electronic performance monitoring and social context...

Journal of Applied Psychology1995, Vol. 80, No. 3, 339-353

Copyright 1995 by the American Psychological Association, Inc.002I-9010/95/$3.00

Electronic Performance Monitoring and Social Context:Impact on Productivity and Stress

John R. Aiello and Kathryn J. KolbRutgers—The State University of New Jersey

In a laboratory study, the presence of individual- or work-group-level electronic perfor-mance monitoring (EPM) was manipulated as participants worked on a data-entry taskalone, as a member of a noninteracting aggregate, or as a member of a cohesive group.The pattern of results suggested the operation of a social facilitation effect, as highlyskilled monitored participants keyed more entries than highly skilled nonmonitored par-ticipants. The opposite pattern was detected among low-skilled participants. No signs ofsocial loafing were detected among group-monitored participants. Nonmonitored work-ers and members of cohesive groups felt the least stressed. The implications of thesefindings for organizations adopting EPM systems are discussed.

Electronic performance monitoring (EPM) is one ofmany technological innovations employees face in today'sworkplace. Using network technology, EPM systems providemanagers with access to their employees' computer termi-nals and telephones, allowing managers to determine at anymoment throughout the day the pace at which employees areworking, their degree of accuracy, log-in and log-off times,and even the amount of time spent on bathroom breaks. Thestudy presented in this article examines how productivityand subjective experiences are affected by EPM and how thesocial context within which monitoring occurs moderatesthat influence.

Electronic Performance Monitoring

In 1987, the U.S. Congress, Office of Technology As-sessment reported that more than 6 million Americanworkers were subject to EPM. By 1990, that number grewto more than 10 million workers (9to5, Working WomenEducation Fund, 1990). Clerical employees and others

John R. Aiello and Kathryn J. Kolb, Department of Psychology,Rutgers—The State University of New Jersey.

A portion of this research was presented at the 1993 Society ofIndustrial and Organizational Psychology Conference in SanFrancisco.

We acknowledge and thank Loretta Wollering and Stephanie Ma-don for their contributions to the execution of this study. We alsothank Rick Guzzo, Paul Paulus, and David Wilder for their com-ments and helpful suggestions on earlier drafts of this article.

Correspondence concerning this article should be addressed toJohn R. Aiello, Department of Psychology, Tillett Hall, Rutgers—The State University of New Jersey, New Brunswick, New Jersey08903.

who perform simple, repetitive tasks represent the pre-ponderance of workers currently subject to electronic ob-servation; however, trends indicate that the work per-formed by professional, technical, and managerial em-ployees will also be electronically observed at some pointin the near future (Garson, 1988; U.S. Congress, Officeof Technology Assessment, 1987).

Some companies monitor the work of individual em-ployees, whereas others choose to review statistics thatfirst have been aggregated to reflect the performance ofthe larger work group. Monitoring at the work-grouplevel is perceived by some to be less intrusive and lessstressful to workers. This has led several countries (e.g.,Norway and Sweden) to enact laws that limit the degreeto which individual performance may be monitored. Al-though monitoring individuals is not prohibited in Ja-pan, most Japanese firms prefer to focus their monitoringefforts on work teams. In the United States, individualmonitoring is most prevalent (9to5, Working Women Ed-ucation Fund, 1990; U.S. Congress, Office of TechnologyAssessment, 1987).

Empirical studies have provided strong evidence linkingEPM with increased stress (Aiello & Shao, 1993; Amick &Smith, 1992; Smith, Carayon, Sanders, Lim, & LeGrande,1992). In one survey of monitored workers, 81% of the re-spondents indicated that electronic observation made theirjobs more stressful (Gallatin, 1989). Another study com-pared the attitudes of electronically monitored insuranceworkers with nonmonitored workers who performed com-parable jobs and found that monitored workers reportedfeeling more stressed (Irving, Higgins, & Safayeni, 1986).In a laboratory experiment, individually monitored partic-ipants exhibited the highest amount of stress, nonmoni-

339

340 JOHN R. AIELLO AND KATHRYN J. KOLB

tored participants showed the lowest level of stress, and par-ticipants who believed that their work was aggregated withothers before it was monitored produced intermediatestress scores (Aiello et al., 1991). Survey, case study, andexperimental data have also indicated an association be-tween EPM and decreased job satisfaction (Cahill & Lands-bergis, 1989; Grant & Higgins, 1989; Irving etal., 1986),increased feelings of social isolation (Aiello, 1993; Amick& Celentano, 1991; Amick & Smith, 1992), and increasedperceptions that generating quantity is more importantthan producing quality work (Gallatin, 1989; Shell & All-geier, 1992).

The increased stress associated with EPM has been at-tributed to changes in job design that often are introducedconcurrently with electronic observation. Specifically, mon-itored workers have complained about increases in work-load and loss of control over the manner in which they per-f&Ym their jobs (Smith et al., 1992). The elevated levels ofpsychological and somatic distress that follow may be un-derstood from within the framework of Karasek's (1979)job strain model. As demand for productivity increases anddecision latitude declines, EPM may transform ordinaryjobs into high-stress positions. Monitoring may also reduceopportunities for employees to socialize at work, leadingsome to suggest that loss of social support is at least partiallyresponsible for the stress associated with EPM (Amick &Smith, 1992).

Research supports the applicability of using a socialfacilitation framework to predict how EPM influencesemployee productivity. Consistent with the premise thatsimple-task performance is enhanced by the presence ofan audience or coactors (Zajonc, 1965), Aiello andChomiak (1992) demonstrated that participants workingon an easy data-entry assignment performed at a higherrate when they believed that their work was electronicallyobserved. Also in keeping with the social facilitation per-spective, complex-task performance has been shown tosuffer among computer-monitored participants (Aiello &Shao, 1992; Aiello & Svec, 1993). Social facilitationeffects have been thought to derive from the mere pres-ence of an audience or coactors (Zajonc, 1965), concernover evaluation (Cottrell, 1972; Geen & Gange, 1977),self-presentation concerns (Duval & Wicklund, 1972),distraction (Baron, Moore, & Sanders, 1978), and atten-tional factors (Baron, 1986; Geen, 1991).

The manner in which monitoring is conducted may in-fluence productivity. When employees believe that theirindividual efforts are being observed, one would expectto find clear social facilitation effects: Performance onsimple tasks should improve, and performance on com-plex tasks should decline. A different productivity pat-tern is expected, however, when work-group level moni-toring is used. Research examining the phenomenon of

social loafing has shown that at times people exert moreeffort when they think they are working alone than whenthey believe their work is being combined with that ofothers (Harkins, Latane, & Williams, 1980; Ingham,Levinger, Graves, & Peckham, 1974; Kerr, 1983). Har-kins (1987) demonstrated that evaluation and identifi-ability of individual contributions are required if groupperformance losses are to be prevented. That is, whenpeople believe that their individual efforts will be moni-tored (i.e., identified) and compared with the work ofothers in the group (i.e., evaluated), social loafing doesnot occur. In contrast, when people believe that theirwork will be pooled with others before it is reviewed, thepossibility for individual contributions to be identifiedand evaluated is eliminated, and productivity loss results.Work-group-level monitoring, therefore, might not pro-duce the same boost in simple-task performances as in-dividual-level monitoring.

This study used the frameworks of social facilitationand social loafing to predict how EPM influences produc-tivity. Specifically, participants working on a simple data-entry task were expected to perform at a higher rate whentheir individual efforts were electronically monitoredthan when their work was not observed. Participantswhose work was monitored at the work-group level wereexpected to show some improvement in performance,but that improvement was not expected to be as high asfor those who were individually monitored. We also pre-dicted that monitored participants would have a morenegative subjective experience during the experiment andwould report feeling more stressed than nonmonitoredparticipants.

Social Context

Almost all investigations of EPM study how electronicpresence affects individual workers rather than the workgroup to which employees belong. Yet, social phenomenacannot be completely understood without consideringthe larger social context in which they occur. Researchersin organizational development, for example, have longadvised that researchers adopt a systems view when in-vestigating workplace issues (Beer & Huse, 1972; Fried-lander & Brown, 1974). By using this approach, re-searchers can examine how EPM precipitates structuralchanges in the workplace, as some researchers (Aiello,1993; Amick & Smith, 1992; Cahill & Landsbergis,1989) did when they documented that workers becamephysically and socially isolated from one another aftermonitoring was introduced. Likewise, researchers canexamine how the composition of the larger work systemmoderates the effects of EPM, as others (Chalykoff & Ko-chan, 1989; Kidwell & Bennett, 1994) did when they ex-

ELECTRONIC PERFORMANCE MONITORING 341

plored the management practices that promote employeeacceptance of electronic observation.

The current study addressed this issue by consideringhow electronic presence influences individuals workingalone, individuals surrounded by strangers (i.e., aggre-gate members), and individuals surrounded by membersof a cohesive group to which they belong. We predictedthat people who worked alongside of others (i.e., aggre-gate and group members) would produce at a higher rateon a simple task than people who worked alone, regard-less of the presence or the absence of EPM. This predic-tion derives from the social facilitation framework, inwhich performance is thought to be influenced not onlyby an audience but by coactors as well (Zajonc, 1965).Moreover, we expected that membership in a cohesivegroup would have a particularly enhancing influence onproductivity, independent of monitoring condition. Fi-nally, we expected that members of cohesive groupswould experience less stress than people who worked inthe presence of strangers or alone.

Numerous studies have examined group cohesive-ness in the workplace and have found that cohesivenessoften produces results that are beneficial both to theindividual employee and to the organization in whichthe employee works. Members of cohesive work groupstend to experience reduced turnover (Van Zelst,1952), greater job satisfaction (Janssens & Nuttin,1976; Manning & Fullerton, 1988; Van Zelst, 1952),a more positive view of the work climate (Janssens &Nuttin, 1976), and lower stress (Manning & Fullerton,1988). The effects of group cohesiveness on productiv-ity appear to be less straightforward, however. Groupcohesiveness may lead to both increases and decreasesin productivity (Guzzo & Shea, 1992). It is also possi-ble for productivity to influence group cohesiveness asmuch as group cohesiveness influences productivity.For example, Bakeman and Helmreich (1975) foundthat high productivity at Time 1 was associated withstronger cohesiveness at Time 2. That is, productivitypreceded, rather than followed, cohesiveness.

Generally defined as "the forces operating on the mem-bers to remain in the group" (Festinger, 1950, p. 274),three sources of group cohesiveness have been identified.These are task based, where members believe that groupparticipation will help them achieve desired goals; socio-emotional, where cohesiveness is based on mutual likingand a desire to affiliate; and prestige based, where partic-ipants are attracted to the status conferred by groupmembership (Anderson, 1975; Back, 1951; Festinger,1950; Tziner, 1982; Zaccaro, 1991). Groups high in task-based cohesiveness tend to outperform noncohesivegroups on additive tasks (tasks in which group perfor-mance scores are calculated by summing the scores ob-

tained by individual group members). However, groupshigh in socioemotional cohesiveness generally are notmore productive than noncohesive groups, because par-ticipants in socioemotional groups tend to interact withone another during task performance in a manner thatdisrupts overall productivity (Zaccaro, 1991; Zaccaro &Lowe, 1988). Researchers (e.g., Schachter, Ellertson,McBride, & Gregory, 1951; Tziner, 1982) have been ableto obtain performance gains in socioemotional groups,however, when group members are encouraged to valueperformance goals. It appears that a group's motivationalorientation moderates the relationship between socio-emotional cohesiveness and productivity. Therefore, highproductivity may be expected of groups characterized bystrong mutual attraction but only when group norms aredirected toward, rather than away from, achievement.

The protection against stress provided by group cohe-siveness has been attributed to the benefits associatedwith receiving social support (Manning & Fullerton,1988). Research has suggested that social support sys-tems have a main effect on perceived well-being, wherebythey protect people from physical and psychological dis-orders regardless of whether stressors are present, and abuffering effect, whereby social support systems reducethe impact of stressors on system members (Cobb, 1976;Cohen & Wills, 1985; House, Landis, & Umberson,1988). Consistent evidence has been found to supportthe main effects model; however, studies examining thebuffering hypothesis have produced more equivocal re-sults (Kobasa, 1982; Shinn, Rosario, Morch, & Chest-nut, 1984).

Summary of Hypotheses

Hypothesis 1: Individually monitored workers will performat a higher rate on a simple task than group-monitored andnonmonitored workers. Group-monitored workers will per-form at a somewhat higher rate than nonmonitored workers.

Hypothesis 2: Monitored workers will experience morestress than nonmonitored workers.

Hypothesis 3: Aggregate and group members will performat a higher rate on a simple task than individuals who workalone.

Hypothesis 4: Members of cohesive work groups will per-form at a higher rate than members of noninteractingaggregates.

Hypothesis 5: Members of cohesive groups will experienceless stress than members of noninteracting aggregates andindividuals who work alone.

We also examine whether social context moderates theeffect EPM has on productivity and stress. For example,we may find that people who are monitored while work-


ing alone feel more stress than group members who aremonitored.

Method

Participants

Participants were 202 undergraduate students enrolled in anintroductory psychology course at a large northeastern univer-sity. They received partial course credit for their participation.

Design

Separate sessions of the experiment were run for each cellof the design. All participants performed a simple task whileworking alone, as a member of an aggregate, or as a member ofa cohesive group. The work they performed was monitored atthe individual level, at the work-group level, or not at all. Thatis, the social context condition and the EPM condition were or-thogonally manipulated to create a 3 X 3 factorial design.

Procedure

Participants assigned to the aggregate and group socialcontext conditions reported to the experiment location, a com-puter laboratory on the university campus, with 3-6 other par-ticipants with whom they were unacquainted. The laboratorywas equipped with 35 IBM personal computers arranged in fiverows. One computer in the center of the fifth row was designatedthe "supervisory" computer and was used by the study supervi-sor during the monitoring phase of the experiment. In the frontof the laboratory, a semicircle of chairs was arranged for use bygroup members during the cohesiveness-building exercises inwhich they participated.

Some participants assigned to the alone social contextcondition reported by themselves to this same laboratory fortheir sessions of the experiment. For logistical reasons, theremaining participants in the alone condition worked in asmaller laboratory equipped with one personal computerworkstation for the participant and a second supervisorycomputer used by the study supervisor during the monitoringmanipulation. Analysis of performance and subjective expe-rience scores for participants in the alone social context con-dition revealed negligible differences between individualswho reported to the larger laboratory and those who reportedto the smaller laboratory (p > .10).

When participants arrived at their designated location, theywere greeted by the experimenter and the study supervisor andwere directed to sit at their assigned computer workstations.The supervisor informed the participants that they would betaking part in a study examining job design and thus would beworking on a series of tasks that simulated the types of behaviorsnormally performed each day by employees in business. Partic-ipants were told that their first assignment would be to practiceworking on a data-entry task, whereby they would key into theircomputers a series of six-digit numbers from a worksheet. Afterthe supervisor instructed the participants on how to use thedata-entry software, participants worked on the task for 5 min.

The software was designed to record the number of six-digitentries keyed by each participant during this 5-min period, thusproviding a practice score that could be used as a measure ofeach participant's initial data-entry ability and baseline moti-vation level.

Social context manipulation. After participants completed thepractice data-entry assignment, the supervisor informed them thatthey would work next on a series of tasks that characterized thetypes of behaviors often performed in a typical business meeting.In reality, this portion of the experiment was designed to induce asense of cohesiveness among those participants assigned to thegroup social context condition while preventing a sense of con-nectedness from forming among aggregate members. Groupmembers, aggregate members, and participants assigned to thealone social context condition all performed the same series ofexercises, yet the manner in which they were permitted to engagein them was varied to foster the emergence of three distinct socialclimates.

Research has indicated that social attraction may be createdunder laboratory conditions by having small groups of partici-pants merely interact with one another (Insko & Wilson, 1977)or participate in simple self-introduction exercises (Zaccaro &Lowe, 1988). In addition, a sense of cohesion has been pro-moted among dyad members by furnishing participants withfalse information about their unusual degree of compatibilitywith one another (Back, 1951;Tziner, 1982). Providing mem-bers with bogus feedback about their group's success on a par-ticular task has also been effectively used as a means of enhanc-ing feelings of connectivity and attraction (Anderson, 1975;Blanchard, Adelman, & Cook, 1975; Tziner, 1982). In the cur-rent study, all of these techniques were used to foster a sense ofcohesiveness among group members.

After completing the practice data-entry task, participants as-signed to the group social context condition left their computerworkstations and were reseated for their "business meeting" in thesemicircle of chairs situated at the front of the laboratory. The su-pervisor asked the 4-7 group members to introduce themselves toone another by relating their name, major, psychology class section,and reason for participating in the experiment. To ensure that groupmembers interacted with one another rather than with the supervi-sor, the supervisor walked away from the participants while theyengaged in this exercise.

When this introduction activity was completed, the supervi-sor returned and informed the group members that becausebusiness meetings are often used as forums for brainstorming,their next task would be to collectively develop a list of as manyuses as they could think of for a student identification card. Agroup member seated at an end of the semicircle was asked torecord the group's responses on a large white board visible to allmembers in the group. Group participants were provided with5 min to complete the brainstorming task. Again, the supervi-sor left the group before the activity was initiated.

When this exercise was completed, the experimenter calledtime and distributed a pseudoprojective test (a brief, forced-choice, inkblot inventory), which each participant completedindependently. These were collected and presumably scored bythe experimenter while each participant worked alone on a fillerquestionnaire.


After all group members had completed the filler question-naire, the study supervisor approached the group members andprovided them with bogus positive feedback about their perfor-mance on the brainstorming task. She reported the total num-ber of items the group had produced and informed participantsthat their group had done an "excellent job," The supervisorindicated that they were "among the best of the groups who hadparticipated in the study to date" and, citing several examplesfrom the group's list, related that their ideas were notably morenovel and creative as well. The experimenter then returned toreport that the group members had obtained unusually similarscores on their inkblot tests. She explained that this could beinterpreted to mean that their "inner traits" were quite compat-ible in important ways that helped them work well together as agroup. The experimenter then informed the participants thatthe business meeting was over and that they should return totheir computer workstations to await instructions from the su-pervisor for their next appointed task.

Participants assigned to the aggregate social context condi-tion performed the same business-meeting tasks as group mem-bers, but they worked on them without interacting with theiraggregate coparticipants. After completing the practice data-en-try task, aggregate members remained at their individual com-puter workstations, in contrast to group members who movedto the more intimate milieu of the semicircle. Aggregate mem-bers accomplished the self-introduction task by recording on apiece of paper their name, major, and so forth. They did notshare this information with the 3-6 other aggregate membersworking in the same room as them. Aggregate members alsoworked independently on a paper-and-pencil version of thebrainstorming task and after completing the pseudoprojectivetest and filler survey, were not provided with any feedback re-garding their degree of compatibility with coparticipants. In-stead, they were instructed to merely move on to the next as-signed task.

Like aggregate members, participants assigned to the alonesocial context condition also worked on paper-and-pencil ver-sions of the business-meeting tasks, yet they did so without thecompany of coparticipants. That is, except for the presence ofthe experimenter and the supervisor, these individuals werealone in the laboratory.

Monitoring manipulation. After the social context manipu-lation was completed, participants were informed by the super-visor that they would now work for 15 min on the same type ofdata-entry task that they had practiced earlier in the experi-ment. The supervisor reviewed the software instructions withthem and then initiated the monitoring manipulation.

Participants assigned to the individual monitoring conditionwere told by the supervisor that their computers were connectedthrough a network to a supervisory computer at the rear of theroom. They were led to believe that through this system, theirsupervisor could and would observe their individual data-entryperformance while they worked.

Participants assigned to the work-group monitoring condi-tion were also told about the monitoring system but were in-formed that the software permitted the supervisor to observeonly the combined performance of all the workers in the room.That is, their work would be consolidated with that of their Co-

participants before it was viewed by the supervisor. Work-group-monitored participants assigned to the alone socialcontext condition were led to believe that the supervisor couldmonitor only the combined output of their performance mergedwith that of other study participants who worked in differentlaboratories at the same time. This account was made believableby having a confederate who "was looking for one of the otherlocations of the experiment" knock at the door just prior to thebeginning of the study.

Participants assigned to the no-monitoring condition weretold nothing about the supervisory computer or the monitoringsystem. To further limit the degree of evaluation the partici-pants felt, the supervisor explained that she had other work todo and therefore could not stay in the laboratory with themwhile they worked on the data-entry task. In contrast, in boththe individual-level monitoring and work-group-level monitor-ing conditions, the supervisor remained in the room and ap-peared to operate the supervisory computer while the partici-pants worked.

After the participants finished working on the data-entrytask, the experimenter administered a series of questionnairesdesigned to elicit participants' self-reported ratings of stress,satisfaction, motivation, and other subjective impressions aboutthe experiment. Aggregate and group members also completeda questionnaire assessing their degree of attraction to each otheras well as their sense of group cohesiveness. Finally, for purposesunrelated to this study, participants completed the RosenbergSelf-Esteem Scale (1965) and the Internal/External Locus ofControl Scale (Rotter, 1966). They were then debriefed.

Results

Preliminary Analyses

Participants. Because of equipment failure, we wereunable to collect performance scores for 8 participants.Questionnaire data for these participants have been omit-ted from all analyses. In addition, data for 2 participantswere discarded when a preliminary review revealed thattheir performance scores fell more than three standarddeviations below the mean (i.e., they were outliers). Itappears that these participants either failed to follow in-structions or failed to take the experiment seriously, asthey each keyed only 5 entries during the 5-min practicedata-entry session (M = 65.86) and between 16 and 18entries during the 15-min data-entry session (M =206.59). After incomplete and outlier data were deleted,performance scores and questionnaire responses fromthe remaining 192 participants were available foranalysis.

Manipulation checks. Responses on the postexperi-mental questionnaire indicated that monitored partici-pants believed that they were observed when they wereworking on the data-entry task. Nearly all participantsresponded correctly to the question, "Was your perfor-mance on the data-entry task monitored by the supervi-


Table 1Social Context Manipulation Check

Questionnaire response

Social context

Agg. Group df

Interacted with coparticipantsAware of other participantsCompatible with other participantsSimilar to other participantsLike other participantsIf study continued, desire to work with same participantsDesire to work with same participants on class assignmentDesire to work on real job with same peopleDesire to attend social event with same people

1.474.053.673.593.824.263.974.653.33

4.065.044.874.295.065.244.965.434.16

136.43*** 1 1258.58** 1

23.45*5.86*

35.94*11.24*17.92*

* 11

* 11

* 18.25** 1

10.01** 1

124124124124117124125124

Note. The scale ranged from 1 to 7, with larger numbers indicating more of the attribute. Agg. = aggregate.**;><.05. ***p<.001.

sor through her terminal?" When asked to report the de-gree to which they felt that their work was evaluated byusing a 7-point scale ranging from 1 (not at all) to 7 (agreat extent), nonmonitored participants scored belowthe scale midpoint (M = 2.86), whereas individuallymonitored (M = 4.55) and work-group-monitored (M =4.19) participants scored above the midpoint, F( 2, 181)= 13.42, p < .001. Contrary to expectations, a post hocScheffe test revealed that individually monitored partici-pants did not report feeling significantly more evaluatedthan did participants who were monitored at the work-group level. Nevertheless, a ScheffS test showed that indi-vidually monitored participants felt more aware of thesupervisor (M = 4.27 on a 7-point scale ranging from 1= not at all aware to 7 = very aware) than either group-monitored(M= 3.32) or nonmonitored (Af = 2.77)par-ticipants, F(2, 182) = 12.31, p < .001. Individually mon-itored participants also reported that monitoring affectedtheir performance to a greater extent (M = 4.10 on a 7-point scale ranging from 1 = not at all affected to 7 = veryaffected) than participants who were monitored at thegroup level (A/= 3.08 ),F(1, 122) = 7.85, p< .05.

Postexperimental questionnaire responses indicatedthat the social context manipulation was successful (seeTable 1). Group members reported that they interactedmore with their coparticipants and were more aware ofthem than were aggregate members. In addition, groupmembers reported feeling more compatible with andmore similar to one another and liking one another morethan did aggregate members. Finally, group members in-dicated a stronger desire to remain with the group, evenoutside the context of the experiment, than did aggregatemembers. These findings suggest that the proceduresused in the experiment successfully induced a sense ofcohesiveness among group members that was not feltamong participants assigned to the aggregate condition.

Description of Analyses

To determine if participants in the nine experimentalconditions had equivalent baseline performance scores,we ran a 3 X 3 (Social Context X Monitoring Condition)analysis of variance (ANOVA), using baseline perfor-mance as the dependent measure. No significant differ-ence was detected among participants assigned to thethree monitoring conditions, F(2, 183) = 1.21, p > .10.However, the ANOVA did reveal a significant differencefor social context, F(2, 183) = 3.92, p < .05. A post hocScheffe test showed that participants assigned to the co-hesive group condition keyed at a higher rate prior to theexperimental manipulation than participants assigned to

Table 2Baseline Performance Scores

Socialcontext

AloneMSDn

AggregateMSDn

GroupMSDn

OverallMSDN

Monitoring condition

None

65.5217.7721

62.7522.6516

79.9638.0123

70.3228.9260

Individual

61.7515.1220

60.0517.5821

70.2317.8822

64.1417.2863

Work group

64.1522.5820

64.6416.7325

67.0018.3724

65.3218.8969 ,

OverallM

63.84^18.4861

62.60.18.4862

72.35b

26.6769

63.9322.12

192

Note. Means with different subscripts differ significantly at p < .05,using the SchefK post hoc contrast of means.


Table 3Summary of Hierarchical Regression Analysis for Postmanipulation Performance

Variable B

Note. F values represent F associated with incremental R 2.V<.10. **p<.05. ***p<.001.

df

Step 1Baseline performance (A)

Step 2Monitoring (B)

Individual vs. NoneGroup vs. None

Social context (C)Aggregate vs. AloneGroup vs. Alone

Step 3A X B

A X Individual vs. NoneA X Group vs. None

A X CA X Aggregate vs. AloneA X Group vs. Alone

B X C

2.05

16.3713.49

6.424.10

1.241.59

0.04-1.25

15.18***

2.20**1.85*

0.870.56

4.03***5.67***

0.124.21***

.55***

.55***

.55***

.68***

.65***2,184

.57***

.01* 2.96*

.00 0.52

.12*** 35.29***

.10*** 26.33***

.01 1.09

2,188

2,188

2,184

2,184

4,182

the aggregate condition (see Table 2). The interaction be-tween social context and monitoring condition was notsignificant, F(4, 183) = 0.71, p> .10.

To help control for differences in baseline ability acrossthe nine cells of the experiment and to account for the highcorrelation between baseline performance and postmanipu-lation performance (r = .74, p < .001), we analyzed all per-formance-related hypotheses using hierarchical regressionanalysis. Baseline performance was entered alone in the firststep of the analysis. In the second step, monitoring conditionand social context were added. Two dummy variables werecreated to accommodate the three levels of performancemonitoring and to permit comparisons between individuallymonitored participants and the nonmonitored control groupand between group-monitored participants and the non-monitored control group. Likewise, two dummy variableswere created to reflect the three levels of social context andto permit comparisons of aggregate and group memberscores with the scores of participants who worked alone. Inthe third step of the regression analysis, two-way interactionsbetween baseline performance, monitoring condition, andsocial context were entered.

The results of the hierarchical regression analysis aresummarized in Table 3. This analysis revealed the pres-ence of a significant interaction between baseline perfor-mance and monitoring condition. To further explore thisinteraction, we created separate regression equations foreach monitoring condition, in which baseline perfor-mance was entered as the independent variable and post-manipulation performance was entered as the dependentvariable (see Table 4). The interaction between baselineperformance and social context was explored in the same

manner (see Table 4). Figures 1 and 2 illustrate theseinteractions by showing predicted postmanipulation per-formance scores for participants with baseline scores be-tween one and one-half standard deviations above and be-low the mean.

The postexperimental questionnaire contained 48items designed to assess study participants' subjective re-actions to the monitoring and social context manipula-

Table4Performance for Each Monitoring and SocialContext Condition

Condition R2 B

MonitoringNone

InterceptBaseline

IndividualInterceptBaseline

GroupInterceptBaseline

Social contextAlone

InterceptBaseline

AggregateInterceptBaseline

GroupInterceptBaseline

.28 23.02***

.83 302.56***

.94 1,004.28***

.77 193.48***

.87 386.86***

.31 29.68***

120.681.21

27.222.85

-4.863.28

3.323.06

17.362.98

133.061.21

6.29***4.80***

2.51**17.39***

-0.6931.69***

0.2313.91***

1.76*19.67***

7.75***5.45***

*p<.10. **p<.05. ***p<.001.

346

400

350

300

250

200

JOHN R. AIELLO AND KATHRYN J. KOLB

400

150

100

20 30 40 50 60 70 80 90 100 110

Baseline Score

Figure 1. Predicted performance scores by monitoringcondition.

350

300

250

200

150

100

50

iroup

H 1 1 h20 30 40 50 60 70 80 90 100 110

Baseline Score

Figure 2. Predicted performance scores by social context.

tions. These 48 questions addressed six general areas:stress, motivation, and task climate associated with thedata-entry task and stress, motivation, and task climateassociated with the brainstorming task. Participants re-sponded to each question by circling a response on a 7-point Likert-type scale. (See the Appendix for samplequestions.)

Questionnaire responses were analyzed by exploratoryfactor analysis (varimax rotated) in which a six-factorsolution (representing the six topic areas) was requested.The six factors accounted for 58% of the total variance.Only one item ("Did you feel that your work on thebrainstorming task was being evaluated during the timeyou were working on the task?") loaded on the sixthfactor; therefore, that factor was omitted from furtheranalyses. The remaining five factors, which accounted for53% of the total system variance, reflected data-entrystress, data-entry motivation, brainstorming stress,brainstorming motivation, and general task climate.

Composite scales representing each factor were createdby summing items with factor loadings greater than orequal to .40. Correlations among the five compositescales are presented in Table 5. A listing of the items inthe two stress scales and their factor loadings can befound in the Appendix. Mean scores for these scales arepresented in Tables 6 and 7. Details regarding the re-

maining three scales are not presented here because theydo not relate directly to the hypotheses presented in thisarticle. Where appropriate, however, general findings re-lated to components of these scales are discussed.

We tested the hypotheses regarding subjective responseto EPM and social context by performing a 3 X 3 (SocialContext X Monitoring Condition) ANOYA, with the twocomposite stress scales as dependent measures. Summa-ries of these analyses are presented in Table 8. Tests of thehypotheses related to EPM and social context are dis-cussed in the sections that follow.

Table 5Intercorrelations Among Subjective Scales

Variable 1

1. Data-entrystress

2. Data-entrymotivation

3. Brainstormingstress

4. Brainstormingmotivation

5. General climate

—

-.16**

.12

.11-.40***

—

.16** —

.09 -.55*** —

.29*** -.18** .08 —

"/x.05. "p<.001.


Table 6Composite Data-Entry Stress Scores

Socialcontext

AloneMSD

AggregateMSD

GroupMSD

OverallMSD


None

47.209.83

44.078.12

48.639.85

47.02.9.45

Individual

43.6312.55

39.8914.85

39.7612.17

41.05b13.10

Work group

43.309.85

44.6410.48

40.2612.48

43.35.,,,11.09

Overall M

45.4110.70

42.9711.60

42.9712.14

43.7511.51

Note. Lower composite scores indicate higher stress. Means withdifferent subscripts differ significantly at p < .05, using the Schefffe posthoc contrast of means.

Effects of Electronic Performance Monitoring

In Hypothesis 1, we predicted that EPM would influ-ence productivity. Individually monitored participantswere expected to perform at a higher rate on a simple taskthan participants who were monitored at the work-grouplevel or who were not monitored at all. Participants whowere monitored at the work-group level were expected toshow some improvement in performance; however, thisimprovement was not expected to be as pronounced asthat shown by individually monitored workers. Support-ing our first hypothesis, the regression analysis revealedthat individually monitored participants keyed signifi-cantly more entries than nonmonitored participants.Group-monitored participants also keyed somewhatmore entries than nonmonitored participants, althoughthis difference was only marginally significant (see Table3). Because of the significant interaction found betweenmonitoring condition and baseline performance, how-ever, it is inappropriate to interpret these main effects.Instead, analysis of the interaction is more enlightening.As can be seen in Figure 1, participants with high-base-line scores keyed more entries when their work was mon-itored, regardless of monitoring method. No socialloafing was detected among high-baseline participantswhose work was monitored at the work-group level. Incontrast, participants with low-baseline scores keyedfewer entries when they were monitored in either individ-ual or work-group mode.

Two interesting findings emerged when the variouscomponents of the data-entry motivation scale were ana-lyzed using a 3 X 3 (Social Context X MonitoringCondition) ANOVA. First, a main effect for monitoringcondition was detected for the question, "How motivated

were you to perform well on the data-entry task?" F(2,181) = 3.75, p < .05. A post hoc Scheffe test revealed thatindividually monitored participants reported feeling themost motivated (M - 5.91 on a 7-point scale rangingfrom 1 = not at all motivated to 7 = very motivated),nonmonitored participants reported feeling the least mo-tivated (M = 5.19), and group-monitored participantsdid not differ significantly from either nonmonitored orgroup-monitored participants (M = 5.65). Second, a sig-nificant main effect for monitoring condition was foundfor the question, "How important do you think the data-entry task is?" F(2, 180) = 3.20, p < .05. Individuallymonitored participants reported that they felt the data-entry task was more important (M = 4.94 on a 7-pointscale ranging from 1 = not at all important to 7 = veryimportant) than did nonmonitored (M = 4.25) andgroup-monitored (M = 4.24) participants.

Hypothesis 2 predicted that monitored workers wouldexperience more stress than nonmonitored workers. Ascan be seen in Table 8, a main effect for monitoring con-dition was detected when the data-entry stress scale wasentered as the dependent variable, F( 2, 175) = 3.99, p <.05. A post hoc Scheffe test revealed that individuallymonitored participants reported feeling the moststressed, nonmonitored participants reported feeling theleast stressed, and group-monitored participants re-ported stress scores that were intermediate (they did notdiffer significantly from either individually monitored ornonmonitored participants; see Table 6).

Influence of Social Context

In Hypotheses 3 and 4, we predicted that the socialcontext in which one works would influence productivity.

Table?Composite Brainstorming Stress Scores

Socialcontext

AloneMSD

AggregateMSD

GroupMSD

OverallMSD


None

39.0012.96

42.2014.28

54.2611.22

45.8614.26

Individual

50.5013.01

47.6716.20

52.819.15

50.3213.07

Work group

39.1011.01

46.8415.75

54.6512.08

47.2114.49

OverallM

42.85.13.32

45.98.15.47

53.94b10.80

47.8214.01

Note. Lower composite scores indicate higher stress. Means withdifferent subscripts differ significantly at p < .05, using the SchefK posthoc contrast of means.


Table 8Summary of Analyses of Variance for Stress Scales

Source df SS

Data-entry stress

Social context (A)Monitoring (B)A X BErrorTotal

224

175183

238.391,023.27

289.4822,467.1424,246.50

0.933.99**0.60

Brainstorming stress

Social context (A)Monitoring (B)A X BErrorTotal

224

179187

4,299.33721.56

1,305.8130,187.6336,514.32

12.75***2.141.94

"p<.05. ***;>< .001.

Specifically, participants who worked in the presence ofcoactors (i.e., aggregate and group members) were ex-pected to be more productive on a simple data-entry taskthan participants who worked alone. Moreover, membersof cohesive groups were expected to work at a higher ratethan members of noninteracting aggregates. Contrary tothese hypotheses, no main effect for social context wasdetected (see Table 3). However, we did find a significantinteraction between social context and baseline perfor-mance (see Tables 3 and 4). As expected, low-baselinegroup members keyed more entries than low-baseline ag-gregate members or participants who worked alone; how-ever, high-baseline group members actually keyed fewerentries than their high-baseline counterparts (see Figure2). Also, although we had predicted that aggregate mem-bers would key more entries than participants whoworked alone, they performed almost identically at eachbaseline level.

Participants' performance on the brainstorming taskwas analyzed to determine if brainstorming productivitywas influenced by group membership. In fact, the averagenumber of ideas produced by each group exceeded theaverage number of ideas produced by each aggregatemember or participant working alone. However, groupmembers produced fewer unique ideas per member thanparticipants in the other social context conditions. (Anaverage idea per member score was generated for eachgroup by dividing the total number of unique ideas gen-erated in the group by the number of its members. Like-wise, for each aggregate, an average idea per memberscore was created by consolidating the ideas generated byeach member of the aggregate, eliminating duplicateideas, and dividing by the number of members in the ag-gregate.) Despite popular beliefs that group brainstorm-ing encourages the production of more ideas per person,

numerous studies (e.g., Diehl & Stroebe, 1987; Paulus,Dzindolet, Poletes, & Camacho, 1993), including ourown, have found lower productivity among brainstorm-ing group members.

Support was found for our fifth hypothesis in which wepredicted that members of cohesive groups would expe-rience less stress than members of noninteracting aggre-gates and individuals who worked alone. Table 8 showsthat social context had a main effect on the amount ofstress experienced when participants were working on thebrainstorming task, F(2, 179) = 12.75, p < .001. Sup-porting our hypothesis, a post hoc Scheffe test revealedthat members of cohesive groups felt the least stressed,whereas aggregate members and participants workingalone did not differ in the amount of stress that they ex-perienced (see Table 7).

Moderating Effects

Social context did not appear to moderate the effects ofEPM on performance or stress. The interaction betweenmonitoring condition and social context was not signifi-cant when task performance (see Table 3) or either ofthe stress scales (see Table 8) was entered as a dependentvariable.

Discussion

Electronic Performance Monitoring

This study supports the view that EPM influences pro-ductivity in a manner that is consistent with the socialfacilitation framework. The social facilitation frameworkpredicts that simple-task performance will be enhancedby the presence of an audience or coactors, whereas com-plex-task performance will be debilitated by social pres-ence (Zajonc, 1965). Therefore, one would expect strongperformance among high-ability workers who are elec-tronically monitored and poorer performance amonglow-ability workers who are observed. In this study, ifbaseline performance is considered to be a measure ofdata-entry skill, then this is precisely the pattern of resultswe obtained. High-ability (high-baseline) participantsperformed faster on a simple task when they were moni-tored than when their work was not observed. In contrast,low-ability (low-baseline) participants actually keyedfewer entries when they were monitored. So, when moni-tored, faster workers get faster, and slower workers getslower. Monitoring actually appears to intensify perfor-mance in accordance with preexisting ability levels. Theimplication for the workplace is that EPM may lead toproductivity improvements among employees who haveattained a high level of task mastery. In contrast, EPM


may lead to performance debilitation among employeeswho are still learning the tasks that comprise their jobs.

Contrary to our expectation, participants who weremonitored at the work-group level did not show signs ofsocial loafing. Individually monitored and work-group-monitored participants performed almost identically.These results are surprising, because studies (cf. Harkins,1987) generally have found performance decline amongparticipants whose individual efforts cannot be identifiedand evaluated (as in work-group monitoring). Neverthe-less, in the current study, group-monitored participantsdid report feeling slightly less motivated than individu-ally monitored participants (although this difference wasnot statistically significant). Also, group-monitored par-ticipants considered the monitored task to be less impor-tant than did individually monitored participants. Thesefindings suggest that group-level monitoring may be lesseffective than individual-level monitoring in communi-cating to employees that the tasks they perform are im-portant (Larson & Callahan, 1990). It is possible thatover time these subjective perceptions may translate intoperformance decline.

This study provides evidence that EPM inducesfeelings of stress among employees. These findings areconsistent with results obtained in field surveys(DiTecco, Cwitco, Arsenault, & Andre, 1992; Gallatin,1989; Irving etal., 1986; Smith etal., 1992), case stud-ies (9to5, Working Women Education Fund, 1990),and laboratory experiments (Aiello & Shao, 1993;Aiello & Svec, 1993; Schleifer & Amick, 1989). More-over, the results in the current study suggest that moni-toring induces stress in a manner that is not completelyexplained by objective changes in job design and lossof social support. All participants were exposed to thesame job design elements. It may be that although jobdesign was objectively the same for monitored and non-monitored participants, subjective perceptions aboutjob demands and control differed. For example, per-haps monitored workers perceived higher demands forproductivity, even though all participants were askedto work as quickly and as accurately as possible. Stressalso may have originated from stronger feelings of eval-uation that were reported by monitored participants,lending support to evaluation-apprehension-basedmodels of social facilitation (Cottrell, 1972).

Monitoring at the work-group level appeared to pro-vide group members with some protection against stress.Their stress scores fell between those who were moni-tored at the individual level and those who were not mon-itored at all, a pattern that has been found in other labo-ratory studies as well (Aiello et al., 1991). This findingsuggests that one potentially effective strategy to reduce

monitoring-related stress may be to use work-group-levelobservation.

Our ability to make recommendations based on thesefindings is qualified, however, by the fact that this studyused only self-report measures to determine participants'stress levels. Self-report measures have been found to beproblematic (Burke, 1987), as they sometimes produceresults that are unrelated to physiological measures ofstress (cf. Fried, Rowland, & Ferris, 1984). Answers tostress-related items on questionnaires also may reflectstable dimensions of a respondent's personality morethan his or her response to a stressor. For example, peoplewho are dispositionally oriented toward negative affec-tivity show a general pattern of reporting more stress andphysical symptoms, even when no objective stressors arepresent (Watson, Pennebaker, & Folger, 1987).

In the current study, some evidence of a general re-sponse pattern is indicated by the significant correlationsthat were found between the data-entry stress scale andthe general climate scale and between the brainstormingstress scale and the general climate scale. Participantswho reported feeling more stressed on either task alsorated the overall climate of the experiment to be less pos-itive. In contrast, the correlation between the data-entryand the brainstorming stress scales was nonsignificant,suggesting that participants did differentiate between sit-uations they found to be stressful and those they did not.Given that several other studies (cf. Aiello & Shao, 1993;Smith et al., 1992), in addition to the current one, havefound that monitored workers are more stressed thannonmonitored workers, it can be assumed with some de-gree of confidence that EPM represents a workplacestressor.

Social Context

The results of this study provide some support for theview that subjective impressions of a task environmentare affected by relations among work peers. Participantswho worked alone or in an aggregate on the brainstorm-ing task reported feeling the most stressed, whereas mem-bers of cohesive groups reported feeling the least stressed.Limited evidence was found, however, to indicate that so-cial context also influences productivity. Aggregate mem-bers were not any more productive than participants whoworked alone. Also, although cohesive group memberswith low-baseline scores keyed more entries than aggre-gate members and participants working alone, high-base-line group members keyed fewer entries than their high-baseline aggregate and alone counterparts.

The pattern of stress found in this study suggests thathaving the opportunity to interact with coworkers maymake work on some tasks less threatening. Social support


systems have been shown to protect individuals againststress by providing additional resources to cope withstressors and by furnishing outlets for expressing tension(Cobb, 1976; Cohen & Wills, 1985; House etal., 1988).In the current study, group members received assistancefrom their coparticipants when they worked on the brain-storming task, perhaps helping to reduce the anxiety as-sociated with task performance. In contrast, aggregatemembers and participants who worked alone did not re-ceive help from their peers. Conversations among groupmembers were not recorded while they worked on thebrainstorming task; therefore, it is not known if groupmembers capitalized on this opportunity to express ten-sions and concerns.

If working in a cohesive group provided participantswith protection against stress, this benefit appears to havebeen transitory. Members of cohesive groups did not re-port feeling any less stress on the data-entry task thanaggregate members and participants who worked alone.The effect of social support was limited to the brain-storming task. This may have been because access togroup members was restricted during the data-entryphase of the study, whereas it was not during the brain-storming task. Group members also could not help oneanother complete the data-entry task as they could withthe brainstorming task. Group membership appeared toprovide protection against stress only in those circum-stances in which group members were accessible andcould provide concrete assistance.

Of course, it is possible that group members experi-enced less stress on the brainstorming task only, not be-cause of any social support benefit but because of the ex-perimental manipulation itself. To induce a sense of co-hesiveness among participants assigned to the groupcondition, group members were provided with positivefeedback regarding their performance on the brainstorm-ing task. In contrast, aggregate members and participantswho worked alone received no feedback. Perhaps thisfeedback led group members to feel less anxious abouttheir performance on the brainstorming task yet didnothing to diminish their worries about work on the data-entry task.

Our failure to obtain the hypothesized effects of socialcontext on productivity may be partially explained bylimitations in the experimental procedure. Participantsassigned to the alone condition never truly worked aloneduring the experiment. Although alone participantscould not be motivated by coactors working alongside ofthem, as was the case with aggregate and group members,their performance may have been partially facilitated bythe experimenter who remained in the laboratory at alltimes. Perhaps the effect of social context on productivitywould have been more pronounced had participants as-

signed to the alone condition truly worked in the absenceof any social stimulus.

The interaction observed between social context andbaseline performance may have resulted from the posi-tive feedback provided to group members on the brain-storming task. Baseline performance may be as much anindicant of general motivation level as it is a measure oftask ability. When less motivated (low-baseline) groupmembers were provided with positive feedback, they mayhave become more motivated to work on future tasks. Incontrast, external incentives may create motivation lossamong intrinsically motivated people (Deci & Ryan,1985). Therefore, providing positive feedback to highlymotivated (high-baseline) group members may have pre-cipitated their productivity decline.

Moderating Effects

Finally, we examined whether social context would mod-erate the effects of EPM on productivity and stress. We sus-pected that monitored members of cohesive groups wouldfeel less stress than monitored participants who were mem-bers of aggregates or who worked alone. Instead, no interac-tion between monitoring condition and social context wasobserved. This may have been because all participants, re-gardless of social context condition, were prevented from in-teracting with one another after the monitoring manipula-tion was introduced. Perhaps monitored group memberswould have reported feeling lower levels of stress than othermonitored participants if group members had been allowedto recongregate midway through working on the data-entrytask. That is, had monitored group members been providedwith an opportunity to obtain social support from one an-other after the stress of monitoring was introduced, then abuffering effect may have been observed. Nevertheless, sev-eral studies examining other workplace stressors also havenot obtained results that support the buffering hypothesis(Kobasa, 1982; Shinn et al., 1984). Social support as a mod-erator of work stress remains a hotly contested issue.

Conclusion

Additional research is necessary to explore the long-term effects of EPM. It was beyond the scope of this study,for example, to determine the cumulative effect of manydays, weeks, or years of being subjected to electronic sur-veillance. Might employees eventually adapt to the stressof being constantly observed, or will the effects reportedhere accumulate and render employees more vulnerableto a myriad of stress-related illnesses?

We expect that a host of other factors will also influ-ence the manner in which monitoring affects workers (cf.Aiello & Kolb, in press). For example, we would expectthat the existence of an oppressive and punitive organiza-


tional climate would exacerbate the stressful effects ofEPM. In contrast, if employees are involved early on inthe introduction of a monitoring system and they feelthat their input has been incorporated in the systemadopted, they may feel greater ownership of their workprocess and experience greater motivation and less stress.Similarly, if employees are permitted to share in the re-wards realized through increased productivity, they maycome to view EPM in a positive manner because thereare real stakes accruing to them under the system. Moreresearch is needed to document the characteristics of theorganizational climate that enhance or exacerbate theeffects of EPM. Information gained from such studiesmay help guide efforts to restructure organizations inways that help optimize productivity and satisfactionwhile minimizing stress.

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Appendix

Items and Factor Loadings for Subjective Scales

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Item and scale anchors DE BS

Data-entry stress*

When working on the data-entry task I felt:1 = stressed, 1 = not stressed. .83 .11

When working on the data-entry task I felt:1 = uptight, 1 = calm. .78 .05

What degree of stress did you experience while working on thedata-entry task? (reverse scored)1 = no stress, 7 = a great deal of stress. .11 .08

When working on the data-entry task I felt:1 = distressed, 1 = not distressed. .74 .02

When working on the data-entry task I felt:1 = frustrated, 1 = not frustrated. .69 .09

I would describe the climate/atmosphere for working on thetasks in this study as:1 = stressful, 1 = not stressful. .67 .20

How frustrating was it to work on the data-entry task?1 = very frustrating, 1 = not at all frustrating. .64 .07

How much pressure did you feel from your supervisor whileworking on the data-entry task?1 = quite a lot, 1 = none at all. .58 .02

To what extent did your supervisor affect your performance onthe data-entry task?1 = very much, 1 = not at all. .52 .06

Brainstorming stress6

When working on the brainstorming task I felt:1 = frustrated, 1 = not frustrated. .07 .85

When working on the brainstorming task I felt:1 = uptight, 1 = calm. .13 ,84

When working on the brainstorming task I felt:1 = stressed, 1 = not stressed. .12 .84

How frustrating was it to work on the brainstorming task?1 = very frustrating, 1 = not at all'frustrating. .02 .81

What degree of stress did you experience while working on thebrainstorming task? (reverse scored)1 = a great deal of stress, 7 = no stress. .11 .77

When working on the brainstorming task I felt:1 = distressed, 1 = not distressed. .14 .73

When working on the brainstorming task I felt (reversescored):1 = content, 1 = annoyed. .06 .62

How satisfied were you with your degree of creativity on thebrainstorming task? (reverse scored)1 = very satisfied,! = not at all satisfied. .07 .57

How satisfied were you with your performance on thebrainstorming task? (reverse scored)1 = very satisfied, 1 = not at all satisfied. .01 .57

How well did you feel you performed on the brainstormingtask? (reverse scored)1 = very well, 1 = not at all well. .08 .55

Note. DE = Data Entry Stress Scale; BS = Brainstorming Stress Scale.• Cronbach's a = .88. b Cronbach's a = .92.

Received April 9, 1993Revision received May 12, 1994

Accepted August 3, 1994

electronic performance monitoring and social context...

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