Feedback specificity, information processing, and transfer of training

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Feedback specificity, information processing, and transfer of training

Jodi S. Goodman a,⇑, Robert E. Wood b, Zheng Chen c,1

a University of Connecticut, School of Business, Department of Management, 2100 Hillside Road, Unit 1041, Storrs, CT 06269-1041, United Statesb Melbourne Business School, University of Melbourne, 200 Leicester Street, Carlton, Vic 3053, Australiac University of South Florida St. Petersburg, College of Business, 140 7th Avenue South, PNM 104 A, St. Petersburg, FL 33701, United States

a r t i c l e i n f o

Article history:Received 11 December 2009Accepted 4 January 2011Available online 3 February 2011Accepted by Paul Levy

Keywords:FeedbackTrainingLearningTransferTransfer of trainingInformation processingLearning processes

a b s t r a c t

This study examines the effects of feedback specificity on transfer of training and the mechanismsthrough which feedback can enhance or inhibit transfer. We used concurrent verbal protocol methodol-ogy to elicit and operationalize the explicit information processing activities used by 48 trainees perform-ing the Furniture Factory computer simulation. We hypothesized and found support for a moderatedmediation model. Increasing feedback specificity influenced the exposure trainees had to different taskconditions and negatively affected their levels of explicit information processing. In turn, explicit infor-mation processes and levels of exposure to different task conditions interacted to impact transfer of train-ing. Those who received less specific feedback relied more heavily on explicit information processing andhad more exposure to the challenging aspects of the task than those who received more specific feedback,which differentially affected what they learned about the task. We discuss how feedback specificity andexposure to different task conditions may prime different learning processes.

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Introduction

Kluger and DeNisi’s (1996) meta-analytic review of 100 years offeedback research provided a watershed in that it challenged ac-cepted wisdom regarding the effects of feedback and identifiedlimitations in the research conducted until their review. Contraryto the widely accepted view that feedback is both necessary andbeneficial, they reported that the observed effects of feedback wereabout equally likely to have positive, negative, or no effect on per-formance. They also pointed to the lack of studies examining thecausal mechanisms through which feedback influences behaviorand performance. Despite its extensive research history, feedbackresearch also has been criticized for its lack of valid, cumulativefindings (Annett, 1969; Ilgen, Fisher, & Taylor, 1979; Kluger & DeNisi,1996) and its many invalid textbook prescriptions (Goodman,Wood, & Hendrickx, 2004).

In response to these criticisms and to increase the field’s under-standing of feedback effects, Goodman and Wood (Goodman,1998; Goodman & Wood, 2004, 2009; Goodman et al., 2004) havebuilt a program of research around the impacts of different feed-back properties on training performance and transfer of training,as well as around the mechanisms through which feedback can en-hance or inhibit learning and subsequent transfer. Their work is

based on motor and cognitive learning literatures that emphasizethe guidance function of feedback and suggest that frequent,immediate feedback can provide too much guidance during train-ing (Christina & Bjork, 1991). Consistent with the guidance hypoth-esis, the results of Goodman and Wood’s studies show that morespecific feedback can benefit training performance yet underminethe learning required for transfer of training, particularly learningthat requires performers to manage more challenging aspects ofa task. As feedback specificity increases, more detailed informationis provided on performers’ actions and the locations of their errors(Annett, 1969; Baron, 1988; Goldstein, Emanuel, & Howell, 1968;Payne & Hauty, 1955; Wentling, 1973). Specific feedback may alsoprovide detailed information on corrective actions. For example,less specific feedback may inform performers only of their perfor-mance levels or simply that they made errors, whereas high spec-ificity feedback may also identify which actions were correct andincorrect, which actions caused specific errors, and what the cor-rect responses are.

While Goodman and Wood focused on guidance as provided byfeedback, guidance can be delivered through many different modesin training and work settings. A parallel body of work, begun byFrese and his colleagues (Dormann & Frese, 1994; Frese & Altmann,1989) has examined the impacts of the levels of guidance providedby different types of error training (e.g., Gardner & Wood, 2009;Gully, Payne, Koles, & Whiteman, 2002; Nordstrom, Wendland, &Williams, 1998). While the manipulations differ across error train-ing studies, error management training is designed to provide alow level of guidance, and error avoidance training is typically

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⇑ Corresponding author. Fax: +1 860 486 6415.E-mail addresses: [email protected] (J.S. Goodman), R.Wood@

mbs.edu (R.E. Wood), [email protected] (Z. Chen).1 Fax: +1 727 873 4571.

Organizational Behavior and Human Decision Processes 115 (2011) 253–267

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designed to provide a high level of guidance. Similar results havebeen found in error training and feedback research, despite differ-ences in how guidance is operationalized. Compared to low guid-ance, providing high guidance during training has been found tohave positive or no effects on training performance and negativeeffects on transfer of training for more challenging tasks that re-quire adaptive transfer of knowledge (Keith & Frese, 2008).

The seemingly contradictory effects of high guidance specificfeedback on training performance and transfer of training are par-tially explained by the impacts of feedback specificity on the extentto which performers spontaneously engage in exploratory behav-ior (Goodman et al., 2004) and are exposed to different task condi-tions (Goodman & Wood, 2004, 2009). Exploration and exposure todifferent task conditions are two distinct but related causal path-ways that influence trainees’ exposure to information during train-ing. However, these pathways do not address what traineesactually do with the information to which they are exposed. Thisis the subject of the current study.

The current study focuses on the learning processes that involveexplicit information processing activities such as planning andevaluating actions (Berardi-Coletta, Buyer, Dominowski, & Rellin-ger, 1995; Keith & Frese, 2005), causal reasoning (Isenberg,1986), and hypothesis testing (Burns & Vollmeyer, 2002; Van derLiden, Frese, & Sonnentag, 2003; Van der Linden, Sonnentag, Frese,& van Dyck, 2001). We build on previous research by examiningexplicit information processing as a causal mechanism that helpsexplain what performers do with the information to which theyare exposed during training, and we provide the first direct testof the popular guidance hypothesis claim that high-guidance feed-back undermines the information processing needed for transfer(Christina & Bjork, 1991). Extant research has yet to test the asser-tion that feedback decreases information processing, which is themost proximal determinant of learning. In addition, the theoreticalrationale for the guidance hypothesis assertion has not been suffi-ciently developed. We use the Dual Space Theory of problem solv-ing (Dunbar, 1995; Klahr & Dunbar, 1988; Simon & Lea, 1974) todevelop arguments to explain how feedback specificity impacts ex-plicit information processing activities and how explicit informa-tion processes contribute to problem solving, learning, andtransfer.

We also further explore Goodman and Wood’s (2004, 2009)ideas about the effects of differences in the guidance provided bylow and high specificity feedback on the extent to which traineesexperience different task conditions. These authors (Goodman &Wood, 2004, 2009) found that trainees’ feedback-induced explor-atory activities resulted in different levels of exposure to unfavor-able conditions, in which trainees experienced the morechallenging aspects of the task, and favorable conditions, in whichtrainees experienced the more straightforward aspects of the task.The rules for correct responses varied across the unfavorable andfavorable task conditions. The rules took the form, ‘‘If this situation,then this action is appropriate.’’ Those who received highly specificfeedback tended to follow the guidance offered by the feedbackand learned how to manage under favorable task conditions, butdeveloped less mastery of the rules for managing unfavorable taskconditions. Alternatively, those who received low specificity feed-back developed greater mastery of the rules for managing unfavor-able task conditions, but acquired less skill for managing favorabletask conditions. In the current study we explore how the exposureto different task conditions impacts and interacts with explicitinformation processes to affect transfer of training. We argue thatexplicit information processing activities are more vital for transferthe greater the exposure to unfavorable task conditions duringtraining.

The contributions of the current study can be summarized asfollows. First, we elaborate the theoretical reasoning for and con-

duct the first direct test of the guidance hypothesis assertion thathigh-guidance feedback decreases the information processingneeded for transfer of training (Christina & Bjork, 1991). Second,we extend the cumulative body of research on how feedback af-fects performance and learning (Goodman & Wood, 2004, 2009;Goodman et al., 2004) to include the effects of feedback specificityon the explicit processing of the information that trainees acquirethrough exploratory behavior and exposure to different task condi-tions during training. Finally, and more generally, our study re-sponds to the call for the study of training conditions andactivities that facilitate adaptive performance (Ford & Weissbein,1997) by addressing how the content of feedback interventions af-fects trainees’ ability to perform independently and adapt tochanging conditions (Salas & Cannon-Bowers, 2001).

Theory and hypothesis development

In summary, our argument is that the levels of feedback speci-ficity during training will impact trainees’ exposure to differenttask conditions and their reliance on explicit information process-ing. In turn, these mechanisms will interact to impact what islearned about the task. This was formally proposed as the moder-ated mediation model depicted in Fig. 1.

Information processing refers to mental operations performersuse to identify and select responses to different task situations. Ex-plicit information processing is deliberate and active and includescognitive activities such as planning ahead, evaluating actions,causal reasoning, and hypothesis development and testing. Learn-ing may also occur through association, which is characterized bythe passive encoding of information through the repeated pairingof trainees’ actions and the effects of those actions (Anderson &Schunn, 2000; Maier, 1973; Shanks, 2007). Trainees may rely moreheavily on associative learning when there is a limited choice of re-sponses and when the links between actions and outcomes arefairly transparent, such that knowledge acquisition does not re-quire further inference (Shanks, 2007). Inference processes play amuch larger part in learning as tasks become more dynamic andcomplex, when the relationships between actions and outcomesare less obvious because of multiple possible responses and lags,reciprocity, and cumulative effects in the relationships between ac-tions and outcomes (Wood, Beckmann, & Birney, 2009).

According to dual space theory, problem solving occurs in twotypes of problem spaces: rule space and instance space (Simon &Lea, 1974), alternatively labeled hypothesis space and experimentspace (Dunbar, 1995; Klahr & Dunbar, 1988). Rule space reflectsthe performer’s current understanding of the problem, and hypoth-eses about how a task works are generated in rule space from theperformer’s representation of the problem. Hypotheses are testedin instance space, where performers experiment with the taskand observe situational properties and the effects of their actions.Once tested and accepted, new rules may be incorporated into therepresentation of the problem in rule space and influence perform-ers’ actions in instance space on later performances of the task,without conscious recourse to rule space.

Coordinated movement back and forth between generatinghypotheses in rule space and testing hypotheses in instance spacefacilitates performers’ effective rule induction (Klahr & Dunbar,1988). For example, a manager may develop a hypothesis abouthow a particular employee will react to various incentives and thenobserve the employee’s reaction to different incentives for evidencethat supports or disconfirms the hypothesis. The manager may thenaccept, reject, or adjust the initial hypothesis based on observationsof the test. This process of developing, testing, and modifyinghypotheses can continue until the manager is confident in his orher knowledge of the actions (incentives, in this example) that leadto desired outcomes. This knowledge may be represented in mem-

254 J.S. Goodman et al. / Organizational Behavior and Human Decision Processes 115 (2011) 253–267


ory in the form of a rule, such as, ‘‘If I give Joe an incentive (e.g.,recognition), then he will produce the desired outcome (e.g., betterquality reports)’’ (Anderson, 1982; Singley & Anderson, 1989).

The engagement in explicit information processing based onknowledge in rule space will vary as a function of the type of prob-lem and a performer’s experience with the problem (Simon & Lea,1974). According to Adaptive Control of Thought-Rational (ACT-R)theory (Anderson, 1993; Anderson & Schunn, 2000), skills passthrough an initial ‘‘cognitive’’ or declarative stage (knowing what),through a second associative or procedural stage (knowing how),and onto a third stage where expression of the skill becomes lar-gely automatic and may be applied without conscious effort orawareness. The cognitive stage of learning is effortful and pro-longed and relies heavily on explicit information processing. Aslearners progress through the procedural stage to the automaticstage, they rely more on associative learning processes.

When a problem is not well understood or if the performerencounters a novel situation or an error, activities that involve ex-plicit information processing activities in rule space (i.e., causalreasoning to work out why, hypothesis generation to predict cor-rect actions, and planning for the implementation of those actions)will provide support for the actions taken in instance space. Theseexplicit information processing activities delay movement to theprocedural and subsequent automatic stages of learning. However,on well understood problems and when performance is consis-tently meeting expectations, explicit information processing activ-ities are less necessary, as a performer simply moves from oneproblem state to another in instance space, through the applicationof previously learned rules that are appropriate to the current sit-uation (Simon & Lea, 1974; Vollmeyer, Burns, & Holyoak, 1996).

A similar argument has been made in the error training litera-ture. The low guidance of error management training is expectedto produce more explicit information processing than high guid-ance error avoidant training because errors, which are encounteredmore frequently in error management training, will prompt learn-ers to explicitly think about why an error occurred and what theyare going to about it (Ivancic & Hesketh, 2000). This hypothesis wastested and supported by Keith and Frese (2005), who found thatthe levels of planning, evaluation, and monitoring reported in ver-bal protocols during training, in concert with emotional control,mediated the effects of error training on adaptive transfer perfor-mance. However, it is not clear whether the results of that studywere driven by the procedural guidance given in the error avoidanttraining condition and/or the motivational faming of errors and/orthe exploration instructions given in the error management train-ing condition.

The specificity of feedback can also impact performers’ move-ment through rule and instance space and their associated cogni-tive processes. Low specificity feedback provides no detail aboutperformers’ errors or corrective actions they may take to improveperformance. Performers must uncover their errors and make

adjustments through their own exploration and explicit informa-tion processing, by testing rules in instance space, by revisingtheir representations of the rules in rule space, and by coordinat-ing activities across the two spaces (Klahr & Dunbar, 1988; Simon& Lea, 1974). Specific feedback gives performers information oncorrect and incorrect actions, as well as the information theyneed to identify corrective actions (Payne & Hauty, 1955). Specificfeedback interprets the results of tests of rules in instance space,making the associations between actions and outcomes easy tosee and thus reducing the need for explicit information process-ing. As the specificity of feedback increases, it reduces the needfor explicit information processing to coordinate the activitieswithin and between instance space and rule space.

Hypothesis 1. Feedback specificity will be negatively related tothe level of explicit information processing over the course oftraining.

For many organizational tasks, the correct responses will varydepending upon task conditions, such that operating effectivelyacross the range of conditions requires performers to learn differ-ent responses and adapt these responses accordingly. Correct re-sponses often depend on the extent to which task conditions arefavorable or unfavorable. Favorable task conditions are fairly posi-tive and closer to ideal conditions, that are reasonably straightfor-ward and have few or relatively minor challenges and obstacles.Unfavorable task conditions are fairly negative and present sub-stantial challenges and obstacles that have to be addressed, suchas crises, malfunctions, errors, and adverse working conditions.

In some cases, including our study, movement between favor-able and unfavorable task conditions will be caused by the per-former’s actions. For example, errors may cause problems andproductive strategies may have beneficial effects that move theperformer to unfavorable and favorable task conditions, respec-tively. Alternatively, shifting between task conditions may be atleast partially out of the control of the performer. For example,an increase or decrease in air turbulence may move a pilot intounfavorable or favorable task conditions, respectively. In any case,the rules for correct actions will differ for the favorable and unfa-vorable task conditions, and performing effectively in these condi-tions will require different actions on the part of the performer.

To learn the different responses for different task conditions,performers must first realize the rules are conditional, and thenidentify the situations in which different responses will prove mosteffective. For example, sales people need to recognize that theeffectiveness of different sales tactics will depend on how well aproduct is selling, the extent to which a business transaction isprogressing, and the quality of a relationship with a client, andthen learn to use different tactics under the appropriate conditions.Similarly, civil engineers should learn which materials are moreor less effective depending on how ideal the conditions are (e.g.,

Favorable task condition rule transfer: Correct responses to favorable task conditions during transfer

Feedback specificity Unfavorable task condition

rule transfer: Correct responses to unfavorable task conditions during transfer

Exposure to different task conditions: Percentage of instances of responding to unfavorable task conditions during training

Explicit information processingH1

H2H3 H5

Fig. 1. The proposed moderated mediation model. Hypotheses 4 and 6 are moderated mediation hypotheses, which cannot be labeled readily in the figure.

J.S. Goodman et al. / Organizational Behavior and Human Decision Processes 115 (2011) 253–267 255


climate, soil composition), and they must learn how to respond tochanging conditions (e.g., significantly increased traffic, unusuallysevere weather).

Under favorable task conditions, individuals are more likely tobe exposed to the repeated pairing of actions and their positive ef-fects over multiple trials. Positive action-outcome combinationsare likely to be judged as indicators of success, which may beaccompanied by little perceived need to figure out which actionswere successful and why, or to change actions to gather and pro-cess additional information to learn more about the task. This sit-uation is conducive to associative learning and requires little or noexplicit processing about actions and effects to identify correct re-sponses (Anderson & Schunn, 2000; Maier, 1973). Alternatively,learning processes may differ under unfavorable task conditionsin which individuals encounter challenges that lead them to at-tempt to figure out which of their actions are incorrect, why theyare incorrect, and identify and test alternatives. These efforts to fig-ure things out will involve explicit coordination between hypothe-sis generation in rule space and testing in instance space (Klahr &Dunbar, 1988; Simon & Lea, 1974). Therefore, trainees’ experienceof favorable and unfavorable task conditions will impact the extentto which they engage in explicit information processing.

Additionally, explicit information processing may be activatedby negative self-evaluations that can accompany perceptions offailure (Forgas, 1995) and also may be more likely when perform-ers have high self-efficacy (Wood, Bandura, & Bailey, 1990) andview errors as learning opportunities (Keith & Frese, 2005). Keithand Frese (2005) found that instructions to explore the task andto treat errors as opportunities to learn (error management train-ing) produced higher levels of explicit information processing thanstep by step instructions designed to prevent errors from occurring(error avoidant training). It was assumed that participants who re-ceived error management instructions encountered more errorsthan their counterparts in the error avoidant condition and thattheir higher levels of explicit information processing were a re-sponse to the errors encountered (Ivancic & Hesketh, 2000; Keith& Frese, 2005). Although their task did not include different rulesfor different task conditions or a direct measure of the errorsencountered, the Keith and Frese (2005) results are consistent withthe argument that exposure to unfavorable task conditions, as a re-sult of errors in this case, leads to more explicit information pro-cessing. With Hypothesis 2, we provide a direct test of exposureto unfavorable task conditions as the proximal cause of explicitinformation processing.

Hypothesis 2. The extent of exposure to different task conditionswill be related to the level of explicit information processing, suchthat as exposure to unfavorable task conditions increases, the levelof explicit information processing will increase.

For complex tasks, development of knowledge is enhanced bythe explicit processing of feedback that delays quick and prema-ture movement to automatization, the third and final stage of skillacquisition in the ACT-R theory (Anderson, 1993; Anderson &Schunn, 2000). The learning of many software programs, such asExcel, provides a common example of premature automatization.Most individuals stop thinking about the task of learning Excelonce they have learned enough to get by on their everyday tasks,and as a result learn how to use less than 10% of the program’sfunctionality. Experts typically delay automatization and continueto explicitly process task information much longer than thosewhose task performance peaks at a much lower level (Ericsson,Krampe, & Tesch-Romer, 1993). Ongoing explicit processing ofexperience is crucial to avoiding automatization and has beenlinked to deep transferable knowledge in studies of individuals(Ericsson et al., 1993) and groups (Smith, Ford, & Kozlowski, 1997).

Explicit information processing may be most beneficial for thelearning and transfer of rules that correspond to unfavorable taskconditions. In Goodman and Wood’s (2004) study, the level ofexposure to favorable task conditions during training impactedthe learning and transfer of rules for favorable task conditions.However, greater exposure to unfavorable task conditions wasinsufficient for learning the corresponding rules. One explanationfor the differential effects was that explicit information processingwas needed to learn rules for unfavorable task conditions, whileassociative learning was sufficient for learning rules for favorabletask conditions because of the more frequent positive pairings be-tween actions and outcomes in favorable task conditions. With in-creased exposure to unfavorable task conditions during training,performers may need to devote additional effort to explicit infor-mation processing activities, such as reasoning about the causesof poor performance, generating and testing hypotheses aboutthe effectiveness of alternatives, and forward planning. Given thedifficult nature of unfavorable task conditions, low levels of explicitinformation processing may be detrimental to the learning andtransfer of unfavorable task condition rules.

Hypothesis 3. Explicit information processing will moderate theeffect of exposure to different task conditions on unfavorable taskcondition rule transfer, such that, as the level of explicit informa-tion processing increases, the effect of exposure to unfavorabletask conditions on transfer will change from negative to positive.

As a corollary to Hypothesis 3, we expect that the interactive ef-fect of exposure to different task conditions and explicit informa-tion processing will mediate the effect of feedback specificity onthe transfer of rules associated with unfavorable task conditions.When trainees receive low guidance, low specificity feedback, theyare more likely to make errors that can lead to unfavorable taskconditions (Goodman & Wood, 2004). Low specificity feedback sig-nals to individuals that past actions did or did not work, but forincorrect actions, this feedback provides no information regardingthe correct action among the many available alternatives. There-fore, in the presence of low specificity feedback, explicit informa-tion processing may be required for trainees to discovercorrective actions, thereby encouraging independent, self-guidedtask learning and performance. Low specificity feedback will bebeneficial to the learning and transfer of the rules for unfavorabletask conditions as a result of increasing exposure to unfavorabletask conditions and stimulating the explicit processing of the infor-mation gathered. We therefore propose the following ‘‘secondstage’’ (Edwards & Lambert, 2007, p. 4) moderated mediationhypothesis, in which the path from the mediator to the dependentvariable is the moderated path (Edwards & Lambert, 2007; Preach-er, Rucker, & Hayes, 2007),

Hypothesis 4. The level of exposure to unfavorable task conditionswill mediate the effect of feedback specificity on unfavorable taskcondition rule transfer, and the effect of exposure to unfavorabletask conditions on unfavorable task condition rule transfer will bemoderated by the level of explicit information processing in whichtrainees engage.

The positive impact of high specificity feedback on the learningand transfer of rules associated with favorable task conditions ismediated by exposure to favorable task conditions during training(Goodman & Wood, 2004). When high-guidance, specific feedbackis provided, individuals implement the actions specified in thefeedback and observe the effects. There is little need for them tounderstand the situation or consider alternatives and little pres-sure for them to engage in explicit information processing. Thespecified actions, when implemented, produce good performance



outcomes that may move performers into favorable task conditionsand, over multiple trials, expose individuals to the repeated pair-ings of actions and effects that contribute to associative learning(Anderson & Schunn, 2000; Maier, 1973; Shanks, 2007). Thus thepotential effects of engaging in explicit information processingmay be negligible.

However, it is also possible that explicit information processingmay interfere with the associative learning that occurs through re-peated exposure to favorable task outcomes following prescribedactions, and, therefore, could be harmful to the learning and trans-fer of the rules associated with favorable task conditions. When ac-tion-effect relations are relatively easy to learn through repeatedpairings under favorable conditions, high levels of explicit informa-tion processing may constitute over-thinking. The action-effectassociations may be less likely to be learned if explicit informationprocessing draws attention away from simply experiencing theassociations.

The preceding argument implies competition for limited cogni-tive resources (Kanfer & Ackerman, 1989; Norman & Bobrow,1975) between explicit information processing and associativelearning processes. However, because favorable task conditionsare characterized as fairly positive, ideal, and straightforward, suf-ficient cognitive resources may be available for engaging in bothassociative and explicit information processing. Therefore, themindful processing of the cues encountered from the repeated ac-tion-effect pairings may strengthen the associations of the rulescorresponding to favorable task conditions. Explicit informationprocessing could supplement associative learning, augmentingthe positive effects of high exposure to favorable task conditions.In this way, explicit information processing could help to avoidpremature automatization and lead to deeper learning (Ericssonet al., 1993).

Drawing on the opposing arguments presented above, we pro-pose the following competing hypotheses.

Hypothesis 5a. Explicit information processing will moderate theeffect of exposure to different task conditions on favorable taskcondition rule transfer, such that, as the level of explicit informa-tion processing increases, the positive effect of exposure tofavorable task conditions will decrease.

Hypothesis 5b. Explicit information processing will moderate theeffect of exposure to different task conditions on favorable taskcondition rule transfer, such that, as the level of explicit informa-tion processing increases, the positive effect of exposure to favor-able task conditions will increase.

As corollaries to Hypotheses 5a and 5b, we propose the follow-ing competing, second stage (Edwards & Lambert, 2007) moder-ated mediation hypotheses.

Hypothesis 6a. The level of exposure to favorable task conditionswill mediate the positive effect of feedback specificity on favorabletask condition rule transfer, and the effect of exposure to favorabletask conditions on favorable task condition rule transfer will bemoderated by the level of explicit information processing in whichtrainees engage in a manner consistent with Hypothesis 5a.

Hypothesis 6b. The level of exposure to favorable task condi-tions will mediate the positive effect of feedback specificity onfavorable task condition rule transfer, and the effect of exposureto favorable task conditions on favorable task condition ruletransfer will be moderated by the level of explicit informationprocessing in which trainees engage in a manner consistent withHypothesis 5b.

Method

Overview and experimental design

We conducted a single-factor experiment with repeated mea-sures. Participants completed a 19-trial training task under oneof two nested levels of feedback specificity. They produced ‘‘thinkaloud’’ verbal protocols while performing the task. We then testedthe transfer of the learning that had occurred during training withperformance on 14 trials across two transfer tasks, completed twodays later, with minimal feedback-provided guidance. Assessmentsof performance on transfer tasks are free of the confounds betweentransient and more permanent learning effects that are present inmeasures of performance during training (Wulf & Schmidt, 1994)and make such measures ambiguous with respect to the amountlearned (Schmidt & Bjork, 1992).

Participants

Individuals were recruited from multiple sections of an under-graduate management course at a large US university and givenextra course credit for their voluntary participation. Fifty-two indi-viduals completed the training task. We recorded useable verbalprotocol data for 48 of the 52 participants. One audio file wasincomplete due to a technical problem, and three audio files couldnot be transcribed because transcribers could not understand theparticipants’ accents. Participants returned 2 days later and com-pleted the transfer tasks. Four participants were lost due to attri-tion from Time 1 to Time 2, reducing the Time 2 sample size to48 (92.3%). Complete Time 1 and Time 2 data were available for44 of the 52 participants (84.6%). Participants were 44 percent fe-male and ranged in age from 19 to 39 (�x = 21.6, SD = 2.73). Becausewe used a management decision-making simulation as our exper-imental task, we assessed participants’ management-related expe-rience with a five item, five-point scale, with 1 = ‘None’ and5 = ‘Very Much.’ We asked them to rate their previous experiencewith delegating work, assigning performance goals, setting theirown performance goals, giving performance feedback, and reward-ing others for their performance. As expected, participants hadonly a moderate amount of previous management-related experi-ence (�x = 2.70, SD = .93, a = .81). The relative novelty of the taskfacilitated the study of learning processes and transfer.

The task

The study was presented as a project in managerial decision-making. Participants served as managers of a work team in a busi-ness simulation called the Furniture Factory (Wood & Bailey,1985). The simulation is one of many computer-simulated scenar-ios that have been widely employed in the study of how individu-als learn to control complex and dynamic systems, such as workteams (Dörner, 1996). The complexity of the simulation is a prod-uct of several factors, including: the number of decision variables,each with multiple options; the interactions of variables; the dy-namic nature of the rules, which change across different task con-ditions; the effects of the performers’ decisions on task conditions;and the lack of transparency, which requires performers to inferthe underlying rules from their observations of surface characteris-tics, including feedback. The simulation records all decisions madeand whether each decision is correct or incorrect, enabling objec-tive assessment of rule transfer.

Participants managed a group of three workers during thetraining period and five workers during the transfer period. Therules linking decisions to outcomes were the same for boththe three-worker and five-worker versions of the simulation. Each



performance trial represented a week at the Furniture Factory. Intheir managerial role, participants assigned the workers to jobsand motivated them by choosing among and providing varioustypes of goals, feedback, and rewards. This feedback should notbe confused with the feedback provided to the participants on theirmanagerial performance and decisions, which we manipulatedwith our high and low specificity feedback treatment conditions.

Participants received written instructions describing their role,the skills and work-related preferences of each worker, thedescriptions and requirements of each Furniture Factory job andchoices of types of goals (i.e., none, low, moderate, or high), feed-back (i.e., none, process, outcome, or process and outcome), and re-wards (i.e., none, moderate, or high) they could give to theirworkers. The instructions were available to participants on a refer-ence sheet throughout the simulation. To learn how to managetheir workers, participants had to figure out the correct responsesfor assigning goals, providing feedback, and allocating rewards totheir employees under different task conditions. The decision rulesthat link the participants’ decisions to outcomes for goals, feed-back, and rewards (in other words, the task structure) are summa-rized in Table 1. Correct job assignment decisions were notdependent on the favorable and unfavorable task conditions, soonce the best employee-job match was found, no job assignmentchanges were necessary. The rules are represented in the mathe-matical model used to calculate the hours taken to complete theassigned furniture order on each trial (see Wood & Bailey, 1985).Participants received no information regarding the mathematicsof the model, the task conditions governing the rules, or the rulesthemselves.

Manipulations

We manipulated the guidance properties of feedback to createtwo nested feedback specificity conditions. Feedback was pre-sented in writing on the computer screen following each perfor-mance trial and remained on the screen until it was updated forthe next trial.

Low feedback specificityIn the low feedback specificity condition, participants received

outcome feedback that included the weekly job performance levelsfor each of their three workers and for the entire group. The perfor-mance data were reported in a table that showed the estimatedstandard hours for each job, derived from time and motion studies;the actual time taken by each employee to perform his assignedjob; and a comparison of the actual to standard performance statedas a percentage above or below the standard. A statement of groupperformance was also provided. For example, ‘‘In week 12, yourdepartment produced its order in 132.0 hours. Its performance is43% worse than estimated hours.’’

High feedback specificityParticipants in the high feedback specificity condition were pro-

vided the same type of outcome feedback as recipients in the lowfeedback specificity condition. This feedback was supplementedwith specific process feedback that told participants whether thejob, goal, feedback, and reward they gave to each worker wereright or wrong. This process feedback included a series of 12 state-ments (3 workers � 4 decisions) for each trial, such as, ‘‘You as-signed Jack to the wrong job,’’ ‘‘You gave Jack the wrongfeedback,’’ ‘‘You gave Jack the correct goal,’’ and ‘‘You gave Jackthe correct reward.’’

Procedure

Time 1 training periodParticipants were randomly assigned to either the low or high

feedback specificity condition. Data were collected from partici-pants individually in a small room with one computer, in the pres-ence of the experimenter. Participants performed 19 trials (or‘‘work weeks’’) of the management simulation task to allow forpractice and learning. All participants were instructed that theirgoal during the training period was to figure out which employeesperform best in which jobs, and which types of goals, feedback, andrewards lead to the best performance.

Participants were instructed to ‘‘think aloud’’ during the train-ing period. Their verbal protocols were audio-recorded and subse-quently transcribed and content coded. Care was taken tominimize the reactive effects of verbalization on participants’thought processes (Ericsson & Simon, 1993, 1998). First, we usedconcurrent verbal protocols, which require participants to verbal-ize their thoughts as they have them. Second, participants were gi-ven general ‘‘think aloud’’ instructions. They were told to, ‘‘sayaloud everything that comes to mind about how you are goingabout making decisions as you perform the task, whether you thinkit makes sense or not.’’ Participants were reminded to ‘‘keep think-ing aloud’’ when they were silent for more than five seconds. Third,we had participants speak into a microphone, rather than directtheir verbalizations to the experimenter. Fourth, prior to startingthe experimental task, participants were given several trials of awarm-up task to practice verbalizing their thoughts while engagedin a task. We used a game called the Lemonade Stand (www.lem-onadestandgame.com), which requires participants to make deci-sions related to running a lemonade stand (e.g., pricing, qualityand inventory control, purchasing supplies). These four conditionsfacilitate undisrupted task focus and have minimal reactive effectson thought processes. Alternatively, retrospective verbal protocols(expressed after task completion); specific instructions andreminders to describe, explain, and interpret one’s thoughts andactions; directing verbalizations to another person; and beginningthe focal task without practicing thinking aloud tend to generatereactive influences on thought processes, leading people to go

Table 1Decision rules for simulation.

Decision type Rule description

Employee job allocation Assign each employee to a job based on the match between job and employee characteristicsGoal Give the high goal initially

Give the moderate goal after an employee performs very poorly for two consecutive weeks (620% worse than standard)Give the high goal after an employee performs well (Pstandard)Giving no goal or the low goal is never optimal

Feedback Give outcome plus process feedback initiallyGive only outcome feedback after an employee performs well (Pstandard) for three consecutive weeksGiving no feedback or only process feedback is never optimal

Reward Give an employee no reward for poor performance (<standard)Give the moderate reward when performance is close to standard (standard 6performance <5% better than standard)Give the high reward for good performance (P5% better than standard)



beyond verbalizing spontaneously generated thoughts (Ericsson &Simon, 1993, 1998). In addition to the procedural precautions, weassessed the reactivity of our verbal protocol methodology by com-paring the effects of feedback specificity on transfer of training inthis study with the results of a similar study done without verbalprotocols. The results of the validity assessment are presented inthe Results section.

Time 2 transfer periodParticipants returned two days later for the second part of the

experiment, in which they performed two transfer tasks, lastingeight trials and six trials, respectively. They were provided thesame type of outcome feedback provided to participants duringthe training period, but no supplemental feedback on their deci-sions was provided. Participants had to manage a different set offive workers for each of the transfer tasks. The rules and mathe-matical model that linked the participants’ responses to perfor-mance outcomes remained the same as for the training task.

The purpose of the first transfer task was to maximize the in-stances participants responded to favorable task conditions, there-by providing them with ample opportunities to demonstrate theextent to which they had learned and could apply the rules forresponding under favorable task conditions (i.e., give the high goal,outcome feedback, and the high reward). The purpose of the sec-ond transfer task was to ensure that participants experienced sev-eral instances of responding to unfavorable task conditions,thereby providing them with ample opportunities to demonstratethe extent to which they had learned and could apply the correctresponses for unfavorable task conditions (i.e., give the moderategoal, outcome plus process feedback, and no reward). This processwas accomplished by assigning participants five workers who per-formed fairly poorly in the furniture factory jobs, even when cor-rect management decisions were made. Together, the two tasksprovided the variance in performance necessary to measure levelsof transfer for the favorable and unfavorable task conditions and totest the hypotheses.

Measures

Explicit information processingThis variable was an assessment of the explicit information pro-

cessing activities participants engaged in during training. Informa-tion processing was measured by content coding transcriptions ofthe verbal protocols collected during the training period. We devel-oped a preliminary coding scheme by searching the literature forcognitive processing dimensions used by other researchers. Thefirst author and a research assistant compiled a list of relevant con-cepts from Berardi-Coletta et al.’s (1995) processing level category,Keith and Frese’s (2005) meta-cognitive and task-focused state-ments, Burns and Vollmeyer’s (2002) predictive and non-predictivetesting, Van der Liden and colleagues’ (2001, 2003) systematic andunsystematic exploration, and Isenberg’s (1986) causal reasoning.The first author and research assistant then went through an iter-ative process of coding sections of several protocols using theseconcepts, discussing the findings, adapting the operational defini-tions to fit the task, and developing descriptive examples for eachdimension to be coded.

With extensive training, the third author (hereafter referred toas the coder) coded the verbal protocol transcripts with no priorknowledge of the study’s purposes, hypotheses, or experimentalconditions. The first author worked closely with the coder to en-sure she understood the definitions and could correctly identifystatements that fit (and did not fit) the various coding dimensions.This process included coding protocols, meeting to discuss the cod-ing, and clarifying ambiguity. This interaction continued through-out the coding process and led to additional changes in the

coding scheme. The coder recoded transcripts as needed to reflectadditional changes in the coding scheme. The protocol transcriptsranged in length from three to ten single-spaced, typed pages,and each transcript took approximately four to six hours to code,not including the time spent discussing the coding. The sevendimensions of the coding scheme used to test our hypotheses aredescribed in Appendix A.

The coder recorded the frequency of occurrence of each of theseven information processing dimensions for each of the 19 train-ing trials for each participant. The seven information processingdimensions were then summed to compute the total frequencyof occurrences of explicit information processing for each trainingtrial and the total amount of explicit information processing acrossthe entire training period.

Exposure to different task conditionsThis variable was measured during the Time 1 training period

and was operationalized as the percentage of instances that partic-ipants were exposed to unfavorable task conditions during train-ing. We determined for which instances each participant shouldhave given the moderate goal, outcome and process feedback,and/or the low reward to each of his or her workers during thetraining period, as stipulated by the task rules listed in Table 1.The percentage of instances of responding to unfavorable task con-ditions was computed as the total number of times an unfavorabletask condition rule should have been applied divided by 171 (3workers � 3 decisions � 19 trials). Because the simulation hastwo task conditions (i.e., favorable and unfavorable) in whichtwo different sets of rules operate, when participants are not expe-riencing the unfavorable task condition for a particular decisionmade for an employee, they are necessarily experiencing the favor-able task condition for that decision. Therefore, high exposure tounfavorable task conditions corresponds to low exposure to favor-able task conditions, and vice versa, and the exposure variableranges from high exposure to favorable (and low exposure to unfa-vorable) task conditions on one end of the continuum to high expo-sure to unfavorable (and low exposure to favorable) taskconditions on the other end of the continuum.

Transfer of trainingTransfer was assessed at Time 2. We separately computed the

accuracy of responses to favorable task conditions and the accu-racy of responses to unfavorable task conditions. This computationaddressed a criticism of past training research that the use of over-all measures of transfer performance makes it difficult to evaluatewhy training conditions impact or do not impact transfer (Ford &Weissbein, 1997).

Favorable task condition rule transfer. This variable was measuredat Time 2 as the percentage of instances the participant madethe correct responses to favorable task conditions, as stipulatedin the rules listed in Table 1. We determined for which instanceseach participant should have applied the rules for responding tofavorable task conditions by giving the high goal, outcome feed-back, and/or the high reward to each worker, and for which ofthose instances these decisions were correctly made. The variablewas computed by dividing the total number of times each favor-able task condition rule was correctly applied by the total numberof times each favorable task condition rule should have been ap-plied during the transfer period.

Unfavorable task condition rule transfer. This variable was the per-centage of instances the participant made the correct responsesto unfavorable task conditions during the Time 2 transfer period.We determined for which instances each participant should haveapplied the rules for responding to unfavorable task conditions



by giving the moderate goal, process plus outcome feedback, and/or no reward to each worker, and for which of those instancesthese decisions were correctly made. The variable was computedby dividing the total number of times each unfavorable task condi-tion rule was correctly applied by the total number of times eachunfavorable task condition rule should have been applied.

Perceived feedback specificity manipulation checkTwo items were collected at the end of Time 1 to assess percep-

tions of the specificity of the objective feedback provided to partic-ipants (rkk = .793). The items were (1) I received detailed feedbackabout my performance as Special Order Manager (2) I was givenspecific feedback about my performance as Special Order Manager.They were measured on a 5-point Likert scale ranging from‘Strongly Disagree’ to ‘Strongly Agree’. As expected, participantsin the high feedback specificity condition (�x = 3.13, SD = .944) per-ceived the feedback they received to be more specific than didthose in the low feedback specificity condition (�x = 2.37, SD = .944)[t(50) = 2.938, p = .005, CI: .243–2.30].

Control variablesSex. Sex was coded male = 0, female = 1. We controlled for sex inthe tests of Hypotheses 3–6 because it was significantly relatedto transfer of training, and including this control could decreasethe error term and increase the power of our analyses. Controllingfor sex in the analyses strengthened the results.

Self-efficacy. Self-efficacy was measured after trial 19 as an assess-ment of motivation levels at the end of the training. We controlledfor self-efficacy in the tests of Hypotheses 3–6 to rule out motiva-tion as an explanation for the results and to examine potentialcomplementary effects of motivational and informational pro-cesses. Self-efficacy was not significantly correlated with the vari-ables of interest, but controlling for this variable in the analysesstrengthened the results.

Participants were asked whether or not they believed theywould be able to attain each of nine different performance levels,and if ‘yes’, the extent to which they were confident that they couldattain the performance level. Confidence scores were assessed on a9-point scale and summed across performance levels to yield theself-efficacy measure, consistent with previous operationalizationsof self-efficacy for this simulation task (Wood et al., 1990). Scoresranged from a low of 0 to a high of 81.

Analyses

Hypothesis 1 was tested with a t-test comparing the overallamount of explicit information processing during training for thosein the low versus high feedback specificity conditions. We alsotested Hypothesis 1 with a generalized estimation equation(GEE), modeled with Poisson regression, to examine the effects offeedback specificity on explicit information processing from trialto trial over the course of training.

We tested Hypothesis 2 with a bivariate correlation betweenexposure to different task conditions and the overall amount of ex-plicit information processing during training.

Hypotheses 3 and 5 were tested with general linear models(GLM) to examine the predicted interactions between exposureto different task conditions and explicit information processingon unfavorable and favorable task condition rule transfer, respec-tively. The models included sex and self-efficacy as control vari-ables, between-subjects main effects for exposure to differenttask conditions and explicit information processing, and thehypothesized interaction.

Edwards and Lambert (2007) and Preacher et al. (2007) devel-oped new approaches for testing for moderated mediation, which

were designed to address the shortcomings of older approaches.The authors provide comparable general frameworks for analyz-ing a number of types of moderated mediation effects, referredto as models for integrating moderation and mediation (Edwards& Lambert, 2007) and conditional indirect effects (Preacher et al.,2007) by the respective authors. The analytical frameworks aresimilarly based on regression and path-analysis models for esti-mating path coefficients. They both use bootstrapping to estimatethe sampling distribution of the conditional indirect effectthrough a process of repeated re-sampling with replacement fromthe data, and both generate bias-corrected confidence intervals.The authors address several of the same models and some uniquemodels, but both include the type of model we hypothesized,which corresponds to Edwards and Lambert’s (2007) second stagemodel and Preacher et al.’s (2007) Model 3, in which the pathfrom the mediator to the dependent variable is the moderatedpath.

Both sets of authors present statistical procedures for estimat-ing moderated mediation models. We used the SPSS macro devel-oped by Preacher et al. (2007), because it computes all necessarystatistics. We assessed whether the strength of the indirecteffects of feedback specificity on unfavorable task condition ruletransfer (Hypothesis 4) and favorable task condition rule transfer(Hypothesis 6) through exposure to different task conditions areconditional on the level of explicit information processing. TheJohnson–Neyman (Johnson and Neyman, 1936) technique wasused to probe significant conditional indirect effects by examiningthe effects at various values of the moderator.

Results

Descriptive statistics

Correlations among the study variables and demographic andcontrol variables and total sample and cell means and standarddeviations are shown in Table 2. The relationships among the studyvariables were as expected, and there were few significant rela-tionships among the demographics, controls, and the study vari-ables. Sex was related to transfer, such that men exhibited betterfavorable task condition rule transfer and women exhibited betterunfavorable task condition rule transfer.

Verbal protocol validity

Prior to testing the hypotheses, we assessed whether the actof thinking aloud was likely to have had reactive effects on cog-nitive processing. Ericsson and Simon (1980, 1993) concludedthat the verbal protocol methodology, if implemented correctly,does not change the cognitive processes under study becauseparticipants are merely stating what is in their short-term mem-ory at the time. Their conclusion was based on findings frommultiple studies showing that think-aloud participants and silentcontrol group participants exhibited similar task behaviors andhad comparable levels of task performance. We made a similarcomparison by using Goodman and Wood’s (2004) participantsas our silent control group and comparing the pattern of findingsfrom their study to those of the current study. The only signifi-cant difference between the methodologies of the two studieswas our addition of the concurrent verbal protocol. Discrepantresults between the two studies would indicate that the act ofthinking aloud was reactive; in that it biased the results byaltering participants’ thought processes. Similarity in the effectsof feedback specificity on transfer across the two studies woulddemonstrate that having people engage in thinking aloud wasnon-reactive; in that it did not alter participants’ thought pro-cesses and bias the results.



A repeated measures ANOVA using the dataset for the currentstudy showed that feedback specificity differentially affectedfavorable and unfavorable task condition rule transfer, as evi-denced by a significant interaction between feedback specificityand the type of transfer (favorable versus unfavorable task condi-tion rule transfer) [F(1,46) = 25.21, p = .000; g2 = .35]. Those in thehigh feedback specificity condition exhibited higher favorable taskcondition rule transfer than those in the low feedback specificitycondition (78% v. 52%; W = �.132, SE = .033, t(46) = �4.041,p = .000, g2 = .262), while those in the low feedback specificity con-dition exhibited higher unfavorable task condition rule transferthan those in the high feedback specificity condition (68% v. 54%;W = .070, SE = .023, t(46) = 2.99, p = .004, g2 = .163). These resultsare equivalent to the results of Goodman and Wood’s (2004) testof Hypothesis 3 (p. 816–817) and provide evidence for the nonre-activity of the verbal protocol methodology. Having our partici-pants think aloud did not impact the effects of feedbackspecificity on transfer and was therefore unlikely to have affectedcognitive processes.

Verbal protocols capture a portion of the information a partic-ipant is attending to, and thus represent an incomplete record ofthe contents of short-term memory (Ericsson & Simon, 1980,1993). While it is not possible to assess the completeness ofour participants’ verbal reports, verbal records tend to be lesscomplete when the information to be processed is not stored ver-bally in short-term memory (e.g., visual imagery that would needto be transformed into words), when performance is highly auto-mated, and for tasks that are primarily psychomotor in nature orinvolve complex visually encoded stimuli (Ericsson & Simon,1980). Our verbal protocols are likely to be relatively more com-plete because our task did not have these characteristics. In addi-tion, as long as the content of short-term memory is equallyunderestimated in all treatment conditions, the results may beconservative, but otherwise unaffected. We therefore assessedwhether there was a difference in the number of words spokenby those in the low and high feedback specificity conditions. Apaired t-test showed that the number of words spoken, excludingmaterial read from the computer screen, was not statistically dif-ferent for those in the low (�xlow = 2919.08, SD = 836.02) and high

feedback specificity conditions (�xhigh = 2538.96, SD = 1131.29;t46 = 1.33, p = .19, 95% CI: �194.65 to 954.90). The similarity inthe number of words spoken suggests no differences in the com-pleteness of the verbal reports across the feedback specificityconditions.

Tests of hypotheses

The prediction in Hypothesis 1 that feedback specificity wouldbe negatively related to explicit information processing was sup-ported (t46 = 2.02, p = .049, 95% CI: .045–35.86). Those in the lowfeedback specificity group engaged in more total instances of expli-cit information processing during training (�x = 80.52, SD = 28.21)compared to those in the high feedback specificity condition(�x = 62.57, SD = 33.38). The hypothesis was also supported by aGEE showing a negative relationship between feedback specificityand explicit information processing (b = �0.334, SE = .138,v2

1 = 5.45, p = .02) that did not change significantly over the courseof training (Feedback specificity � Trial: v2

18 = 23.88, p = .160).For a more fine-grained assessment of Hypothesis 1, we also

examined the individual cognitive processes that are listed inAppendix A of the manuscript. Feedback specificity was negativelyrelated to planning for future decisions (r = �.242, p = .049) andperformance evaluation (r = �.343, p = .009) and to the combina-tion of strategies indicative of lower-level information processing(i.e., planning, performance evaluation, non-predictive testing,and lower-level causal reasoning; r = �.296, p = .021). Feedbackspecificity was not significantly related to the other individualindicators of explicit information processing or to the combinationof strategies indicative of higher-level information processing (i.e.,predictive testing, experimentation despite good performance orcorrect decision, and higher-level causal reasoning). These findingsare addressed in the Discussion section.

Hypothesis 2 was supported by the bivariate correlation be-tween exposure to different task conditions and explicit informa-tion processing (r = .557, p = .000). The percentage of instances ofresponding to unfavorable task conditions was positively relatedto the amount of explicit information processing participants en-gaged in during training.

Table 2Correlations among variables and descriptive statisticsa.

1 2 3 4 5 6 7 8 9

1. Sexb 12. Age �.026 13. Management experience �.118 .050 14. Self-efficacy �.261 �.001 .032 15. Feedback specificityc �.116 �.078 �.122 �.071 16. Exposure to different task conditionsd .170 �.056 �.129 �.045 �.657*** 17. Explicit information processing e .212 �.033 �.284 �.201 �.285* .557*** 18. Favorable task condition rule transferf �.338* .043 .024 .151 .512*** �.640*** �.513*** 19. Unfavorable task condition rule transferf .292* �.119 .122 �.139 �.404** .252 .218 �.209 1Total sampleMean .44 21.56 2.70 48.78 – 28.33% 71.92 65% 61%SD .50 2.73 .93 16.42 20.46 31.79 26 18Low feedback specificityMean .50 21.77 2.80 49.96 – 41.24 80.52 52 68SD .51 3.67 .93 16.23 20.46 28.21 27 13High feedback specificityMean .38 21.35 2.58 47.65 – 15.42 62.57 78 54SD .50 1.26 .94 16.84 6.21 33.38 17 19

a Pairwise Ns range 45–52.b Coded 0 = Male, 1 = Female.c Coded 1 = Low, 2 = High feedback specificity.d Coded as the percentage of instances of responding to unfavorable task conditions across the 19 training trials.e Total number of instances of explicit information processing during training.f Percentage of correct responses during transfer.

* p < .05.** p < .01.

*** p < .001.



Hypothesis 3 was supported by the significant interaction termin the GLM (b = .012, SE = .007, t38 = 1.79, p = .041, 95% CI: .001–.023, partial g2 = .078). Panel A of Fig. 2 depicts the form of theinteraction, which is consistent with our predictions. As explicitinformation processing increased, the effect of exposure to unfa-vorable task conditions during training on unfavorable task condi-tion rule transfer changed from negative to positive. When traineeshad greater exposure unfavorable task conditions, engaging in low-er levels of explicit information processing was detrimental to thetransfer of unfavorable task condition rules, whereas engaging inhigher levels of explicit information processing benefited thetransfer of unfavorable task condition rules. We explored theinteraction further by using the Johnson–Neyman (Johnson andNeyman, 1936) technique to examine the effects at various valuesof the moderator. The results showed that the exposure to unfavor-able task conditions had a statistically significant positive effecton unfavorable task condition rule transfer when the frequencyof explicit information processing was above 102 instances duringtraining, which is approximately 1 standard deviation above themean.

Table 3 shows the results for Hypothesis 4. The predicted mod-erated mediation effect was supported by the significant interac-tion between exposure to different task conditions and explicitinformation processing. To investigate the form of the indirect ef-fect, we examined it at a range of values of exposure to the differ-ent task conditions. The results presented in the lower portion ofTable 3 show that the indirect effect of feedback specificity onunfavorable task condition rule transfer through exposure to unfa-vorable task conditions was positive at lower levels and negative athigher levels of explicit information processing. Low feedbackspecificity led to higher exposure to unfavorable task conditions,which, in turn, had a negative effect on the transfer of unfavorabletask condition rules when participants engaged in low-to-moder-ate levels of explicit information processing. Alternatively, whenparticipants engaged in high levels of explicit information process-ing, exposure to unfavorable task conditions had a positive effecton the transfer of unfavorable task condition rules. The indirect ef-fect of feedback specificity was strongest when trainees engaged inlow-to-moderate levels of explicit information processing.

Hypotheses 5 predicted that explicit information processingwould moderate the effects of exposure to different task conditionson the transfer of favorable task condition rules, and we offeredcompeting hypotheses about the form of the interaction. The sig-

nificant interaction term in the GLM (b = �.012, SE = .007,t38 = �1.86, p = .036, 95% CI: �.023 to �.001, partial g2 = .083) pro-vides support for Hypothesis 5b, that explicit information process-ing strengthens the positive effect of exposure to favorable taskconditions. Panel B of Fig. 2 depicts the form of the interactionand shows that exposure to favorable task conditions is helpfulfor favorable task condition rule transfer across all levels of explicitinformation processing, but becomes more beneficial as explicitinformation processing increases. Examination of the effects at var-ious values of the moderator (Johnson & Neyman, 1936) showedthat the exposure to favorable task conditions had a statisticallysignificant positive effect on favorable task condition rule transferwhen the frequency of explicit information processing was above43 instances during training, which is approximately 1 standarddeviation below the mean. Note that the exposure to different taskconditions variable is coded so that higher levels are indicative ofmore exposure to unfavorable task conditions and lower levelsare indicative of more exposure to favorable task conditions.

The results shown in Table 4 address competing Hypotheses 6aand 6b. The moderated mediation effect proposed in Hypothesis 6bis supported by the significant interaction between exposure todifferent task conditions and explicit information processing. High-er feedback specificity led to more exposure to favorable task con-ditions, which, in turn, had a positive effect on favorable taskcondition rule transfer. The indirect effect of feedback specificityon the transfer of favorable task condition rules through exposureto different task conditions is positive at all levels of explicit infor-mation processing, and is stronger as trainees engage in higher lev-els of explicit information processing.

Discussion

The results of our study support the argument that the specific-ity of feedback affects explicit information processing, which hasdistinct effects on the learning of the rules for performing the taskunder favorable and unfavorable task conditions.

Feedback specificity was negatively related to the level of expli-cit information processing participants engaged in during training.This finding supports the strong guidance function of specific feed-back and is consistent with the notion that those who receive high-guidance are less likely to rely on explicit information processingin their learning of a task. In addition, those who had more expo-sure to unfavorable task conditions during training engaged in

(A) (B)R

ule

Tra

nsfe

r: %

Cor

rect

Dec

isio

ns

Exposure to Different Task ConditionsExposure to Different Task Conditions

Fig. 2. Interactions between exposure to different task conditions and explicit information processing on transfer, controlling for sex and self-efficacy. (A) Unfavorable taskcondition rule transfer. (B) Favorable task condition rule transfer.



more explicit information processing than those who had moreexposure to favorable task conditions. This finding is consistentwith and provides support for the argument from error manage-ment studies that exposure to errors encourages explicit informa-tion processing during training (Ivancic & Hesketh, 2000; Keith &Frese, 2005).

Support was found for the predicted moderated and moderatedmediation effects on the transfer of rules associated with unfavor-able task conditions. Participants who received low specificityfeedback had greater exposure to unfavorable task conditions,which helped them learn rules for managing unfavorable task con-ditions, but only when they engaged in high levels of explicit infor-mation processing. This finding suggests that explicit informationprocessing is necessary when trainees encounter challenges thatrequire the discovery of corrective actions. Not only is exposureto unfavorable task conditions insufficient for learning the corre-sponding rules (Goodman & Wood, 2004), failing to engage in en-ough explicit information processing can be detrimental to thelearning and transfer of the unfavorable task condition rules. Whattrainees do with their experiences is important.

Moderated and moderated mediation effects were also foundfor the transfer of the rules associated with favorable task condi-tions. With high feedback specificity, participants had less expo-sure to unfavorable task conditions and more exposure tofavorable task conditions. The greater exposure to favorable taskconditions benefited the learning and transfer of favorable taskcondition rules, and became more beneficial as explicit informationprocessing increased. This finding suggests that specific feedback

and explicit information processing work together to impact thelearning and subsequent transfer of favorable task conditionrules. However, since specific feedback tends to decrease explicitinformation processing, the challenge is in how to encourage suchprocessing when specific feedback is provided.

Implications for future research

Our finding that participants who had ample exposure to favor-able task conditions decreased their levels of explicit informationprocessing presents a challenge for promoting continuousimprovement and, for some tasks, peak performance. Future re-search might consider how to promote explicit information pro-cessing under favorable task conditions, so that learning does notplateau too early or at highly suboptimal levels, as is typicallythe case for dynamic, complex tasks (Gary & Wood, 2010).

Future research could also examine how to suppress the nega-tive effects of feedback specificity on explicit information process-ing, so that explicit information processes can operate in tandemwith high guidance. This examination might involve explainingthe benefits of explicit information processing to trainees andteaching them skills or techniques for the explicit processing ofspecific feedback in ways that are most beneficial to transfer.

The future research directions suggested above could be studiedin the context of training, as in our study. However, other contextssuch as performance appraisal and coaching could also be exam-ined. Studies could be developed in which supervisors and coachestry to facilitate explicit information processing when employees

Table 3Regression results for moderated mediation effects on unfavorable task condition rule transfera.

Predictor b SE t p

Exposure to different task conditionsConstant 46.942 7.098 6.613 .000Sex .028 3.902 .007 .497Self-efficacy �.157 .116 �1.352 .092Feedback specificity �24.180 3.775 �6.405 .000

Unfavorable task condition rule transferConstant 93.965 16.386 5.734 .000Sex 9.633 5.141 1.874 .034Self-efficacy �.062 .156 �.396 .347Feedback specificity �17.290 6.949 �2.488 .009Explicit information processing �.270 .195 �1.384 .087Exposure to different task conditions �1.208 .538 �2.245 .015Explicit information processing by exposure to different task conditions .012 .006 1.939 .030

Explicit information processing Bootstrap indirect effect Boot SE Boot Z Boot p

Conditional indirect effect at a range of values of explicit information processing14.00 26.165 12.552 2.085 .01919.85 24.354 11.636 2.093 .01825.70 22.543 10.740 2.099 .01831.55 20.731 9.869 2.101 .01837.40 18.920 9.032 2.095 .01843.25 17.109 8.237 2.077 .01949.10 15.297 7.499 2.040 .02154.95 13.486 6.836 1.973 .02460.80 11.675 6.273 1.861 .03166.65 9.863 5.837 1.690 .04672.50 8.052 5.559 1.449 .07478.35 6.241 5.462 1.142 .12784.20 4.429 5.558 .797 .21390.05 2.618 5.835 .449 .32795.90 .807 6.271 .129 .449

101.75 �1.005 6.834 �.147 .442107.60 �2.816 7.497 �.376 .354113.45 �4.628 8.234 �.562 .287119.30 �6.439 9.029 �.713 .238125.15 �8.250 9.866 �.836 .202131.00 �10.062 10.737 �.937 .174

a n = 44. Unstandardized regression coefficients are reported. Bootstrap sample size = 5000.



are experiencing favorable task conditions and encourage or re-quire employees to process specific feedback provided.

Studying guidance provided by human sources could presentinteresting challenges. Research in the context of tutoring showsthat instructors do not necessarily provide assistance in a way thatoptimizes independent performance (Merrill, Reiser, Merrill, &Landes, 1995; VanLehn, Siler, Murray, Yamauchi, & Bagget, 2003).Tutors often provide modeling and other forms of guidance thatprevent errors from occurring. They also tend to identify errorsright way and demonstrate how to correct them, without givinglearners the chance to try to do this themselves (Merrill et al.,1995; VanLehn et al., 2003). These behaviors may also be exhibitedby supervisors, coaches, trainers, and others responsible for em-ployee development. If so, future research could investigate howto promote more effective developmental behaviors, such asprompting performers to explain their errors and identifying cor-rections and providing explanations only when performers are un-able to do so (VanLehn et al., 2003) and then assess the effects ofthose efforts on employees’ explicit information processing, expo-sure to different task conditions, and subsequent performance.

Our supplemental analyses for Hypothesis 1 suggested thatfeedback specificity affected lower-level explicit information pro-cesses, but not the higher-level processes that require moreextensive analyses and may provide more information about thetask. However, even more frequent lower-level explicit informa-tion processing proved to be beneficial for transfer. Most ofthe explicit information processing in the verbal protocols was

lower-level processing, suggesting that the participants may havebeen relatively unskilled in high quality explicit information pro-cessing or unmotivated to engage in such processing. Future re-search should attempt to stimulate higher-level processing byteaching trainees meta-cognitive skills or motivating them to en-gage in meta-cognitive processes. Meta-cognitive processing alsocan be encouraged through the manipulation of verbal protocolinstructions (Berardi-Coletta et al., 1995). With higher-level pro-cessing induced, future research can explore whether specificfeedback also leads to reductions in the amounts of higher-levelinformation processing and examine the subsequent effects onlearning and transfer. A related avenue for future research is toassess whether specific feedback on trainees’ information pro-cessing activities is more beneficial for learning and transfer thanspecific feedback on their task performance. Perhaps prescriptionsregarding specific feedback are targeting the wrong issue andshould be focused on the analytical processes of learning ratherthan on the task itself.

Although not directly tested, the results of our study are consis-tent with the argument that explicit and associative processes runin parallel (Anderson, 1993; Anderson & Schunn, 2000; Maier,1973). Total learning is likely to be a result of associative and expli-cit learning processes and, as the results of our study suggest, thelearning processes that are best suited to skill acquisition may de-pend upon the different task conditions that have to be mastered.Researchers have yet to figure out how to capture and differentiateexplicit and associative learning processes simultaneously on the

Table 4Regression results for moderated mediation effects on favorable task condition rule transfera.

Predictor b SE t p

Exposure to different task conditionsConstant 46.942 7.098 6.613 .000Sex .028 3.902 .007 .497Self-efficacy �.157 .116 �1.352 .092Feedback specificity �24.180 3.775 �6.405 .000

Favorable task condition rule transferConstant 80.289 17.862 4.495 .000Sex �12.729 5.604 �2.271 .015Self-efficacy �.055 .170 �.322 .375Feedback specificity 2.559 7.575 .338 .369Explicit information processing .267 .212 1.259 .108Exposure to different task conditions .018 .586 .031 .488Explicit information processing by exposure to different task conditions �.012 .007 �1.837 .037

Explicit information processing Bootstrap indirect effect Boot SE Boot Z Boot p

Conditional indirect effect at a range of values of explicit information processing14.00 1.397 15.833 .088 .46519.85 3.373 14.658 .230 .40925.70 5.348 13.515 .396 .34631.55 7.324 12.414 .590 .27837.40 9.300 11.366 .818 .20743.25 11.276 10.388 1.086 .13949.10 13.252 9.500 1.395 .08254.95 15.228 8.732 1.744 .04160.80 17.203 8.116 2.120 .01766.65 19.179 7.689 2.494 .00672.50 21.155 7.484 2.827 .00278.35 23.131 7.519 3.076 .00184.20 25.107 7.791 3.223 .00190.05 27.082 8.276 3.272 .00195.90 29.058 8.940 3.250 .001

101.75 31.034 9.747 3.184 .001107.60 33.010 10.663 3.096 .001113.45 34.986 11.664 3.000 .001119.30 36.962 12.728 2.904 .002125.15 38.937 13.843 2.813 .002131.00 40.913 14.995 2.728 .003

a n = 44. Unstandardized regression coefficients are reported. Bootstrap sample size = 5000.



same task. Future research designed to compare the determinantsand effects of the two types of learning processes occurring concur-rently on the same task awaits the development of an appropriateresearch paradigm.

Three causal pathways for the effects of feedback specificity ontransfer have now been identified; explicit information processingfrom the current study, as well as exploratory behavior and expo-sure to different task conditions from the work of Goodman andWood, 2009, 2004; Goodman et al., 2004). So far, this researchprogram has taken a decidedly informational approach to feedbackeffects. However, the informational processes, in combination withexisting knowledge about feedback’s motivational effects (e.g.,Fedor, 1991; Ilgen et al., 1979; Kluger & DeNisi, 1996; Locke &Latham, 1990), could provide the foundation for a model of thecognitive motivational processes of learning dynamic, complextasks. The model also could be expanded to explain learning effectsfor a range of contextual determinants, such as the types of goalsassigned (e.g., learning versus performance); the goal orientations,regulatory focus, and other individual differences of trainees; thebehavior and characteristics of organizational leaders; organiza-tional support for training; and characteristics of the transferenvironment.

Practical implications

Our results suggest that it is important to consider exactly whatwe want trainees to learn when designing feedback interventions.If we want trainees to learn to respond to both favorable and unfa-vorable task conditions, then they need ample exposure to bothtask conditions during training, and they should engage in highlevels of explicit information processing. For example, if we wantsales people to learn to close deals that are moving along welland to learn to cope with setbacks in their business transactions,feedback on their sales tactics should not be so specific that it pre-vents errors from occurring. Less specific feedback will be morelikely to expose trainees to task conditions that give them opportu-nities to learn to correct errors.

High specificity feedback during training will foster less explicitinformation processing than low specificity feedback and movetrainees into positions where they are primarily exposed to favor-able task conditions. This process creates a practical problem, be-cause our results showed that explicit information processing isbeneficial when high specificity feedback is given and traineesare exposed to favorable task conditions.

One option might be to provide less specific feedback and con-trol trainees’ levels of exposure to favorable and unfavorable taskconditions through real or simulated task assignments, much likewe did during the transfer phase of our experiment. For example,sales trainees could be informed of their sales performance, withno additional information linking their actions to their perfor-mance levels or suggesting more effective actions. This type oflow specificity feedback could be given on a variety of assignmentsthat differ in the likelihood that obstacles will occur. They couldwork on selling products that sell themselves or with customerswho are easy to manage in addition to products that are difficultto sell or customers that are challenging to manage. The challeng-ing assignments would ensure that trainees would have to worktheir way out of failures and setbacks. However, the less challeng-ing assignments are likely to lead to insufficient amounts of expli-cit information processing, because they will expose trainees tofavorable task conditions. Therefore, explicit information process-ing may need to be induced, perhaps through training in meta-cog-nitive processing, providing feedback on trainees’ informationprocessing activities, or by motivating trainees to engage in explicitinformation processing.

Contributions, strengths, and limitations

The current study has several strengths and limitations. Themost significant strength is that it builds on and extends a sys-tematic program of research initiated by Goodman and Wood(2004, 2009; Goodman et al., 2004) to explore the mechanismsthrough which feedback affects training and transfer. Therefore,the study contributes to a growing body of findings on the cau-sal mechanisms that link feedback to learning and transfer,which will provide the platform for more applied research intothe effects of feedback interventions. Our study was the first todirectly examine the effects of feedback on explicit informationprocessing, providing the first actual test of the guidancehypothesis (Christina & Bjork, 1991) and adding to the two path-ways previously examined by Goodman and Wood. Knowledgeof the results for any single pathway gives an incomplete pictureof how feedback specificity affects learning and subsequenttransfer.

As part of the program design, Goodman and Wood made sig-nificant investments in the research paradigm, which providesthe capability to objectively manipulate the specificity and otheraspects of the feedback content, obtain objective measures oftransfer, and study learning on a task that provides an analogto the properties of many complex, dynamic organizational tasksthat make learning difficult and, hence, interesting to study. Thechoice of research design and continued use of the establishedparadigm reflects the primary concern with internal validity.External validity is a secondary, but not neglected, concern atthis stage of the research program. Management textbooks al-ready contain a plethora of generalizations, many of which arebased on the results of studies with low internal validity andare inconsistent with results of other studies. Therefore, a solidplatform of robust results is needed from which to develop validinterventions. The choice of task and investment in the experi-mental paradigm was done to maximize potential generalizabil-ity, within the constraints imposed by the primary focus oninternal validity.

Despite the primary focus on internal validity, we believe ourresults should generalize to many other complex tasks, particu-larly those that have conditional decision rules and require train-ees to figure out which decision rules operate under certaincircumstances. However, the decision options were well struc-tured and readily available to participants in the simulation,and we do not know whether the results would hold when par-ticipants have to search for and identify options. Further, our re-sults should apply to computer-based training, softwareprograms, and high-tech equipment in organizations, in whichcomputers are primary feedback sources (Goodman & Wood,2004). However, we do not know the extent to which our resultswill generalize to human feedback sources, such as trainers, coa-ches, and supervisors, with which issues such as source credibility(Fedor, 1991) and inconsistencies in feedback provision are morelikely to come into play.

The verbal protocol method also presented both strengths andweaknesses. It enabled access to participants’ internal thoughtsand allowed us to assess how they processed information overmultiple training trials. Additionally, the content coding of the ver-bal protocols provided an independent and non-reactive assess-ment of information processing, not possible with morecommonly used self-reports. At the same time, verbal protocolsmay represent incomplete records of thoughts (Ericsson & Simon,1980, 1993), so the amount of information processing may havebeen underestimated. In addition, the content of verbal protocolsis limited to the information held in short-term memory (Ericsson& Simon, 1980, 1993) and, therefore, captures only explicit infor-mation processing.



Appendix A

See Table A1.

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Table A1Component variables for the composite explicit information processing variable.

Variables Original sources Our variable definitions

Planning Berardi-Coletta et al. (1995) andKeith and Frese (2005)

Participant describes a plan for future decisions on subsequent trials

Predictive Testing Burns and Vollmeyer (2002) andVan der Linden et al. (2001, 2003)

Participant explores the system, trying out decision options in a structured and reflectivemanner by explicitly making predictions about what will happen (i.e., developinghypotheses), providing a rationale for the prediction and testing the hypotheses

Non-predictive Testing Burns and Vollmeyer (2002) andVan der Linden et al. (2001, 2003)

Participant purposely chooses a decision option to perform a test of the effects of a decisionoption, without including a specific prediction

Experimentation despite goodperformance or correctdecision

Participant indicates that s/he elects to change his/her course of action (i.e., one or moredecisions from the previous trial are changed) despite good or improved performance or thefact that previous decisions were correct. The participant may indicate that s/he is changinghis/her approach to test different options, learn, figure out the rules, etc

Lower-level causal reasoning Isenberg (1986) Participant makes statement indicating causal reasoning (e.g., because, so, maybe, however)when making a decision. The statement indicates fairly superficial reasoning by basingdecisions solely on performance levels or changes or characteristics of the employees

Higher-level causal reasoning Isenberg (1986) Participant makes statement indicating causal reasoning (e.g., because, so, maybe, however)when making a decision. The statement indicates higher-order reasoning by includingconsideration of how decision options operate to impact employee performance (e.g., hardgoals are motivating; process feedback provides information about how to do a job) and/or areasoned basis for a decision (e.g. job allocation based on fit between job characteristics andemployees’ skills and interests; to develop employees’ skills)

Performance evaluation Berardi-Coletta et al. (1995) andKeith and Frese (2005)

Participant comments on the performance levels of his employees or whether or notdecisions were correct, without evidence of causal reasoning



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Feedback specificity, information processing, and transfer of training

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