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Technology, credibility, and feedback use
ORGANIZATIONAL
BEHAVIOR
Technology,
AND HUMAN
DECISION
Credibility,
PROCESSES 44,
83-96 (1989)
and Feedback
Use
GREGORYB. NORTHCRAFTAND P. CHRISTOPHEREARLEY Department of Management and Policy, University of Arizona This paper extends the literature of feedback use through an empirical examination of two central issues in the use of feedback: feedback credibility and technology as a feedback source. In a laboratory study, 55 subjects received performance feedback from one of four sources (organization, supervisor, and self-generated with or without the aid of a computer) while participating in a stock market simulation. The results of repeated-measures MANOVAs demonstrated that self-generated feedback (with or without the use of a computer) significantly influenced credibility of feedback, strategy acquisition, and performance. There was no support for the contention that technology-based feedback sources foster “technomindlessness.” o 1989 Academic press, 1~.
Feedback is information received by an individual about his or her past behavior (Annett, 1969). Feedback has become an integral management tool because it is thought to serve both informational and motivational functions (Kopelman, 1986; Locke, Cartledge, & Koeppel, 1968). Feedback can provide information about the correctness, accuracy, and adequacy of work behaviors (Bourne, 1966). Presumably this means feedback encourages task strategy development, revision, and refinement, and thereby lays the groundwork for improved performance (Earley , 1988). Motivationally, feedback may be necessary for building in workers a sense of competence, accomplishment, and control (Bandura, 1977; Hackman & Oldham, 1976). In short, feedback is an important resource, both to individuals and the organizations in which they work (Ashford & Cummings, 1983). This paper extends the literature on feedback use through an empirical examination of two central issues in the use of feedback: feedback credibility and technology as a feedback source. After a thorough review of the research literature on feedback, Ilgen, Fisher, and Taylor (1979) identified feedback Source as a particularly critical determinant of feedback utilization. Previous work (e.g., Greller, 1980; Greller & Herold, 1975) has identified several primary sources of
Work on this paper was supported in part by a research grant (“Feedback and the Science of Employee Involvement”) from the Center for Microcontamination Control, University of Arizona. The authors thank Carolyn O’Reilly for her valuable assistance on this project. Requests for reprints should be addressed to Gregory B. Northcraft, Department of Management and Policy, University of Arizona, Tucson, AZ 85721. 83 0749-5978189$3.00 Copyright 8 1989 by Academic Press. Inc. All rights of reproduction in any fom reserved.
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feedback in organizations, including the organization, supervisors, coworkers, the task itself, and the individual. Herold, Liden, and Leatherwood (1987) have shown that differences among sources of feedback can be captured in three dimensions: consistency, usefulness, and amount of feedback. These dimensions, however, all refer to message value characteristics of feedback. Ilgen et al. (1979) suggest that feedback source also influences perceiver acceptance of the feedback message (for instance, the amount of credibility the individual puts in the feedback), and acceptance in turn influences the extent to which feedback is attended to and utilized, This contention is substantiated by the findings of Greller and Herold (1975) that people rely most for feedback upon sources psychologically close to themselves-where credibility should be high. One obvious means through which an individual’s psychological distance from a source of feedback may be decreased is through the use of self-generatedfeedback (e.g., Greller & Herold, 1975).In support of this contention, Ivancevich and McMahon (1982) showed in a field experiment that self-generatedfeedback led to superior ratings on five of seven performance and worker attitude measures.To date, however, no studies have identified the psychological mechanisms by which self-generated feedback enhances performance. While technology has been used extensively to automate manufacturing, technology (specifically, computers) also has the potential of serving a critical “informating” function-improving and increasing innovation by providing workers timely and comprehensive updates on work processes (Zuboff, 1985). The informating function is an important and expanding use of computers in organizations. The increased complexity and automation of work processes, and the increased miniaturization of many products, such as semiconductors and other microchips, are making workers increasingly dependent on technological sources of feedback. One field study in the semiconductor industry (Northcraft, 1986) has shown that the use of technological feedback sources in “high-tech” settings is, at best, poorly understood. To date only one study has looked at worker receptivity to and utilization of computers as a feedback source. Earley (1988) examined the impact of self- versus supervisorprovided performance feedback using a computer database as the ultimate source of the feedback information. Both the self and supervisor conditions derived their feedback from a common computer database. Self-generated feedback using a computer-tracking system led to higher performance and trust in the feedback than supervisor-provided feedback. Unfortunately, this study did not directly compare computer- and noncomputer-generated feedback. Weick (1985) has suggested that the use of technological sources of
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feedback (such as computers) poses two threats. First, technology psychologically distances workers from the source of performance information, thereby decreasing the likelihood of the information’s appropriate utilization. Second, technology allows the substitution of machine skills for intellectual involvement in work tasks, encouraging a sort of “technomindlessness” that may serve to decrease productivity and innovation. Interestingly, Ilgen et al. (1979) warn that individual differences often play an important role in feedback receptivity and use. Thus, familiarity or facility with computers may prove to be a key moderator of reactions to the computer as a feedback source. The study described in this paper examined the importance of technological sources of feedback and involvement in feedback generation to the appropriate utilization of performance feedback. Subjects received performance feedback while participating in a stock market simulation. Following from our discussion four hypotheses are proposed: (Hl) Involvement in the generation of feedback will enhance performance. This hypothesis posits a laboratory replication of the findings of Ivancevich and McMahon (1982). (H2) The relation between involvement in feedback generation and performance will be mediated by perceived credibility of the feedback and appropriate strategy acquisition. This hypothesis reflects the suggestions of Earley (1986), Ilgen et al. (1979), and Greller and Herold (1975) that feedback source influences perceived feedback credibility which in turn influences attention to and effective utilization of feedback. (H3) Credibility and psychological distance of feedback generated by an individual using a computer will be influenced by the individual’s experience with a computer (i.e., experienced computer users will perceive computer-generated feedback to be more credible and less psychologically distant than inexperienced computer users). This hypothesis reflects the concern of Ilgen et al. (1979) that individual differences play an important role in worker receptivity to feedback. (H4) As a result of (H3), involvement in generating feedback using a computer on the average will yield performance and strategy acquisition less than that of subjects involved directly in generating their own feedback but greater than that of subjects not involved in generating feedback. Subjects familiar with computers will see computer feedback as highly credible and will use it effectively to acquire appropriate strategies; subjects unfamiliar with computers will see computer feedback as less credible and will be less likely to use it effectively.
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METHOD Subjects
Fifty-five students from the MBA program at the University of Arizona (33 males, 22 females) participated as subjects. Participants were recruited from graduate courses in organizational behavior and volunteered to participate in partial fulfillment of a course requirement. The mean age of the subjects was approximately 31 years and their mean previous fulltime work experience was approximately 7 years (demographic information was drawn from course records). The subjects participated in the study during their first semester. Design and Experimental Task
The task consisted of a stock market simulation, buying and selling blocks of stock for five companies. Each subject began the simulation with $100,000in cash and used this cash to trade stock during six trading periods so as to increase the value of their portfolio. For all experimental conditions, each stock began the simulation at $100and randomly went up or down $10 in price during each round. In addition to the five stocks, there were five brokerage houses which provided trading recommendations. At the beginning of each transaction period, each brokerage house would provide a trading recommendation (“buy” or “sell”) for each of the stocks. Each brokerage house correctly and uniquely predicted the movement of one of the five stocks, and each house also perfectly incorrectly predicted the movement of one of the other stocks. Thus, the movement of each stock reliably was revealed by the recommendations of two brokerage houses each round; the other brokerage house predictions were correct only 50% of the time. In pilot tests of the simulation, subjects figured out which brokerage houses were useful in predicting particular stocks by the fourth or fifth round. Two types of performance feedback were available. First, feedback was available concerning each subject’s portfolio transactions and the effect of these transactions on the total cash-equivalent value of the subject’s portfolio (transaction feedback). Second, feedback also was available concerning the accuracy (number of correct predictions) of brokerage house predictions for each of the traded stocks (brokerage-house feedback). Regardless of the experimental condition, all subjects had access to these two forms of feedback information. Thus, the experimental conditions refer to differences in the Source of feedback and not the content of feedback. There were four feedback conditions: organization, supervisor, computer-generated, and self-generated. These experimental conditions are discussed in detail in the Procedure section.
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Dependent Measures
Credibility in the transaction feedback received was assessedusing four items each rated on a 5-point scale: (1) How much did you trust the information you received concerning your investment transactions (sales and purchases of stocks)? (2) How reliable was the investment transaction feedback you received concerning your buy/sell decisions? (3) To what extent was the investment transaction feedback you received useful for helping you to increase your profits on the simulation? (4) How accurate was the investment transaction information you received during the study? In addition, the psychological distance of the feedback source to the individual was assessedusing four items each rated on a 5-point scale: (1) How personal was the way you got investment transaction information? (2) How “close” to you was the source of the investment feedback? (3) How important was the investment transaction feedback you received as you made your investment decisions? (4) To what extent did you find the investment transaction feedback you received to be involving? Equivalent measures assessed subjects’ reactions concerning the brokerage house recommendations by substituting “brokerage house recommendation information” for “investment transaction information.” Item responses were collapsed into two composite factors for subsequent analyses: credibility of the transaction feedback versus credibility of the brokerage-house feedback. The reliabilities (Cronbach’s alpha) for these composite factors were .78 and .87 for the transaction and brokeragehouse composites, respectively. Task strategy acquisition was assessed for each of the six transaction periods by a simple scoring procedure. For each of the five stocks available a correct decision to buy or sell was assigned a value of 2 points, a decision not to buy or sell a particular stock received 1 point, and an incorrect choice to buy or sell received no points. Thus, if no transactions occurred, a subject would receive a score of 5 points. Scores greater than 5 indicate a profit-making strategy; scores lower than 5 indicate a profitlosing strategy. Finally, an individual’s task performance was represented by the amount of each subject’s cash-equivalent portfolio value at the end of the sixth trial. Procedure
Upon volunteering to participate each subject was assigned a time slot and provided instructions for getting to the site. Subjects were told that the study involved decisions concerning the purchase and sale of stock and that the most successful investors would receive a gift certificate for dinner for two at a local restaurant. Upon arrival at the site, each subject
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was placed in a small (8 by 7 ft) room with a table and a chair. Each room was equipped with a desk-top NCR personal computer. (Only in the Computer-generated condition were these machines used by subjects. In all other conditions the computer’s screen faced the wall and was turned off.) Next, each subject was given a short verbal overview of the simulation by the experimenter. Written instructions also were provided, as were the Round 1 “prices and recommendations” sheet and a stack of Transaction Request Forms. In the Organization and Supervisor conditions each subject received three pages of feedback at the beginning of each trading period including (1) a record of the previous round’s transactions and the effect of these transactions on the subject’s cash-equivalent portfolio value; (2) a table displaying the number of correct predictions of each stock by each brokerage house so far in the simulation; and (3) an update on the new prices and brokerage-house recommendations for each of the five stocks. In the Organization condition, this feedback was delivered impersonally. The information was slipped under the door of the room in which the subject was making his or her transaction decisions. In the Supervisor condition, the feedback was provided verbally and in print by a person who subjects were led to believe was evaluating their performance. (In fact, the feedback provider was a professor in the subject’s organizational behavior class.) The feedback simply was read off the sheets with no evaluative information added, and then the sheets were given to the subject. In the Computer-generated and Self-generated feedback conditions subjects received under the door at the beginning of each round only the update of the new prices and brokerage-house recommendations for each of the five traded stocks. Subjects were given responsibility for generating both (1) the record of the previous round’s transactions and the effect of these transactions on the subject’s cash-equivalent portfolio value and (2) a table displaying the number of correct predictions of each stock by each brokerage house so far in the simulation. In the Self-generated condition, these two types of feedback could be generated with detailed worksheets the subjects were provided and encouraged (but not required) to use. In the Computer-generated condition, each subject was provided a personal computer equipped with a user-friendly menu-driven portfolio analysis program (PAP). The PAP compiled information entered by the subject (portfolio transactions and price and trading recommendation updates) and could be queried for feedback. The three PAP feedback options available to subjects were (1) past transactions, (2) current portfolio contents (stock and cash held), and (3) a table of the number of correct predictions made by each brokerage house for each stock. For each round of the simulation, each subject decided which stocks to buy or sell and tilled out a Transaction Request Form. Only when this
TECHNOLOGY,
CREDIBILITY,
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FEEDBACK
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form was completed and slipped under the door would the subject receive the “prices and recommendations” sheet for the next round. After six rounds, the experimenter entered the subject’s room and provided the subject (1) a final feedback and “prices and recommendations” sheet, and (2) a short postexperiment questionnaire assessing subjects’ perceived importance of the various brokerage houses in making investment decisions, familiarity with computers and typical computer usage, and trust in the feedback received or generated as well as four items assessing the strength of the manipulations. Upon completion of the questionnaire, each subject was debriefed and at the conclusion of the study, dinner awards were provided for the top performer in each condition. RESULTS Manipulation checks. Responses to four questions (“How much investment feedback concerning your transactions did you receive from . . . the organization; a supervisor; yourself (on your own); a computer system” where I = none and 5 = a great deal) concerned with subjects’ perceived sources of feedback revealed that subjects in the Organization condition perceived themselves as receiving more feedback from the organization than other subjects (F(3, 50) = 7.80, p < .05; M = 3.82, 2.44, 2.00, 1.80 for the organization, supervisor, self, and computer-generated conditions, respectively); subjects in the Supervisor condition perceived themselves as receiving more feedback from a supervisor than other subjects (F(3,50) = 8.59, p < .05; M = 1.45, 2.82, 1JO, I. 18 for the organization, supervisor, self, and computer-generated conditions, respectively); subjects in the Computer-generated condition perceived themselves as receiving more feedback from the computer than other subjects (F(3, 50) = 38.43, p < .05; M = I .27, 1.OO,2.10, 4.23 for the organization, supervisor, self, and computer-generated conditions, respectively); and subjects in the Self-generated condition perceived themselves as receiving more feedback from themselves than did other subjects (F(3, 50) = 3.87, p < .05; M = 2.18, 3.20, 4.63, 3.31 for the organization, supervisor, self, and computer-generated conditions, respectively). Thus, the feedback condition manipulations were successful. Other analyses revealed no significant differences by condition for personal goals set, self-efficacy expectations concerning task competence, and interest in the task (all p’s > .20). Prior to testing for the effect of feedback condition on performance, the computer-generated feedback condition was separated into high and low computer literacy groups using a median split on responses to two items: (1) How often do you use computers during your typical day? (1 = not at all often, 5 = extremely often). (2) How much do you use computers in
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your classes? (1 = not much at all, 5 = a great deal). As moderated regression analysis failed to reveal significant differences between the two computer literacy groups on any of the dependent measures, Hypothesis 3 was not supported and the two Computer-generated groups were combined for all subsequent analyses. Performance. The means, standard deviations, and correlations for performance (final-round portfolio value), strategy acquisition, and credibility of transaction and brokerage-house recommendation feedback by feedback condition are presented in Table 1. As shown in Table 2, analysis of variance revealed a significant effect for feedback condition on performance. Post-hoc analyses (NewmanKeuls, p < .05) demonstrated that performance was significantly higher in the two conditions (Computer-generated and Self-generated) in which subjects were involved in generating feedback than in the two conditions (Organization and Supervisor) in which subjects were not involved in generating it. No other significant comparisons were found. Thus, Hypothesis 1 was supported and Hypothesis 4 was not supported. Intervening variables. Several variables intervening in the relation between feedback condition and performance were examined. The descriptive statistics for strategy acquisition and credibility (composite factors) of the transaction and brokerage-house feedback also are presented in Table 1. The strategy acquisition measure was analyzed using a repeatedmeasures MANOVA with transaction period as the repeated factor. Results of the analysis indicate significant effects for feedback condition and transaction period. No significant interaction was found. A post-hoc analysis (Newman-Keuls, p < .05) of the mean strategy-acquisition scores across transaction periods revealed a significant effect for feedback condition. Appropriate strategy acquisition was significantly lower in the Organization condition than in either condition in which subjects were involved in generating feedback (Computer-generated or Self-generated). Appropriate strategy acquisition did not differ significantly among the Supervisor, Computer-generated, and Self-generated conditions. As noted in Table 2, the impact of the feedback conditions on the two composite credibility factors was assessedusing two one-way ANOVAs. Results revealed significant effects for feedback condition on both composite credibility factors. Further post-hoc analyses (Newman-Keuls, p < .05) on the transaction credibility factor demonstrated that credibility of transaction feedback was significantly higher in the Self-generated feedback condition than in the other conditions, and significantly higher in the Computer-generated feedback condition than in the Organization condition. The Supervisor and Organization conditions did not significantly differ on this measure. Post-hoc analyses of the brokerage-house
TABLE 1
.71
.72
2.71
2.54
2.94
3.08
95.99 5.52
M
.78
.44
8.81 .82
SD
Supervisor
3.08
3.40
105.31 5.83
M
M
111.86 6.25 4.05 3.48
8.30 .73 .66 .85
.66
.45
10.61 .77
SD
Selfgenerated
SD
Computergenerated
5. Feedback conditionb a Expressed in units of 1,000. b Coded as 1, 2, 3, 4 for organization, supervisor, computer-generated, and self-generated, respectively. * p < .Ol.
8.59 1.07
97.25 4.83
1. Performance” 2. Strategy used 3. Credibility of transaction feedback 4. Credibility of brokerage feedback
SD
M
Variable
Organization
Feedback condition
-
1.
.56* -
.46*
-
-
46* .60*
57* .53*
Pearson correlation 2. 3. 4.
MEANS, STANDARD DEVIATIONS, AND PEARSON CORRELATIONS FOR PERFORMANCE, CREDIBILITY OF TRANSACTION AND BROKERAGE FEEDBACK, AND STRATEGY USED
-
46*
.71*
53* .50*
5.
W
Ii g F w
G g i;; F: 7 ce > z
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EARLEY
TABLE 2 ONE-WAY ANALYSES OF VARIANCE FOR PERFORMANCE, STRATEGY USED, AND CREDIBILITY OF TRANSACTION AND BROKERAGE FEEDBACK BY FEEDBACK CONDITION
Dependent variable
MS
F”
eta*
Performance Strategy usedb Feedback condition Performance period Interaction Credibility of transaction feedback (composite factor) Credibility of brokerage feedback (composite factor)
627,585,242&l
7.84**
.34
22.17 54.40 2.91
5.29** 24.33** 1.30
.15 .31 .04
3.86
9.76**
.50
1.67
2.75*
.24
a &are (3,50). b Multivariate analysis of variance using performance period (l-6) as the repeated factor. *p < .05. ** p < .Ol.
composite credibility factor demonstrated that credibility of brokeragehouse feedback was significantly higher in the Self-generated condition than in the other conditions. The Computer-generated, Supervisor, and Organization conditions did not differ. Tests of mediation. To test for mediating effects of strategy acquisition and credibility of feedback on the relation between feedback condition and performance, two sets of regression analyses were conducted using the procedures outlined by James & Brett (1984). The results of these analyses are presented in Table 3. In the first regression analyses (3A), the hypothesized intervening variables (strategy acquisition and feedback credibility) were entered before feedback condition. In the second set regression (3B), feedback condition was entered before the hypothesized intervening variables. The results indicate that feedback condition significantly predicted performance (AR* = .28) only if entered into the equation prior to the hypothesized intervening variables. When entered into the equation after the hypothesized intervening variables, feedback condition only explained an additional 1% of the variance (p > .20) of performance. However, the intervening variables account for an additional 16% of the variance (I, < .05) in performance when entered in the equation after feedback condition. Thus, the analysis provides support for the hypothesized mediating effects of strategy acquisition and credibility of feedback, as proposed in Hypothesis 2. Regression (3A) also demonstrates that strategy acquisition and credibility of transaction feedback have both shared and independent effects on performance.
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FEEDBACK
TABLE 3 HIERARCHICAL REGRESSION ANALYSESON PERFORMANCEUSINGSTRATEGYUSED, CREDIBILITY OF TRANSACTION AND BROKERAGE FEEDBACK, AND FEEDBACK CONDITION
Dependent variable
Variable entered
T
Step
R2-change
R2
(for beta)
(3A) Performance Strategy used Credibility of transaction Credibility of brokerage Feedback condition
1
.42
.42
2.29* 2.49*
2
.43
.Ol
.33 1.05
I 2
.27 .43
.27 .16
4.30** 2.05*
(3B) Performance Feedback condition Strategy used Credibility of transaction Credibility of brokerage
1.56 ____
-~
.33
* p < .05. **p < .Ol.
DISCUSSION A primary finding of this study is that self-generation of feedback is significantly linked to credibility of feedback and strategy acquisition and performance. In particular, if an individual is involved in the generation of performance feedback, the feedback generally seems psychologically “closer,” is more trusted, and is perceived as more useful. It does not seem surprising that more trustworthy information is perceived as more useful, or that more useful information is more likely to be attended to, resulting in superior strategy acquisition and performance. Because feedback credibility in this study was measured after performance, it might be argued that feedback source “directly” influenced performance (perhaps motivationally), and that performance in turn influenced feedback credibility (i.e., better performance creates more favorable impressions of feedback). While favorable performance well might enhance perceptions of feedback credibility, feedback credibility also remains a plausible explanation for feedback source effects on performance where feedback content has been controlled. An additional issue raised by the analyses concerns the direct effect of credibility of transaction feedback on performance. The indirect effect of credibility on performance through strategy acquisition seems relatively straightforward, as noted above. It is less clear how credibility of feed-
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back directly intluences performance. One plausible explanation for this additional effect is suggestedby Kopelman (1986) and Ilgen et al. (1979) who claim that feedback has a motivational impact in addition to its information impact. The additional “motivation” attributable to credibility of feedback may lead individuals to execute their task strategies more efficiently or effectively than the less motivated individuals. As Earley, Wojnaroski, and Prest (1987)note, the relation of task strategies to effort may be cyclical such that effort enhances strategy search and development which, in turn, directs the individual’s effort during task accomplishment. Thus, the direct path of credibility of feedback to performance probably reflects increased effort expended toward task accomplishment. Future research may want to explore further the relative positioning of credibility and strategy acquisition as mediators of the effects of feedback source on performance. Our results speak only indirectly to the possibility of Weick’s (1985) “technomindlessness.” Weick warns that an increasing reliance of human information processors on technological assistance will decrease awareness of critically diagnostic feedback and lead to an atrophying of human cognitive abilities-a state of technology-induced mindlessness. Our computer-generated feedback condition might have been contrasted with feedback received passively from a computer-for instance, if a specialist technician collected and entered the performance data and the worker subsequently used the computer only to display or retrieve (rather than generate) feedback. The favorable performance of subjects using the computer to generate feedback in this study may be a result of the benefits of self-generation of feedback overcoming the disadvantages of technology as a feedback source. As with any study, the present investigation is not without limitation. First, the laboratory nature of the study might have led subjects to be suspicious of our manipulations, thus explaining the superiority of the self- and computer-generated conditions over the supervisor and organization conditions. Although this alternative explanation cannot be discarded entirely the similarity of these findings to others (e.g., Earley, 1988)conducted in field settings suggeststhat our results are generalizable. The suspicion generated in our setting is analogous to the suspicion workers display of feedback provided to them from psychologically distant sources (Greller & Herold, 1975). A second limitation concerns the absence of a noninteractive computer-generated condition in which individuals passively receive feedback from a computer. It is unclear if individuals in the computer condition were using the feedback because of their inherent trust in computers or their involvement in its generation. Further, some of the beneficial impact of the computer-generated condition may be attributed to its novelty. Longitudinal work may provide the
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setting to resolve some of these issues. Finally, the measures of perceived feedback credibility incorporate both trust and usefulness dimensions although there are reasons to believe these constructs should be kept separate (Ilgen et al., 1979). Given that trust in feedback is highly related to its source and perceived usefulness is a characteristic derived from the feedback itself, future research should assess the independent contributions of these intervening variables. Interestingly, a feedback database that could be collected, organized, and made available to the worker automatically (i.e., completely by computer) is within reach of current technology. In such circumstances, allowing the worker choices about how to query the feedback database, or options in the way the feedback data are presented, could produce a valuable opportunity for involvement in the feedback generation process. Future research should be directed at better understanding what types of involvement in feedback generation are useful. A logical extension of our work would be to examine workers who derive their feedback from a computer source (e.g., production personnel). Although the present study has shown that computer-generated feedback is not inherently problematic, a clearer specification of the role of technology in providing feedback awaits the outcomes of future research. Clearly, computers have become a permanent presence in American business. The psychological ramifications of their use in providing feedback should be a major concern. Judging from the results of this study, the future of computers as a source of feedback appears encouraging. REFERENCES Annett. J. (1%9). Feedback and human behavior. Baltimore, MD: Penguin. Ashford, S. J., & Cummings, L. L. (1983). Feedback as an individual resource: Personal strategies of creating information. Organizational Behavior and Human Performance, 32, 370-398. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 191-215. Boume, L. E., Jr. (1966). Comments on Professor I. M. Bilodeau’s paper. In E. Bilodeau (Ed.), Acquisition of skill. New York: Academic Press. Earley, P. C. (1986). Trust, perceived importance of praise and criticism, and work performance: An examination of feedback in the United States and England. Journal of Management 12, 457-473. Earley, P. C. (1988). Computer-generated performance feedback in the magazinesubscription industry. Organizational Behavior and Human Decision Processes, 41, 50-64. Earley, P. C., Wojnaroski, P., & Prest, W. (1987). Task planning and energy expended: Exploration of how goals intluence performance. Journal of Applied Psychology 72, 107-l 14. Greller, M. M. (1980). Evaluation of feedback sources as a function of role and organizational level. Journal of Applied Psychology 65, 24-27.
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Greller, M. M., & Herold, D. M. (1975). Sources of feedback: A preliminary investigation. Organizational Behavior and Human Performance, 13, 244-256. Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16, 2X1-279. Herold, D. M., Liden, R. C., & Leatherwood, M. L. (1987). Using multiple attributes to assess performance feedback sources. Academy of Management Journal 30, 826-835. Ilgen, D. R., Fisher, C. D., & Taylor, M. S. (1979). Consequences of individual feedback on behavior in organizations. Journal of Applied Psychology 64, 349-371. Ivancevich, J. M., & McMahon, J. T. (1982). The effects of goal setting, external feedback, and self-generated feedback on outcome variables: A field experiment. Academy of Management Journal, 25, 359-372. James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation. Journal of Applied Psychology 69, 307-321. Kopelman, R. E. (1986). Managing productivity in organizations: A practical, peopleoriented perspective. New York: McGraw-Hill. Locke, E. A., Cartledge, N., & Koeppel, J. (1968). Motivational effects of knowledge of results: A goal-setting phenomenon. Psychological Bulletin, 70, 474-485. Northcraft, G. B. (1986). A manager’s guide to clean room operators. Microcontamination, 4, 52-56. Weick, K. E. (1985). Cosmos vs. chaos: Sense and nonsense in electronic contexts. Organizational Dynamics, 14, 50-65. Zuboff, S. (1985). Automate/informate: The two faces of intelligent technology. Organizational Dynamics, 14, 4-18. RECEIVED:
October 6, 1987
BEHAVIOR
Technology,
AND HUMAN
DECISION
Credibility,
PROCESSES 44,
83-96 (1989)
and Feedback
Use
GREGORYB. NORTHCRAFTAND P. CHRISTOPHEREARLEY Department of Management and Policy, University of Arizona This paper extends the literature of feedback use through an empirical examination of two central issues in the use of feedback: feedback credibility and technology as a feedback source. In a laboratory study, 55 subjects received performance feedback from one of four sources (organization, supervisor, and self-generated with or without the aid of a computer) while participating in a stock market simulation. The results of repeated-measures MANOVAs demonstrated that self-generated feedback (with or without the use of a computer) significantly influenced credibility of feedback, strategy acquisition, and performance. There was no support for the contention that technology-based feedback sources foster “technomindlessness.” o 1989 Academic press, 1~.
Feedback is information received by an individual about his or her past behavior (Annett, 1969). Feedback has become an integral management tool because it is thought to serve both informational and motivational functions (Kopelman, 1986; Locke, Cartledge, & Koeppel, 1968). Feedback can provide information about the correctness, accuracy, and adequacy of work behaviors (Bourne, 1966). Presumably this means feedback encourages task strategy development, revision, and refinement, and thereby lays the groundwork for improved performance (Earley , 1988). Motivationally, feedback may be necessary for building in workers a sense of competence, accomplishment, and control (Bandura, 1977; Hackman & Oldham, 1976). In short, feedback is an important resource, both to individuals and the organizations in which they work (Ashford & Cummings, 1983). This paper extends the literature on feedback use through an empirical examination of two central issues in the use of feedback: feedback credibility and technology as a feedback source. After a thorough review of the research literature on feedback, Ilgen, Fisher, and Taylor (1979) identified feedback Source as a particularly critical determinant of feedback utilization. Previous work (e.g., Greller, 1980; Greller & Herold, 1975) has identified several primary sources of
Work on this paper was supported in part by a research grant (“Feedback and the Science of Employee Involvement”) from the Center for Microcontamination Control, University of Arizona. The authors thank Carolyn O’Reilly for her valuable assistance on this project. Requests for reprints should be addressed to Gregory B. Northcraft, Department of Management and Policy, University of Arizona, Tucson, AZ 85721. 83 0749-5978189$3.00 Copyright 8 1989 by Academic Press. Inc. All rights of reproduction in any fom reserved.
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feedback in organizations, including the organization, supervisors, coworkers, the task itself, and the individual. Herold, Liden, and Leatherwood (1987) have shown that differences among sources of feedback can be captured in three dimensions: consistency, usefulness, and amount of feedback. These dimensions, however, all refer to message value characteristics of feedback. Ilgen et al. (1979) suggest that feedback source also influences perceiver acceptance of the feedback message (for instance, the amount of credibility the individual puts in the feedback), and acceptance in turn influences the extent to which feedback is attended to and utilized, This contention is substantiated by the findings of Greller and Herold (1975) that people rely most for feedback upon sources psychologically close to themselves-where credibility should be high. One obvious means through which an individual’s psychological distance from a source of feedback may be decreased is through the use of self-generatedfeedback (e.g., Greller & Herold, 1975).In support of this contention, Ivancevich and McMahon (1982) showed in a field experiment that self-generatedfeedback led to superior ratings on five of seven performance and worker attitude measures.To date, however, no studies have identified the psychological mechanisms by which self-generated feedback enhances performance. While technology has been used extensively to automate manufacturing, technology (specifically, computers) also has the potential of serving a critical “informating” function-improving and increasing innovation by providing workers timely and comprehensive updates on work processes (Zuboff, 1985). The informating function is an important and expanding use of computers in organizations. The increased complexity and automation of work processes, and the increased miniaturization of many products, such as semiconductors and other microchips, are making workers increasingly dependent on technological sources of feedback. One field study in the semiconductor industry (Northcraft, 1986) has shown that the use of technological feedback sources in “high-tech” settings is, at best, poorly understood. To date only one study has looked at worker receptivity to and utilization of computers as a feedback source. Earley (1988) examined the impact of self- versus supervisorprovided performance feedback using a computer database as the ultimate source of the feedback information. Both the self and supervisor conditions derived their feedback from a common computer database. Self-generated feedback using a computer-tracking system led to higher performance and trust in the feedback than supervisor-provided feedback. Unfortunately, this study did not directly compare computer- and noncomputer-generated feedback. Weick (1985) has suggested that the use of technological sources of
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feedback (such as computers) poses two threats. First, technology psychologically distances workers from the source of performance information, thereby decreasing the likelihood of the information’s appropriate utilization. Second, technology allows the substitution of machine skills for intellectual involvement in work tasks, encouraging a sort of “technomindlessness” that may serve to decrease productivity and innovation. Interestingly, Ilgen et al. (1979) warn that individual differences often play an important role in feedback receptivity and use. Thus, familiarity or facility with computers may prove to be a key moderator of reactions to the computer as a feedback source. The study described in this paper examined the importance of technological sources of feedback and involvement in feedback generation to the appropriate utilization of performance feedback. Subjects received performance feedback while participating in a stock market simulation. Following from our discussion four hypotheses are proposed: (Hl) Involvement in the generation of feedback will enhance performance. This hypothesis posits a laboratory replication of the findings of Ivancevich and McMahon (1982). (H2) The relation between involvement in feedback generation and performance will be mediated by perceived credibility of the feedback and appropriate strategy acquisition. This hypothesis reflects the suggestions of Earley (1986), Ilgen et al. (1979), and Greller and Herold (1975) that feedback source influences perceived feedback credibility which in turn influences attention to and effective utilization of feedback. (H3) Credibility and psychological distance of feedback generated by an individual using a computer will be influenced by the individual’s experience with a computer (i.e., experienced computer users will perceive computer-generated feedback to be more credible and less psychologically distant than inexperienced computer users). This hypothesis reflects the concern of Ilgen et al. (1979) that individual differences play an important role in worker receptivity to feedback. (H4) As a result of (H3), involvement in generating feedback using a computer on the average will yield performance and strategy acquisition less than that of subjects involved directly in generating their own feedback but greater than that of subjects not involved in generating feedback. Subjects familiar with computers will see computer feedback as highly credible and will use it effectively to acquire appropriate strategies; subjects unfamiliar with computers will see computer feedback as less credible and will be less likely to use it effectively.
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METHOD Subjects
Fifty-five students from the MBA program at the University of Arizona (33 males, 22 females) participated as subjects. Participants were recruited from graduate courses in organizational behavior and volunteered to participate in partial fulfillment of a course requirement. The mean age of the subjects was approximately 31 years and their mean previous fulltime work experience was approximately 7 years (demographic information was drawn from course records). The subjects participated in the study during their first semester. Design and Experimental Task
The task consisted of a stock market simulation, buying and selling blocks of stock for five companies. Each subject began the simulation with $100,000in cash and used this cash to trade stock during six trading periods so as to increase the value of their portfolio. For all experimental conditions, each stock began the simulation at $100and randomly went up or down $10 in price during each round. In addition to the five stocks, there were five brokerage houses which provided trading recommendations. At the beginning of each transaction period, each brokerage house would provide a trading recommendation (“buy” or “sell”) for each of the stocks. Each brokerage house correctly and uniquely predicted the movement of one of the five stocks, and each house also perfectly incorrectly predicted the movement of one of the other stocks. Thus, the movement of each stock reliably was revealed by the recommendations of two brokerage houses each round; the other brokerage house predictions were correct only 50% of the time. In pilot tests of the simulation, subjects figured out which brokerage houses were useful in predicting particular stocks by the fourth or fifth round. Two types of performance feedback were available. First, feedback was available concerning each subject’s portfolio transactions and the effect of these transactions on the total cash-equivalent value of the subject’s portfolio (transaction feedback). Second, feedback also was available concerning the accuracy (number of correct predictions) of brokerage house predictions for each of the traded stocks (brokerage-house feedback). Regardless of the experimental condition, all subjects had access to these two forms of feedback information. Thus, the experimental conditions refer to differences in the Source of feedback and not the content of feedback. There were four feedback conditions: organization, supervisor, computer-generated, and self-generated. These experimental conditions are discussed in detail in the Procedure section.
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Dependent Measures
Credibility in the transaction feedback received was assessedusing four items each rated on a 5-point scale: (1) How much did you trust the information you received concerning your investment transactions (sales and purchases of stocks)? (2) How reliable was the investment transaction feedback you received concerning your buy/sell decisions? (3) To what extent was the investment transaction feedback you received useful for helping you to increase your profits on the simulation? (4) How accurate was the investment transaction information you received during the study? In addition, the psychological distance of the feedback source to the individual was assessedusing four items each rated on a 5-point scale: (1) How personal was the way you got investment transaction information? (2) How “close” to you was the source of the investment feedback? (3) How important was the investment transaction feedback you received as you made your investment decisions? (4) To what extent did you find the investment transaction feedback you received to be involving? Equivalent measures assessed subjects’ reactions concerning the brokerage house recommendations by substituting “brokerage house recommendation information” for “investment transaction information.” Item responses were collapsed into two composite factors for subsequent analyses: credibility of the transaction feedback versus credibility of the brokerage-house feedback. The reliabilities (Cronbach’s alpha) for these composite factors were .78 and .87 for the transaction and brokeragehouse composites, respectively. Task strategy acquisition was assessed for each of the six transaction periods by a simple scoring procedure. For each of the five stocks available a correct decision to buy or sell was assigned a value of 2 points, a decision not to buy or sell a particular stock received 1 point, and an incorrect choice to buy or sell received no points. Thus, if no transactions occurred, a subject would receive a score of 5 points. Scores greater than 5 indicate a profit-making strategy; scores lower than 5 indicate a profitlosing strategy. Finally, an individual’s task performance was represented by the amount of each subject’s cash-equivalent portfolio value at the end of the sixth trial. Procedure
Upon volunteering to participate each subject was assigned a time slot and provided instructions for getting to the site. Subjects were told that the study involved decisions concerning the purchase and sale of stock and that the most successful investors would receive a gift certificate for dinner for two at a local restaurant. Upon arrival at the site, each subject
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was placed in a small (8 by 7 ft) room with a table and a chair. Each room was equipped with a desk-top NCR personal computer. (Only in the Computer-generated condition were these machines used by subjects. In all other conditions the computer’s screen faced the wall and was turned off.) Next, each subject was given a short verbal overview of the simulation by the experimenter. Written instructions also were provided, as were the Round 1 “prices and recommendations” sheet and a stack of Transaction Request Forms. In the Organization and Supervisor conditions each subject received three pages of feedback at the beginning of each trading period including (1) a record of the previous round’s transactions and the effect of these transactions on the subject’s cash-equivalent portfolio value; (2) a table displaying the number of correct predictions of each stock by each brokerage house so far in the simulation; and (3) an update on the new prices and brokerage-house recommendations for each of the five stocks. In the Organization condition, this feedback was delivered impersonally. The information was slipped under the door of the room in which the subject was making his or her transaction decisions. In the Supervisor condition, the feedback was provided verbally and in print by a person who subjects were led to believe was evaluating their performance. (In fact, the feedback provider was a professor in the subject’s organizational behavior class.) The feedback simply was read off the sheets with no evaluative information added, and then the sheets were given to the subject. In the Computer-generated and Self-generated feedback conditions subjects received under the door at the beginning of each round only the update of the new prices and brokerage-house recommendations for each of the five traded stocks. Subjects were given responsibility for generating both (1) the record of the previous round’s transactions and the effect of these transactions on the subject’s cash-equivalent portfolio value and (2) a table displaying the number of correct predictions of each stock by each brokerage house so far in the simulation. In the Self-generated condition, these two types of feedback could be generated with detailed worksheets the subjects were provided and encouraged (but not required) to use. In the Computer-generated condition, each subject was provided a personal computer equipped with a user-friendly menu-driven portfolio analysis program (PAP). The PAP compiled information entered by the subject (portfolio transactions and price and trading recommendation updates) and could be queried for feedback. The three PAP feedback options available to subjects were (1) past transactions, (2) current portfolio contents (stock and cash held), and (3) a table of the number of correct predictions made by each brokerage house for each stock. For each round of the simulation, each subject decided which stocks to buy or sell and tilled out a Transaction Request Form. Only when this
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form was completed and slipped under the door would the subject receive the “prices and recommendations” sheet for the next round. After six rounds, the experimenter entered the subject’s room and provided the subject (1) a final feedback and “prices and recommendations” sheet, and (2) a short postexperiment questionnaire assessing subjects’ perceived importance of the various brokerage houses in making investment decisions, familiarity with computers and typical computer usage, and trust in the feedback received or generated as well as four items assessing the strength of the manipulations. Upon completion of the questionnaire, each subject was debriefed and at the conclusion of the study, dinner awards were provided for the top performer in each condition. RESULTS Manipulation checks. Responses to four questions (“How much investment feedback concerning your transactions did you receive from . . . the organization; a supervisor; yourself (on your own); a computer system” where I = none and 5 = a great deal) concerned with subjects’ perceived sources of feedback revealed that subjects in the Organization condition perceived themselves as receiving more feedback from the organization than other subjects (F(3, 50) = 7.80, p < .05; M = 3.82, 2.44, 2.00, 1.80 for the organization, supervisor, self, and computer-generated conditions, respectively); subjects in the Supervisor condition perceived themselves as receiving more feedback from a supervisor than other subjects (F(3,50) = 8.59, p < .05; M = 1.45, 2.82, 1JO, I. 18 for the organization, supervisor, self, and computer-generated conditions, respectively); subjects in the Computer-generated condition perceived themselves as receiving more feedback from the computer than other subjects (F(3, 50) = 38.43, p < .05; M = I .27, 1.OO,2.10, 4.23 for the organization, supervisor, self, and computer-generated conditions, respectively); and subjects in the Self-generated condition perceived themselves as receiving more feedback from themselves than did other subjects (F(3, 50) = 3.87, p < .05; M = 2.18, 3.20, 4.63, 3.31 for the organization, supervisor, self, and computer-generated conditions, respectively). Thus, the feedback condition manipulations were successful. Other analyses revealed no significant differences by condition for personal goals set, self-efficacy expectations concerning task competence, and interest in the task (all p’s > .20). Prior to testing for the effect of feedback condition on performance, the computer-generated feedback condition was separated into high and low computer literacy groups using a median split on responses to two items: (1) How often do you use computers during your typical day? (1 = not at all often, 5 = extremely often). (2) How much do you use computers in
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your classes? (1 = not much at all, 5 = a great deal). As moderated regression analysis failed to reveal significant differences between the two computer literacy groups on any of the dependent measures, Hypothesis 3 was not supported and the two Computer-generated groups were combined for all subsequent analyses. Performance. The means, standard deviations, and correlations for performance (final-round portfolio value), strategy acquisition, and credibility of transaction and brokerage-house recommendation feedback by feedback condition are presented in Table 1. As shown in Table 2, analysis of variance revealed a significant effect for feedback condition on performance. Post-hoc analyses (NewmanKeuls, p < .05) demonstrated that performance was significantly higher in the two conditions (Computer-generated and Self-generated) in which subjects were involved in generating feedback than in the two conditions (Organization and Supervisor) in which subjects were not involved in generating it. No other significant comparisons were found. Thus, Hypothesis 1 was supported and Hypothesis 4 was not supported. Intervening variables. Several variables intervening in the relation between feedback condition and performance were examined. The descriptive statistics for strategy acquisition and credibility (composite factors) of the transaction and brokerage-house feedback also are presented in Table 1. The strategy acquisition measure was analyzed using a repeatedmeasures MANOVA with transaction period as the repeated factor. Results of the analysis indicate significant effects for feedback condition and transaction period. No significant interaction was found. A post-hoc analysis (Newman-Keuls, p < .05) of the mean strategy-acquisition scores across transaction periods revealed a significant effect for feedback condition. Appropriate strategy acquisition was significantly lower in the Organization condition than in either condition in which subjects were involved in generating feedback (Computer-generated or Self-generated). Appropriate strategy acquisition did not differ significantly among the Supervisor, Computer-generated, and Self-generated conditions. As noted in Table 2, the impact of the feedback conditions on the two composite credibility factors was assessedusing two one-way ANOVAs. Results revealed significant effects for feedback condition on both composite credibility factors. Further post-hoc analyses (Newman-Keuls, p < .05) on the transaction credibility factor demonstrated that credibility of transaction feedback was significantly higher in the Self-generated feedback condition than in the other conditions, and significantly higher in the Computer-generated feedback condition than in the Organization condition. The Supervisor and Organization conditions did not significantly differ on this measure. Post-hoc analyses of the brokerage-house
TABLE 1
.71
.72
2.71
2.54
2.94
3.08
95.99 5.52
M
.78
.44
8.81 .82
SD
Supervisor
3.08
3.40
105.31 5.83
M
M
111.86 6.25 4.05 3.48
8.30 .73 .66 .85
.66
.45
10.61 .77
SD
Selfgenerated
SD
Computergenerated
5. Feedback conditionb a Expressed in units of 1,000. b Coded as 1, 2, 3, 4 for organization, supervisor, computer-generated, and self-generated, respectively. * p < .Ol.
8.59 1.07
97.25 4.83
1. Performance” 2. Strategy used 3. Credibility of transaction feedback 4. Credibility of brokerage feedback
SD
M
Variable
Organization
Feedback condition
-
1.
.56* -
.46*
-
-
46* .60*
57* .53*
Pearson correlation 2. 3. 4.
MEANS, STANDARD DEVIATIONS, AND PEARSON CORRELATIONS FOR PERFORMANCE, CREDIBILITY OF TRANSACTION AND BROKERAGE FEEDBACK, AND STRATEGY USED
-
46*
.71*
53* .50*
5.
W
Ii g F w
G g i;; F: 7 ce > z
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TABLE 2 ONE-WAY ANALYSES OF VARIANCE FOR PERFORMANCE, STRATEGY USED, AND CREDIBILITY OF TRANSACTION AND BROKERAGE FEEDBACK BY FEEDBACK CONDITION
Dependent variable
MS
F”
eta*
Performance Strategy usedb Feedback condition Performance period Interaction Credibility of transaction feedback (composite factor) Credibility of brokerage feedback (composite factor)
627,585,242&l
7.84**
.34
22.17 54.40 2.91
5.29** 24.33** 1.30
.15 .31 .04
3.86
9.76**
.50
1.67
2.75*
.24
a &are (3,50). b Multivariate analysis of variance using performance period (l-6) as the repeated factor. *p < .05. ** p < .Ol.
composite credibility factor demonstrated that credibility of brokeragehouse feedback was significantly higher in the Self-generated condition than in the other conditions. The Computer-generated, Supervisor, and Organization conditions did not differ. Tests of mediation. To test for mediating effects of strategy acquisition and credibility of feedback on the relation between feedback condition and performance, two sets of regression analyses were conducted using the procedures outlined by James & Brett (1984). The results of these analyses are presented in Table 3. In the first regression analyses (3A), the hypothesized intervening variables (strategy acquisition and feedback credibility) were entered before feedback condition. In the second set regression (3B), feedback condition was entered before the hypothesized intervening variables. The results indicate that feedback condition significantly predicted performance (AR* = .28) only if entered into the equation prior to the hypothesized intervening variables. When entered into the equation after the hypothesized intervening variables, feedback condition only explained an additional 1% of the variance (p > .20) of performance. However, the intervening variables account for an additional 16% of the variance (I, < .05) in performance when entered in the equation after feedback condition. Thus, the analysis provides support for the hypothesized mediating effects of strategy acquisition and credibility of feedback, as proposed in Hypothesis 2. Regression (3A) also demonstrates that strategy acquisition and credibility of transaction feedback have both shared and independent effects on performance.
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FEEDBACK
TABLE 3 HIERARCHICAL REGRESSION ANALYSESON PERFORMANCEUSINGSTRATEGYUSED, CREDIBILITY OF TRANSACTION AND BROKERAGE FEEDBACK, AND FEEDBACK CONDITION
Dependent variable
Variable entered
T
Step
R2-change
R2
(for beta)
(3A) Performance Strategy used Credibility of transaction Credibility of brokerage Feedback condition
1
.42
.42
2.29* 2.49*
2
.43
.Ol
.33 1.05
I 2
.27 .43
.27 .16
4.30** 2.05*
(3B) Performance Feedback condition Strategy used Credibility of transaction Credibility of brokerage
1.56 ____
-~
.33
* p < .05. **p < .Ol.
DISCUSSION A primary finding of this study is that self-generation of feedback is significantly linked to credibility of feedback and strategy acquisition and performance. In particular, if an individual is involved in the generation of performance feedback, the feedback generally seems psychologically “closer,” is more trusted, and is perceived as more useful. It does not seem surprising that more trustworthy information is perceived as more useful, or that more useful information is more likely to be attended to, resulting in superior strategy acquisition and performance. Because feedback credibility in this study was measured after performance, it might be argued that feedback source “directly” influenced performance (perhaps motivationally), and that performance in turn influenced feedback credibility (i.e., better performance creates more favorable impressions of feedback). While favorable performance well might enhance perceptions of feedback credibility, feedback credibility also remains a plausible explanation for feedback source effects on performance where feedback content has been controlled. An additional issue raised by the analyses concerns the direct effect of credibility of transaction feedback on performance. The indirect effect of credibility on performance through strategy acquisition seems relatively straightforward, as noted above. It is less clear how credibility of feed-
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back directly intluences performance. One plausible explanation for this additional effect is suggestedby Kopelman (1986) and Ilgen et al. (1979) who claim that feedback has a motivational impact in addition to its information impact. The additional “motivation” attributable to credibility of feedback may lead individuals to execute their task strategies more efficiently or effectively than the less motivated individuals. As Earley, Wojnaroski, and Prest (1987)note, the relation of task strategies to effort may be cyclical such that effort enhances strategy search and development which, in turn, directs the individual’s effort during task accomplishment. Thus, the direct path of credibility of feedback to performance probably reflects increased effort expended toward task accomplishment. Future research may want to explore further the relative positioning of credibility and strategy acquisition as mediators of the effects of feedback source on performance. Our results speak only indirectly to the possibility of Weick’s (1985) “technomindlessness.” Weick warns that an increasing reliance of human information processors on technological assistance will decrease awareness of critically diagnostic feedback and lead to an atrophying of human cognitive abilities-a state of technology-induced mindlessness. Our computer-generated feedback condition might have been contrasted with feedback received passively from a computer-for instance, if a specialist technician collected and entered the performance data and the worker subsequently used the computer only to display or retrieve (rather than generate) feedback. The favorable performance of subjects using the computer to generate feedback in this study may be a result of the benefits of self-generation of feedback overcoming the disadvantages of technology as a feedback source. As with any study, the present investigation is not without limitation. First, the laboratory nature of the study might have led subjects to be suspicious of our manipulations, thus explaining the superiority of the self- and computer-generated conditions over the supervisor and organization conditions. Although this alternative explanation cannot be discarded entirely the similarity of these findings to others (e.g., Earley, 1988)conducted in field settings suggeststhat our results are generalizable. The suspicion generated in our setting is analogous to the suspicion workers display of feedback provided to them from psychologically distant sources (Greller & Herold, 1975). A second limitation concerns the absence of a noninteractive computer-generated condition in which individuals passively receive feedback from a computer. It is unclear if individuals in the computer condition were using the feedback because of their inherent trust in computers or their involvement in its generation. Further, some of the beneficial impact of the computer-generated condition may be attributed to its novelty. Longitudinal work may provide the
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setting to resolve some of these issues. Finally, the measures of perceived feedback credibility incorporate both trust and usefulness dimensions although there are reasons to believe these constructs should be kept separate (Ilgen et al., 1979). Given that trust in feedback is highly related to its source and perceived usefulness is a characteristic derived from the feedback itself, future research should assess the independent contributions of these intervening variables. Interestingly, a feedback database that could be collected, organized, and made available to the worker automatically (i.e., completely by computer) is within reach of current technology. In such circumstances, allowing the worker choices about how to query the feedback database, or options in the way the feedback data are presented, could produce a valuable opportunity for involvement in the feedback generation process. Future research should be directed at better understanding what types of involvement in feedback generation are useful. A logical extension of our work would be to examine workers who derive their feedback from a computer source (e.g., production personnel). Although the present study has shown that computer-generated feedback is not inherently problematic, a clearer specification of the role of technology in providing feedback awaits the outcomes of future research. Clearly, computers have become a permanent presence in American business. The psychological ramifications of their use in providing feedback should be a major concern. Judging from the results of this study, the future of computers as a source of feedback appears encouraging. REFERENCES Annett. J. (1%9). Feedback and human behavior. Baltimore, MD: Penguin. Ashford, S. J., & Cummings, L. L. (1983). Feedback as an individual resource: Personal strategies of creating information. Organizational Behavior and Human Performance, 32, 370-398. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 191-215. Boume, L. E., Jr. (1966). Comments on Professor I. M. Bilodeau’s paper. In E. Bilodeau (Ed.), Acquisition of skill. New York: Academic Press. Earley, P. C. (1986). Trust, perceived importance of praise and criticism, and work performance: An examination of feedback in the United States and England. Journal of Management 12, 457-473. Earley, P. C. (1988). Computer-generated performance feedback in the magazinesubscription industry. Organizational Behavior and Human Decision Processes, 41, 50-64. Earley, P. C., Wojnaroski, P., & Prest, W. (1987). Task planning and energy expended: Exploration of how goals intluence performance. Journal of Applied Psychology 72, 107-l 14. Greller, M. M. (1980). Evaluation of feedback sources as a function of role and organizational level. Journal of Applied Psychology 65, 24-27.
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Greller, M. M., & Herold, D. M. (1975). Sources of feedback: A preliminary investigation. Organizational Behavior and Human Performance, 13, 244-256. Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16, 2X1-279. Herold, D. M., Liden, R. C., & Leatherwood, M. L. (1987). Using multiple attributes to assess performance feedback sources. Academy of Management Journal 30, 826-835. Ilgen, D. R., Fisher, C. D., & Taylor, M. S. (1979). Consequences of individual feedback on behavior in organizations. Journal of Applied Psychology 64, 349-371. Ivancevich, J. M., & McMahon, J. T. (1982). The effects of goal setting, external feedback, and self-generated feedback on outcome variables: A field experiment. Academy of Management Journal, 25, 359-372. James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation. Journal of Applied Psychology 69, 307-321. Kopelman, R. E. (1986). Managing productivity in organizations: A practical, peopleoriented perspective. New York: McGraw-Hill. Locke, E. A., Cartledge, N., & Koeppel, J. (1968). Motivational effects of knowledge of results: A goal-setting phenomenon. Psychological Bulletin, 70, 474-485. Northcraft, G. B. (1986). A manager’s guide to clean room operators. Microcontamination, 4, 52-56. Weick, K. E. (1985). Cosmos vs. chaos: Sense and nonsense in electronic contexts. Organizational Dynamics, 14, 50-65. Zuboff, S. (1985). Automate/informate: The two faces of intelligent technology. Organizational Dynamics, 14, 4-18. RECEIVED:
October 6, 1987