performing this task? 1 ________________ 2 ________________ 3 ________________ 4 ________________ 5 ________________ 6 ________________ 7 Some Very little Very much variety variety variety 19. Did you feel like you could prevent yourself from becoming understimulated (bored, drowsy) while performing this task? 2 ________________ 3 ________________ 4 ________________ 5 ________________ 6 ________________ 7 1 ________________ Somewhat Not at all Very much 20. Did you feel like you could prevent yourself from becoming overstimulated (excited, overwhelmed) while performing this task? 2 ________________ 3 ________________ 4 ________________ 5 ________________ 6 ________________ 7 1 ________________ Not at all Somewhat Very much Note. Items 1,7, and 13 are perceived intensity questions. Items 2,8, and 17 are perceived complexity questions. Items 3, 9, and 14 are perceived meaningfulness questions. Items 4, 10, and I5 are perceived novelty questions. Items 5, II, and 18 are perceived variety questions. Intensity, complexity, meaningfulness, novelty, and variety items are summed to create a total perceived task stimulation score. Items 6, 12, 16, 19 and 20 are perceived freedom to initiate stimulation-modifying behavior questions. Items 10, 19, and 20 were eliminated from analyses because of low corrected item-total correlations.

APPENDIX B Task Satisfaction Measure Complete this section on the basis of how you would rate evaluate this task. Please indicate your responses by checking the appropriate number on each scale. This task is: 1. Frustrating

Gratifying

------1

2

3

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5

6

7

1

2

3

4

5

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7

----2

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4 ---4

5

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7 __ 7

5

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7

4

5

6

7

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5

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I __ 7

2. Complex

Simple Informal

3. Formal -i-

7

4. Unstructured

Structured I

5. Satisfying - 1

2

Dissatisfying

6. Doubtful

1

2

3

I

2

3 ----3

7. Necessary 8. Boring 1

2

Unnecessary Interesting

9. Valuable

Worthless 1

10. Good

Certain

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1

2 -

2

3 -

3

-

4

-

5

-

6

Bad

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Disliked ---5 7 6 2 3 4 1 Variable 12. Constant ------5 7 4 6 1 2 3 Unpleasant 13. Pleasant -------4 5 7 6 1 2 3 Rigid 14. Flexible 4 5 6 7 1 2 3 Unimportant 15. Important ------4 5 6 7 1 2 3 Awful 16. Nice ------4 5 6 7 1 2 3 17. Sad Happy 4 5 6 7 1 2 3 Slow 18. Fast ------4 5 6 7 1 2 3 Painful 19. Pleasurable ------4 5 6 7 1 2 3 Annoying 20. Pleasing ------4 5 7 6 1 2 3 Note. Items 1,5,8, 10, 11, 13, 16, 17, 19, and 20 constitute the task satisfaction scale. The remaining items are distracters and are not used in analyses. 11. Liked

--

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