How Did you Do That? Exploring the Motivation to Learn

r Academy of Management Discoveries2021, Vol. 7, No. 1, 15–39.Online onlyhttps://doi.org/10.5465/amd.2018.0217

HOW DID YOU DO THAT? EXPLORING THE MOTIVATION TOLEARN FROM OTHERS’ EXCEPTIONAL SUCCESS

RYAN W. QUINN*University of Louisville College of Business

CHRISTOPHER G. MYERS*Johns Hopkins University

SHIRLI KOPELMANUniversity of Michigan

STEFANIE SIMMONSEnvision Physician Services

In this article, we explore how perceptions of other people’s exceptional successinfluence individuals’motivation to learn—a relationship that has been surprisinglyunexplored within the broad literature on learning in organizations. Our researchreveals, across two distinct samples and methodologies, that an individual’s moti-vation to learn is higher when they perceive performance by another person to bemore exceptionally successful, as compared to perceiving the other’s performance asa more “normal” success. We also observe, in line with prior research, marginalsupport for the notion that motivation to learn is higher when individuals perceiveothers’ performance as more of a failure; thereby suggesting a curvilinear relation-ship between perceived performance and motivation to learn. Our second studydemonstrates that the relationship between others’ performance and the motivationto learn is mediated by interest and moderated by surprise. We discuss the impli-cations of these results for provoking new theorizing, measurement, and practicalimplementation of learning in organizations.

* Authors contributed equally.We would like to thank Jerry Davis, David Mayer, and

participants at the 2015 conference on positive organiza-tional scholarship who gave us feedback on ideas in this

manuscript. We are also grateful to Uri Simonsohn for hisadvice onmethods, and to our many other colleagues whoprovided helpful suggestions and support throughout theresearch process.

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https://doi.org/10.5465/amd.2018.0217

https://players.brightcove.net/4095259328001/default_default/index.html?videoId=6223249910001

Organizational life is rife with opportunities for indi-viduals to learn (e.g., Cohen&Bacdayan, 1994;Weick &Ashford, 2001), but learning is unlikely to happen, or isunlikely to be effective, if individuals are not motivatedto learn (Argyris & Schon, 1996; Noe, 1986). One factorthatmayhaveasignificant impactonmotivation to learnis encountering exceptionally successful performance,asevident in thecountlessbooks, articles, speeches, blogentries, and other media that present exceptional suc-cesses asmodels fromwhich to learn (e.g., Collins, 2001;Willink & Babin, 2017). The market for such media im-plies amotivation to learn fromexceptional success, butresearchers have both criticized (e.g., Denrell, Fang, &Zhao, 2013; Denrell & Liu, 2012) and seen value (e.g.,Lilien, Morrison, Searls, Sonnack, & von Hippel, 2002;Starbuck, 1993) in learning from exceptional success.Their assessments of such learning have focused pri-marily on the content of what is learned, rather than onwhether and how this exceptional success motivateslearning. To our knowledge, no one has explored thequestion of whether, when, and why an individual’smotivationtolearnfromexceptionalsuccessdiffers frommotivation to learn from “normal” levels of success orfailure. Given themany factors that demotivate learningfrom others in the workplace (e.g., Argyris & Schon,1996; Darley & Fazio, 1980), understanding a phenom-enon that, in popular media at least, appears to haveimplications for motivation to learn would be of valueto organizational scholars and practitioners.

People may be motivated to learn from others’exceptional successes, but learning from media ac-counts is not the same as learning from coworkers’exceptional performance in similar work tasks. Forexample, negative or positive interpersonal rela-tionships may complicate the desire to learn fromcoworkers. Moreover, there may be cases in which acoworker’s exceptional success demotivates learn-ing in an individual because they are intimidated bythat coworker’s success, or because they see the co-worker’s success as irrelevant. Coworkers learnabout each others’performance in similarwork tasksthrough the conversations that ensue when man-agers post employee performance (Nordstrom,Lorenzi, & Hall, 1991), through internal or external

websites dedicated to sharing stories about work(e.g., Facebook’s “Workplace” software; workplace.com), or through the stories that spontaneouslyemerge when people work together on challengingprojects (Orr, 1996). Yet we know of no theory orempirical research on how others’ exceptional suc-cess affects individuals’motivation to learn at work,leaving the presence and nature of this relationshipopen for empirical exploration.

Because motivation to learn is just one type ofmotivation,wecanderive some intuitions abouthowexceptional success affects motivation to learn fromgeneral theories of motivation; however, these the-ories actually suggest conflicting intuitions. For ex-ample, if people lose their motivation to performwhen they believe that an outcome is impossible forthem to achieve (Locke & Latham, 1990), they maylose their motivation to learn when others succeedexceptionally at an activity that they believe theycould never learn to perform that well. In contrast, ifanother person’s exceptional success acts as an ex-istence proof (Weick, 2007) that extraordinary per-formance is possible (when an individual previouslybelieved it to be impossible [see Vroom, 1964]), thepossibility of learning to perform at that level mayhave a significant positive impact on an individual’smotivation to learn.

We conducted research to first compare how otherpeople’s exceptional success influences individuals’motivation to learn, relative to other levels of per-formance that people achieve (specifically, normallevels of success and failure), and then to explorepotential mediators and moderators of this rela-tionship between other people’s performance andone’s own motivation to learn. This analysis offerssome “first suggestions” (Bamberger, 2018) for un-derstanding individual motivation to learn whenencountering others’ exceptionally successful per-formance as compared to normal success or failure.We perform this analysis using data from a fieldstudy with multiple emergency departments andfrom an online scenario-based study. We find thatperceptions of others’ performance—ranging fromfailure, through normal success, to exceptional suc-cess—display a curvilinear relationship with indi-viduals’motivation to learn in both studies. We alsofind that this relationship is mediated by individ-uals’ feelings of interest in, and moderated by thelevel of surprise they feel about, others’performance.Based on these findings, we theorize about others’performance and the motivation to learn in severalimportant ways, most notably providing coalescingevidence for a nonlinear influence of others’ perfor-mance (as it ranges from failure, through normalsuccess, to exceptional success) on individuals’motivation to learn in work organizations that

Author’s voice:What motivated you to undertakethis research? (Part 1)

Author’s voice:What motivated you to undertakethis research? (Part 2)

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highlights the motivating value of encounteringothers’ exceptional success.

THE MOTIVATION TO LEARN FROM OTHERS’EXCEPTIONALLY SUCCESSFUL PERFORMANCE

AT WORK

Recent theorizing onvicarious learning has arguedthat individuals’motivation to learn is an importantcomponent of learning from others’ experiences be-cause motivation to learn influences the extent towhich they spend time and energy making senseof, and drawing lessons from, others’ experience(Myers, 2018). This motivation to learn reflects theeffort and persistence invested into acquiringknowledge and skills (Noe, 1986), and is necessarybecause learning can be difficult, embarrassing,complicated, or inconvenient (e.g., Staw, Barsade, &Koput, 1997; Westen, Pavel, Harenski, Kilts, &Hamann, 2006). Given these obstacles, learningfrom others’ performance is less likely to occur ifpeople are not sufficiently motivated.

In the present research project, we explorewhether, when, and why encountering others’ ex-ceptional success can impact motivation to learn,relative to encountering other levels of performance(i.e., a more typical success or a failure). Others’performance can varywidely, including not only thefrequently studied distinction between failed andsuccessful performance but also ranging beyond thistraditional threshold to more exceptionally positiveperformance. Indeed, successful performance atwork can manifest to varying degrees, ranging fromacceptable or typical performance (“normal suc-cess”) to unusually positive performance that ex-ceeds typical performance (“exceptional success”).Yet the distinction between success and exceptionalsuccess tends to be more ambiguous than the dis-tinction between failure and success. Thedistinctionbetween success and failure depends on whetherperformance exceeds or falls below a reference point(Lewin, Dembo, Festinger, & Sears, 1944), whichmay be a preset goal, a previous level of performanceachieved, or one’s own performance relative to theperformance of others. Reference points such asthese tend to be relatively explicit, and known oreasily discoverable by others. The distinction be-tween success and exceptional success also dependson exceeding a threshold. However, in this case, thethreshold is the individual’s perception of what istypical relative to what is exceptional, and, unlikethe distinction between success and failure, thisthreshold is seldom prespecified, but is instead im-plicit and intuited from experience. Nor is thisthreshold as likely to be publicly accepted or sharedwithin a given social group. Because it is intuited

from experience, it is likely to be idiosyncratic to theindividual perceiving the performance.

If individuals distinguish normal performancefrom exceptional performance, the relationship be-tween perceptions of others’ performance and anindividual’s motivation to learn may not be linearacross the performance spectrum (i.e., ranging fromfailure, through normal success, to exceptional suc-cess). For example, if others’ exceptional successserves as an existence proof (Weick, 2007), showingthat exceptional success can be achieved, andtherefore challenging an individual’s expectationsabout what is possible, then perceiving that excep-tional success would indicate a significant change inhow the individual thinks. This change should, inturn, have a correspondingly strong impact on howmotivated the individual is to learn from another’ssuccess, because motivation is influenced by thelikelihood of success, especially if improved per-formance also implies improved rewards (Vroom,1964). Thus, the relationship betweenperceptions ofperformance and motivation to learn may be in-creasingly positive as others’ performance exceedsthe bounds of “typical” success, and could be con-sidered more exceptional, because it generates anincrease in the interest people have in the otherperson’s performance. In this sense, exceptionalperformance is likely to interest people because it isoften novel or complex while still being compre-hensible (Silvia, 2008).

In contrast, perceiving others’ performance assurpassing the limit of what previously seemedpossible may also have a nonlinear effect on moti-vation to learn, because a person may believe thatlearning to achieve such performance would be ex-ceptionally difficult. Exceptionally difficult activi-ties can reduce people’s motivation if the effortnecessary to learn and achieve seems impossible tothem, or unreasonable to pursue (Locke & Latham,1990): For example, peoplemay think, “Just becausesomeone else was able to achieve that performancedoesnotmean that I canor should learnhow todo it.”They may also be averse to failure in these contextsand disengage from even wanting to learn how toperform difficult tasks. If this logic prevails, it wouldimply a curvilinear relationship between percep-tions of others’ performance and individuals’ moti-vation to learn, with the individual being moremotivated by seeing others achieve normal amountsof success, relative to seeing failure (e.g., Wood &Bandura, 1989), but in turn being less motivated byseeing someone’s more exceptional success (relativeto the normal success).

These potential nonlinear effects of perceptions ofothers’ performance on one’s own motivation tolearn suggest that it may be valuable to compare the

2021 17Quinn, Myers, Kopelman, and Simmons

effects of perceived exceptional success with per-ceived normal success and perceived failure onmotivation to learn. Is an individual’s motivation tolearn uniquely affected when others’ performance isperceived as not only acceptable (i.e., as a successrather than a failure) but actually exceptional? Ordoes this motivation to learn continue to grow (ordiminish) linearly through these performance levels(i.e., failure, success, and exceptional success) inspite of the violated expectations, new possibilities,or desirable capabilities and outcomes latent in ex-ceptional success? Taken together, our review ofextant research suggests that the overall relationshipbetween perceptions of others’ performance and anindividual’s motivation to learn—especially whenthat performance is exceptional—is poorly under-stood and would benefit from exploratory research(Bamberger, 2018). We explored this across two tasksettings. First, we considered the effect of perceptionsof others’ failure, success, and exceptional success onindividuals’ motivation to learn in a sample of emer-gency department clinicians. Second, we used amorecontrolled task setting to replicate the effect found inthe first study and explore interest and surprise aspotential mediators and moderators.

STUDY 1: FIELD STUDY

Our first study was a field-based, multi-weekexploration of motivation to learn among doctors,physician assistants (PAs), and nurse practitionerswho work in emergency departments. Utilizingstories of failure, success, and exceptional success,and measuring learning motivation among individ-uals as theywere actively engaged in their day-to-daywork (in this case, treating patients), both enabled usto explore perceptions of others’ failure, success, andexceptional success in a natural setting and providedexternal validity for the relationship between others’performance and individuals’motivation to learn.

Participants and Procedures

The participants in our first study were cliniciansfrom the Emergency Physicians Medical Group(EPMG), a company that staffs emergency depart-ments. The study proceeded in two stages. The pre-liminary stage involved creating ameans for ensuringawide range of perceptions of others’ performance by

generating and collecting stories of real failures, suc-cesses, and exceptional successes experienced in theemergencydepartment setting. In the primary stage ofthe study we examined clinicians’ perceptions ofothers’ performance in these stories, and their moti-vation to learn, over a six-week period.

Preliminary stage: Creating a means for ensur-ing a breadth of perceptions. To ensure that therewould be variance in our participants’ perceptions,we based our study on what O’Keefe (2003) called aClass I research claim regarding the effect of mes-sages on psychological states. A Class I researchclaim asserts that psychological states (such as theperception of someone else’s performance) havespecific effects on variables of interest (such as themotivation to learn), and even though these statesmay be caused by specific messages (such as an ex-perimental manipulation), the messages are onlyimportant inasmuch as they create variance in thepsychological state. It may be interesting to knowwhether the manipulation had the intended effect,but the purpose of the manipulations was only tocreate variance in perceptions of performance inorder to examinehow these perceptionswere relatedto the motivation to learn. Therefore, the study weconducted is not an experiment, even though weused manipulations of the messages presented toparticipants in the primary stage: we used thesemanipulations to create the variance we were inter-ested in studying with the independent variables.

The messages we constructed were stories frommedical directors andclinician leaders at EPMGwhohad emergencymedicine experience.We couldhavecreated scenarios ourselves, but we believed thatactual reports would be more authentic, and thatstories from colleagues in the same organizationwould be more meaningful to our participants. Weasked these leaders (who were not included as par-ticipants in theprimary study) towrite up the story ofan experience in the emergency department setting,and specifically to describe the context, characters,plot, outcome, and “moral of the story,” or lesson ofthe experience they reported.We asked some leaderstowrite about failed experiences, some towrite aboutsuccessful experiences, and some to write about ex-ceptional successes. We collected 11 exceptionalsuccess stories, 10 success stories, and 10 failurestories. Many of the stories used jargon or uncom-mon acronyms, were told in a way that assumedreaders would know things they might not neces-sarily know, or simply did not flow well. Therefore,an emergency medicine physician on our researchteam edited the stories for readability, without alter-ing their content.

We ran pilot tests with the stories to confirm theygenerated variance in perceived performance. We

Author’s voice:If you were able to do this studyagain,what if anythingwould youdodifferently?








asked two emergency medicine clinicians, notemployed by EPMG, to rate the performance in thestories using a sliding scale from 0 to 120 with an-chors of “A complete failure” covering the rangefrom 0 to 12, “A failure” from 13 to 30, “Less successthan desired” from 31 to 50, “An acceptable out-come” from 51 to 70, “A success” from 71 to 89, “Avery successful outcome” from 90 to 108, and “Anoutcome that exceeds all expectations” from 109 to120. Scholars often use 100-point scales to draw onthe intuitive experience of people who have atten-ded schools where grades are based on percentagesto judge success (e.g., Pajares, Hartley, & Valiante,2001). We used this same intuition to suggest that a100-percentage point scale indicates a range of fail-ure and success, but that as performance approachesand exceeds 100, that performance would be con-sidered exceptional. This broad scale also allowedfor more variance in perceptions of performancecompared to a traditional 5- or 7-point scale, evenwhen those perceptions fall within the same generalcategory of performance. We also asked the raters toinclude feedback on the clarity of the stories.

We checked the interrater reliability of the scoresbecause reliable ratings at different levels implied thatwewouldgenerate thevarianceweneeded inouractualstudy. Interrater reliability fell short of the standard0.70cutoff, and feedback from our raters suggested that thestorieswerenot as clear as theyneeded to be. Therefore,we used their feedback to edit the stories further. Thisediting includedreplacing less-commonacronymswiththe words for which they stood, improving grammar,and replacing even more of the medical jargon. Ourraters indicated that simpler languagewasneeded, eventhough the writers and readers of the stories were allmedical professionals. We then had two new emer-gency medicine clinicians rate the stories using thesame scale. These raters achieved an interrater reliabil-ity of 0.88.

Our raters agreed on their ratings of performance inthe stories; however, therewere a few stories the ratersassessed as demonstrating a failure, success, or ex-ceptional successbut that theEPMGleaderswhowrotethe initial stories had categorized differently (wetreated storieswitha ratingbetween0and40as failure,storieswith a rating between 41 and 90 as success, andstorieswith a rating between91 and120as exceptionalsuccess, based on the anchors on our scale). To tightenthemanipulationandminimizeconfusionwedroppedthese stories in which our raters did not align with theauthors about the level of performance. This was notbecause of the content of the stories but becauseour goal was to produce stories that maximize thelikelihood of participants having failure, success, andexceptional success perceptions, while still allowingfor plenty of variance in those perceptions. Eight

stories of exceptional success, six stories of success,and seven stories of failure met these criteria. Becauseparticipants would read one story of one type eachweek,weneeded tohave the samenumber of stories ineach category. Therefore, we included the six successstories, along with six failure stories and six excep-tional success stories, to use as stimuli in our study.

The six stories within each performance category(success, failure, and exceptional success) generatedin this preliminary stage of research varied in termsof features such as characteristics of the protagonists(e.g., gender) and thepatient (child versus adult), andthe length of the story. This variation provided anoverarching set of stimuli that would reflect the cat-egory and ensure that findings would not be an arti-fact of a particular element of the story.

Primary stage: Six-week story study.Wecontactedclinicians employed by EPMG in emergency depart-ments throughout theMidwest United States to requestparticipation. Fifty-five clinicians from 21 differentemergency departments participated in at least the ini-tial survey (from the set of surveys described below).Twenty-four, or44%,of theclinicianswhoparticipatedwere female. We confirmed with EPMGmanagers thatthis is the same proportion of female cliniciansemployed by EPMG overall. The leaders of EPMG of-fered points toward clinicians’ bonus pool for partici-pating, which, depending on end-of-year calculations,could be worth $600. The 55 participating clinicianswere randomly assigned to read stories from one of thethree performance categories generated in the prelimi-nary stage of this study. These stories, and the oppor-tunity to learn from them,were embedded inclinicians’normal weekly routine through a six-week series ofonline surveys.

Participation in the study required clinicians to fillout an initial survey before the six weeks began and anadditional six surveys in the subsequent sixweeks, onein each week. The initial survey assessed baselinemeasures of learning tendencies, including motivationto learn and learning behaviors, aswell as demographicdata. The measures of perceptions of performance andmotivation to learn were collected in the six weeklysurveys. Eachweek, a clinicianwould be sent an onlinesurvey containing one new story, always from the samecategory (failure, success, or exceptional success). Ineachweeklysurvey,participantsweregivenopenspaceto reflect inwriting on thatweek’s story, includingwhatthey felt the story was about, whether and how theycould relate to the story, and what they would havedone in the situation depicted. After reflecting, theywere asked to rate their perception of the performancein the story (using the 120-point rating scale describedin thepreliminary stage of this study), and responded to7-pointLikert-scalequestions thatmeasuredothermainstudy variables.


Measures

Focal measures. The focal variables in this studywere perceptions of performance and motivation tolearn. We measured perceptions of performance byasking participants to rate howwell they thought theclinician in the story performed on the same 120-point sliding scale used by our raters in pretesting.Ratings were then rescaled to facilitate the analysisby dividing by 100, and thus ranged from0 to 1.2.Wemeasured motivation to learn using three itemsadapted from Noe and Schmitt (1986) and Colquittand Simmering (1998). Reliability for this measurewasa5 .91.All perceptual scales in our research canbe found in Appendix A.

Control variables. We collected additional mea-sures in the initial survey (administered before any ofthe participantswere exposed to the story conditions)in order to both rule out alternative explanations forour findings and control for appropriate individualcharacteristics. Specifically, we captured partici-pants’ baseline motivation to learn using the samescale as on weekly surveys, other than beginning thefirst item with “I exert” instead of with “This week, Iintend to exert” (a5 .74).We included this variable toexamine howmotivation to learn deviates from one’stypical motivation to learn after each weekly expo-sure to a story. We measured participants’ learningbehaviors (a5 .94) to account for changedmotivationrelative to effort that clinicians were already puttinginto learning (Edmondson, 1999).

For each weekly story, we also measured empathyfor the story protagonist in order to account for dif-ferences in participants’ resonance with, or per-ceived similarity to, particular story protagonists.The reliability for this scale was 0.52, which failed toreach the traditional 0.70 threshold, even though thisscale has been found reliable in prior research(Parker & Axtell, 2001). In spite of this problem, wedecided to use this scale as constructed because it isan established scale and reliabilities as low as 0.50can be considered acceptable if the construct’s con-ceptualization is valid (John&Benet-Martınez, 2000;Pedhazur & Schmelkin, 1991). Additionally, confir-matory factor analysis (using MPlus [Muthen &Muthen, 2005]) showed adequate support for thefactor structure of this measure alongside our mea-sure ofmotivation to learn (the onlyothermulti-scalemeasure in the study), with the two-factor modeldemonstrating good fit (RMSEA5 .02, SRMR 5 .03,CFI 5 .999, TLI 5 .998).1

As additional controls, we created a binary vari-able for clinicians who were physicians (MedicalDoctor [MD] or Doctor of Osteopathy [DO] degree)versus nonphysician clinicians (PAs or nurse prac-titioners) to control for the possibility that differentpositions may have different expectations for learn-ing. We also controlled for those who were assignedto read exceptional success, success, or failurestories (using dummy variables), because eventhough the purpose of the descriptions of perfor-mance was to ensure that there was variance in per-ceptions of performance, it was possible that thestories may have influenced motivation to learnthrough mechanisms other than perceptions ofperformance. For example, the positive languagein exceptional success stories, or the negative lan-guage in failure stories, may have led people tosubconsciously frame the stories as more or less in-teresting, irrespective of the extent of success orfailure the participants perceived the protagonists inthe story to have experienced. Thus, including thesecontrol variables (dummy variables for the excep-tional success and failure stories, with success as theomitted category) accounted for the possibility thatthese descriptions would affect motivation to learnthrough alternative mechanisms.

Analysis

Procedure. We explored possible linear and cur-vilinear relationships between perceptions of per-formance and motivation to learn at the weeklyresponse level (individual week, which is a within-person unit of analysis), with a primary sample of289 clinician-week observations from 53 uniqueclinicians (whocompletedat least oneweekly survey,out of the 55 who completed the presurvey). We an-alyzed our data with mixed-effects models, utilizingtheMIXEDcommand inSPSS24 (specifying repeatedobservations for participants’ responses over the sixweeks and a compound symmetric covariance struc-ture). Using this approach, rather than a repeated-measures analysis of variance (ANOVA), allowed usto include the effects of time-varying predictors (e.g.,weeklymeasures of perceived story performance andempathy) and to avoid excluding participants whoweremissing one ormoreweekly responses (both keylimitations of a repeated-measures ANOVA) whilestill accounting for the nonindependence betweenparticipants’ responses over time (Bagiella, Sloan, &Heitjan, 2000). Specifically, we constructedmarginal(i.e., population-average) models, rather than fullrandom-effects models, as our interest was in theglobal effect of our variables (vs. subject-specific ef-fects [Gardiner, Luo, & Roman, 2009]). In all analyses,fixed effects for the week (for each of the six weeks of

1 We used a different measure of empathy in our secondstudy that was more reliable, mitigating the risk of unreli-able conclusions based on this specific measure.


the study), the randomly assigned story condition(failure, success, or exceptional success story), andthe binary variable controlling for physician (vs.nonphysician) clinicians were included as categori-cal factors; fixed effects were estimated for allremaining variables as continuous covariates.

Common method bias could have been a concernin our model if we found main effects and no curvi-linear relationship because, as Siemsen, Roth, andOliveira (2010) have shown, common method vari-ance does not create artificial quadratic effects andinteraction effects. In fact, if we were to find a qua-dratic relationship and there was common methodvariance, then the results of our analysis wouldprovide “strong” evidence for the existence of such arelationship, because the relationship would besubject to significant deflation in the presence ofcommon method variance (Siemsen et al., 2010:468). However, tominimize potential problemswithcommon method bias, we ensured anonymity, keptparticipants blind to the purpose of the study, andused psychologically separate measures (e.g., moti-vation to learn is self-focused while the other twomeasures are other-focused).

Data quality. The means, standard deviations,correlations, and reliabilities (where appropriate) forStudy 1 variables are presented in Table 1. We calcu-latedcorrelationsbetween the individualvariablesandthe individual-week variables by putting each indi-vidual’s values in each individual-week row for therespective individuals, and then counterweightedthese observations by the number of weekly responseswe received fromeachof these individuals (seeCullen,

Parboteeah, &Hoegl, 2004;Quinn&Bunderson, 2016).The significant correlations between the binary vari-able for physicians and the dummy variables for thefailure and exceptional success conditions (–0.21 forexceptional success and 0.20 for failure) are artifacts ofthe random distribution of participants. Our randomdistribution led to one PA being in the failure condi-tion, three PAs in the success condition, and one nursepractitioner and five PAs in the exceptional successcondition. Thesewere small differences overall, but ina sample of 53 participants these numbers were suffi-ciently different from each other to affect the correla-tions. Higher correlations also occurred for similarreasons between the baseline measures (learning be-haviors and motivation to learn) and the failure (0.20and 0.26), success (0.16 and 0.10), and exceptionalsuccess conditions (–0.36 and –0.35). While thesecorrelations are entirely plausible in a random sampleof 53 individuals, they provide additional impetus forusing these variables as controls in our models to ac-count for any anomalies.

Although it was not necessary in a Class I study ofhow psychological states influence variables of in-terest (O’Keefe, 2003), we nevertheless conductedregression analyses to examine whether the stories offailure, success, and exceptional success affectedperceptions of performance. Using the controls de-scribed above, we found that failure stories had anegative impact (B 5 –0.52, p , .001), and excep-tional success stories had a positive impact (B5 0.14,p , .001), on perceptions of performance (stories ofsuccess servedas theomittedbasecondition).Wealsoexaminedhistograms of residuals fromour fullmodel

TABLE 1Study 1 Descriptive Statistics

Variables Mean SD 1 2 3 4 5 6 7 8 9

Control Variables1. Exceptional success storiesa,c 0.34 0.47 –

2. Success storiesa,c 0.34 0.47 20.51*** –

3. Failure storiesa,c 0.32 0.47 20.49*** 20.49*** –

4. Physiciana,c 0.83 0.38 20.21*** 0.01 0.20** –

5. Baseline motivation to learn 6.22 0.67 20.36*** 0.16** 0.20** 20.25*** (0.74)6. Learning behaviorsa 5.48 0.88 20.35*** 0.10† 0.26*** 20.22*** 0.58*** (0.94)7. Empathyb 5.62 0.96 0.19** 20.03 20.16** 20.11 † 0.12* 20.06 (0.52)

Independent Variable8. Perceptions of performanceb 0.76 0.36 0.59*** 0.24*** 20.83*** 20.32*** 20.18** 20.25*** 0.24*** –

Dependent Variable9. Weekly motivation to learnb 6.02 0.86 20.13* 0.04 0.09 20.15** 0.47*** 0.31*** 0.40*** 20.04 (0.91)

Note: n5 289.a Individual-level data (n 5 53 individuals).b Week-level data (n 5 289 individual-weeks).c Dummy variable.†p < 0.10*p < 0.05

**p < 0.01***p < 0.001


predicting motivation to learn, which revealed oneoutlying observation. Upon further examination, thisoutlier appeared to reflect a genuine reaction by oneparticipant to one of the stories, and as there was notheoretical reason to remove it, we retained this ob-servation in all of the analyses reported below.

Results

Our purpose for having emergency medicine cli-nicians read stories of failure, success, and excep-tional success was to explore how perceptions ofperformance influence motivation to learn, andspecifically to test for the presence of a nonlinearrelationship. We examined this relationship byconstructing the three models presented in Table 2.In Model 1, we regressed motivation to learn fromweekly stories on the controls described above, as wellas oneachweek (withweek6as thebase condition) andthe story conditions (failure and exceptional success,with success as the omitted base condition). InModel 2,we included the main effect for perceptions of perfor-mance, which revealed no significant linear effect ofperformance perceptions on motivation to learn (B 5–0.11, p5 .47). InModel 3, we added the squared termfor perceptions of performance, to test the presence

of a curvilinear relationship between perceptions ofperformance andmotivation to learn, which revealed asignificant main effect (B 5 -1.47, p 5 .001) and a sig-nificant quadratic effect (B 5 0.95, p 5 .002) of per-ceptions of performance on motivation to learn. Tocheck for the presence of a more complex nonlinearrelationship, we also constructed separate models withcubic and quartic terms for perceptions of others’ per-formance, which were both nonsignificant as thehighest-order term in their respective models (BCubic 5–.57,p5 .51;BQuartic53.63,p5 .20), suggesting that thecurvilinear (squared) effect more accurately reflectedthe nonlinear shape of the relationship.

Because thecoefficient for thequadratic terminModel3 is positive, it suggests that the relationship betweenperceptions of others’ performance and individual’smotivation to learn isU-shaped.Weplotted this effect inFigure 1, revealing a curve where motivation to learn ishigherforstories thatareperceivedas failures, thenloweras perceptions of performance increase to the level ofnormal success, but rises again as perceptions of perfor-mance increase to exceptionally successful levels.

To be maximally conservative in our test of this rela-tionship, we also subjected our data to Simonsohn’s(2018) two-lines test of quadratic curves. This in-volves identifying a breakpoint in the curve, and

TABLE 2Study 1: Fixed-Effect Estimates Predicting Motivation to Learn

MODEL 1 MODEL 2 MODEL 3

Parameters Estimate SE T Estimate SE T Estimate SE T

ControlsIntercept 1.64 1.00 1.63 1.74 1.01 1.71† 2.16 1.00 2.16*Week 1a 20.05 0.08 20.57 20.06 0.09 20.74 20.09 0.09 21.07Week 2a 20.05 0.08 20.59 20.05 0.08 20.60 20.05 0.08 20.55Week 3a 20.06 0.09 20.69 20.07 0.09 20.77 20.03 0.09 20.29Week 4a 0.02 0.09 0.28 0.02 0.09 0.25 0.03 0.09 0.32Week 5a 0.00 0.09 0.05 0.00 0.09 0.01 0.05 0.09 0.57Week 6a 0.00b 0.00 . 0.00b 0.00 . 0.00b 0.00 .Exceptional success storiesa 0.06 0.24 0.27 0.08 0.24 0.34 0.02 0.23 0.08Failure storiesa 0.04 0.22 0.17 20.02 0.24 20.09 20.12 0.23 20.52Success storiesa 0.00b 0.00 . 0.00b 0.00 . 0.00b 0.00 .Nonphysiciana 0.02 0.26 0.06 0.03 0.26 0.11 0.00 0.26 20.01Physiciana 0.00b 0.00 . 0.00b 0.00 . 0.00b 0.00 .Baseline motivation to learn 0.55 0.17 3.23** 0.55 0.17 3.22** 0.53 0.17 3.17**Learning behaviors 0.08 0.13 0.61 0.08 0.13 0.60 0.08 0.13 0.65Empathy 0.10 0.03 2.74** 0.10 0.03 2.77** 0.11 0.03 3.24***

Main Effect TermPerceptions of performance 20.11 0.16 20.72 21.47 0.45 23.26***

Quadratic TermPerceptions of performance squared 0.95 0.30 3.21**

Note: n5 289.a Entered as a categorical variable.b This parameter is set to 0 because it is redundant.†p < 0.10*p < 0.05

**p < 0.01***p < 0.001


then estimating a linear effect from the low valuesof the independent variable up to the break point anda second linear effect from the break point to thehighervaluesof the independentvariable, and testingthese relationships for significant slopes in the ex-pected direction. Importantly, this breakpoint is notthe specific point at which the curve switches sign(i.e., the low point of the U-shape), but rather isset algorithmically with the goal of increasing powerfor detecting the two linear effects (Simonsohn,2018). To calculate this breakpoint, we were notable to use Simonsohn’s online calculator, whichuses his more precise “Robin Hood” algorithm tocalculate the breakpoint for the two lines (deliveringhigher power in detecting U-shaped relationships),because of our mixed-effects modeling approach.Instead, we calculated the breakpoint of our curve byconstructing a model with all of our controls and themain, squared, cubic, and quartic terms for perceivedperformance and then identifying the value of per-ceived performance that generated the most extreme(in this case lowest) predicted value of motivation tolearn from that model.2 This generated a breakpoint

of 1.09. Following the procedures for the two-linestest (i.e., constructing two interrupted regressions,one including the breakpoint in the first segment andthe other including it in the second, then reporting thecoefficients for eachsegment from the regressionwhereit included thebreakpoint [Simonsohn,2018]) revealedamarginally significantnegativeslopeon the lowerendof the perceived performance spectrum (i.e., from 0 to1.09; B5 –.30, p5 .10) and a significant positive slopeon at the highest end of the spectrum (i.e., from 1.09 to1.20; B 5 2.79, p 5 .04; see dashed lines in Figure 1).Taken together with the analysis above, we interpretthese results as suggesting that perceiving others tohave achieved more highly exceptional success is as-sociated with greater motivation to learn, compared toperceiving more normal successes. Perceived failuremay also be associatedwith greatermotivation to learnamong emergency department clinicians, relative tonormal success, but this finding was not as strong inthe more conservative two-lines test.

Discussion

The results of this study suggest that motivation tolearnwas lowestwhenemployeesperceivedothers tobesuccessful, and highest when they perceived that othershad failed or achieved exceptional success. This findingis intriguing, given the many different forms we might

FIGURE 1Study 1: Effect of Perceptions of Performance on Motivation to Learn

120

115

110

105

100

95908580757065605550454035302520151050

7.0

6.5

6.0

5.5

5.0

4.5

4.0

Perceptions of Performance (%)

Mot

ivat

ion

to

Lea

rn

Notes: Plot depicts predicted values (gray circled points) and quadratic effect (solid black line) from the final mixed-effects model (Model 3in Table 2) tested in Study 1. Dashed gray lines depict the linear effects generated for Simonsohn’s (2018) two-lines test. Quadratic andlinear effects are depicted with all continuous control variables set to their sample means and all categorical control variables set to 0.

2 This approach was detailed in an earlier versionof Simonsohn’s (2018) article, and was confirmed asa valid approach with Simonsohn through personalcommunication.


have assumed that this relationship would take, but isnonetheless a solitary finding from a study that had in-herent limitations. For example, the between-personsample size was only 53 participants (though we ob-tained 289week-level observations) and ourmeasure ofempathy had uncharacteristically low reliability com-pared to previous research using the same measure(Parker & Axtell, 2001). We therefore designed andconducted a second study with the goal of replicatingthisU-shaped effect, while also exploring potential me-diators andmoderators.

STUDY 2: ONLINE SCENARIO-BASED STUDY

To build on the observed results of our initial fieldstudy, we conducted a scenario studywith an onlinesample. Our goals in Study 2 were to constructivelyreplicate the curvilinear relationship betweenothers’ performance and motivation to learn dem-onstrated in Study 1, and to explore potential medi-ators and moderators. We focused in particular onthe role of interest and surprise because both areemotions that people may experience in response toexceptional performance, that may alter how peopleperceive performance, and that may influence mo-tivation to learn.

Emotions are subjective experiences character-ized by relatively short periods of physiological ac-tivation and bodily expressions that occur as peopleappraise specific stimuli from their circumstancesand that tend to alter their cognition,motivation, andaction tendencies (Barrett, 2006; Izard, 2007; Thoits,1989). The emotion of interest is a subjective expe-rience in which people feel “engaged, caught-up,fascinated, curious” (Izard, 1977: 216), because theyappraise stimuli to be novel or complex, but alsocomprehensible (Silvia, 2005). When interested,individuals exhibit behavioral or physiologicalchanges such as speaking faster andwithmore rangein vocal frequency, and adjusting the muscles neartheir eyes and forehead to focus attention and con-centrate better, and motivate exploration (Silvia,2008). Surprise, in contrast, is an initially mildlyunpleasant emotion because it disrupts people’sdesires for structure and predictability, but it canturn positive if the source of the surprise is appraisedto bepositive after the initial disruption (Noordewier& Breugelmans, 2013). Surprise is distinct from be-ing startled, which is a reflexive reaction to intense,sudden stimuli (Ekman, Friesen, & Simons, 1985),with surprise occurring when people actually ap-praise stimuli to be unexpected and to interfere withongoing mental activity, rather than just react re-flexively to it (Reisenzein, 2000). Surprise tends toinitiate analysis of one’s circumstances in a way thatis more conscious and deliberate, and to updating of

one’s beliefs if necessary (Meyer, Reisenzein, &Schutzwohl, 1997).

Surprise and interest could both potentially me-diate the curvilinear relationship between percep-tions of others’ performance andmotivation to learn,or potentially moderate the relationship. Surprisewould mediate the relationship if people appraiseothers’more failed or more exceptionally successfulperformance to be unexpected and disruptive, be-cause these are the appraisal criteria that lead tosurprise (Reisenzein, 2000); in addition, becausesurprise readies people to update beliefs, it alsomotivates analysis (Meyer et al., 1997), which is amotivated learning effort. However, it may be thatnot all failures or exceptional successes are sur-prising. For example, if one person knows thatanother person regularly performs better thanother employees, was working on a new and im-proved way to do the work, or happened to expe-rience all of the right conditions for exceptionalperformance they may not be as surprised by theperformance. Likewise, if the person knows that thetask at hand is extremely difficult or irregular, theymay be less surprised by seeing others’ failure atthe task, or could even find others’ achievement ofnormal success to be surprising. If so, then the re-lationship betweenperceiving these different levelsof others’ performance and individuals’motivationto learn may be moderated by the extent to whichthe performance is more or less surprising.

Similarly, interest could mediate the relationshipbetween perceptions of others’ performance andmotivation to learn if someone else’s failure or ex-ceptional success in the same or a similar task leadsthem to appraise that task asmore novel or complex,but also more comprehensible, as these appraisalsmotivate exploration, which is also a motivatedlearning effort (Silvia, 2005, 2008). Alternatively,it may be that others’ different levels of perfor-mance matter less to people when they are alreadyinterested in an activity or task. In other words, anindividual’s greater or lesser interest in the taskcould moderate the effect of perceptions of others’performance on the individual’s motivation tolearn. To explore these possibilities, we subjectedthe relationship between perceptions of perfor-mance andmotivation to learn to a set ofmediationand moderation analyses focusing on interest andsurprise.

Participants and Procedures

We recruited participants for this scenario-basedstudy using Qualtrics Panels. This service recruitsparticipants from a large pool of potential partici-pants who sign up to receive cash or reward points.


Researchers identify criteria for data quality inadvance, and the service removes any responsesthat do not meet these criteria. We included onlyparticipants who were able to pass a colorblind-ness test (because the task required participants todifferentiate colors), who did not use a mobile de-vice to complete the study (again to enable greatervisibility on the task), who completed the entirestudy, andwhohadnot completed a study utilizingthe same task previously. We further excludedparticipants who wrote nonsensical answers to theopen-ended questions (20 people), who spent lessthan 5 minutes or more than 45 minutes on thestudy (14 additional people), who spent less than10 seconds attempting the practice round (or didnot click at least one part of the image as describedbelow; three additional people), or who correctlyguessed the goal of the study (four additionalpeople). This left us with 296 participants in ourfinal sample (out of 337 responses). Participantsaveraged 55 years of age (SD5 14.7) and 79%werefemale.

Study task and manipulation. Our study task in-volved identifying Howell–Jolly bodies (HJBs)—anindicator of damaged spleen function that canimply increased risk of infection and illness—from a blood smear image (a professionally pre-pared slide image of a blood sample), similar tothe tumor cell–labeling task used by Chandlerand Kapelner (2013). Participants were told thatthe purpose of the task was to examine whetherlaypeople (rather than medical professionals)could be trained to identify HJBs sufficiently wellto perform this work, allowing medical profes-sionals to perform other life-saving work. Theywere shown a sample smear and then engagedin a “practice round” of HJB identification. Next,they were presented with a story about a previousparticipant who completed the task, which weused to create variance in perceptions of perfor-mance. We did this by randomly assigning par-ticipants to read a story of a prior participant whofailed, succeeded, or achieved exceptional successin the task.

Specifically, participants were told that a pro-gram of utilizing laypeople to identify HJBswouldonly be effective if they could identify at least 60%of the HJBs in a smear correctly. Then, partici-pants were told how well a previous participant(referred to as FP) had done, with participantsrandomly assigned to read one of three versions ofthe story, reflecting failure, success, or excep-tional success, respectively, as indicated by thewords in brackets:

For your reference, FP was [not / fairly / excep-tionally] successful [at all (for the failure con-dition only)] at this task, identifying [far lessthan 60% / a littlemore than 60% / 100%] of theHowell–Jolly bodies correctly, [and takingmuchlonger than average / taking an average amountof time / and was the only person out of thou-sands to do this, in what was nearly the shortesttime] to complete the task.

Finally, participants were given a quote from FP(with language adapted for each condition):

When I accepted this task, I thought it was fairlychallenging, but interesting. I really liked thatdoing a good job on this taskwouldhelpmedicalprofessionals—it seemed like aworthwhile task.I tried very hard to do a good job and identify theHowell–Jolly bodies correctly. I think the reasonI [didn’t do well / did pretty well / did reallywell] was because I [was not / was / was] verydiligent about how I searched through the imagefor theHJBs. I learned it is important todevelopavery clear search strategy for this task, likemoving top to bottomor left to right, and doing itconsistently. It’s very easy to get distracted ornot be systematic about it.

Next, participants answered perceptual questions,including questions about the empathy they felt forthe previous participant, their perceptions of howsuccessful the previous participant has been, andtheir own motivation to learn. Participants then sawthe slide of HJBs from the practice round again, butthis time with feedback on the cells that should havebeen labeled as containing HJBs (provided by twomedical students), and were asked to reflect on whatworked well or poorly, and what they might do dif-ferently in the “actual” test. After sharing their re-flections, participants performed the “actual” test ofidentifyingHJBs in another blood smear image. Thenthey were asked demographic and perceptual ques-tions.On the final pageof the survey, after submittingall of their responses, they were debriefed on thepurpose of the study.

Measures

Focal measures.We assessed motivation to learn(a 5 .95) and perceptions of performance using thesame items as in Study 1, adapted to fit the studycontext (for instance, we changed the third and sev-enth anchors in our scale of perceptions of perfor-mance from “Less success than expected” and “Anoutcome that exceeds all expectations” to “Lesssuccess than desired” and “An exceptional out-come”; see Appendix A).


Surprise and interest measures.As noted earlier,one of the goals of Study 2 was to explore potentialmediating and moderating roles of surprise and in-terest in explaining the relationship between per-ceptions of others’ performance and individualmotivation to learn. surprise (Kotsch, Gerbing, &Schwartz, 1982) and interest (Mitchell, 1993) wereboth measured after assessing participants’ percep-tions of FP’s performance in the provided story, butbefore assessing their motivation to learn. TheCronbach’s a for these variables are 0.83 and 0.91,respectively, and we captured participant responsesfor both variables with a 7-point Likert scale rangingfrom “strongly disagree” to “strongly agree.”

Controls. Consistent with Study 1, we controlledfor empathy, which could influence motivation tolearn because people who feel others’ emotions andperspectives tend to be more receptive to using theirviews as a new way of understanding (Brown &Starkey, 2000). Given the low reliability of the em-pathymeasure used in Study 1 (adapted from Parker&Axtell, 2001), in this studywe adapted Escalas andStern’s (2003) five-item measure (on a 7-point Likertscale ranging from “strongly disagree” to “stronglyagree”) of empathizing with advertisements (be-cause it focuses on empathizing with a story pro-tagonist), and found that it demonstrated greaterinternal consistency (a 5 .96) in our research con-text. A confirmatory factor analysis (using MPlus[Muthen & Muthen, 2005]) of all items in our in-cluded scale measures (motivation to learn, empa-thy, surprise, and interest) revealed acceptablemodel fit (RMSEA 5 .10, SRMR 5 .06, CFI 5 .93,TLI 5 .91).

Similar to Study 1, we included dummy variablesfor the exceptional success and failure story condi-tions to account for any additional effects from thestories that may have occurred above and beyondtheir effects on perceptions of performance. We in-cluded a binary variable for whether a person was ahealth professional (05 no, 15 yes), coded based ontheir self-reportedoccupational category (assessed atthe end of the study), to account for any effects thatprevious knowledge about the medical field mayhave on the desire to learn an activity from that samefield. Finally, we controlled for age because it hasbeen shown to affect motivation to learn and relatedvariables (Colquitt, LePine, & Noe, 2000) as well asperformance in similar tasks (Chandler & Kapelner,2013).

Analysis

Procedure and data quality. We conducted ouranalysis at the individual level using ordinary leastsquares (OLS) regression, as well as Hayes’ (2013)

MEDCURVE macro in SPSS 24. Means, standarddeviations, and correlations are presented inTable 3.Again, though not a necessary part of our analysis(O’Keefe, 2003), we examined whether the stories offailure, success, and exceptional success affectedperceptions of performance (using success stories asthe omitted base condition). Including the controls,we found that failure stories had a negative impact(B5 –0.22, p, .001), and exceptional success had apositive impact (B5 0.26, p, .001), on perceptionsof performance. Histograms of residuals from ana-lyses suggested the presence of several potentialoutliers (three exceptional residuals in the modelpredicting motivation to learn). We reviewed theseresponses and determined that they were withinreasonable bounds (e.g., there were no obvious signsof error or mis-entry), and we had no theoreticallygrounded reason for removing them. All of our ana-lyses therefore include these observations.

Results

We were interested in the possibility that partici-pants’ perceptions of FP’s performance in the storythey read would demonstrate a U-shaped effect onparticipants’motivation to learn from the story basedon whether it reflected failure, success, or excep-tional success. We constructed the first threemodelsin Table 4 to examine this idea. Model 1 displaysthe effect of the controls on motivation to learn andModel 2 displays the effect of the controls and themain effect of perceptions of FP’s performance.Model 3 includes all variables, including the squaredterm for perceptions of FP’s performance. As inStudy 1, the coefficient for themain effect inModel 2was not significant (B5 0.33,p5 .29), but inModel 3the coefficients for the main effect (B 5 –2.36, p 5.03) and the squared term (B 5 1.79, p 5 .01) forperceptions of performance were both significant.The sign of the coefficient for the squared term sug-gests that the curve takes a U shape, as shown in theplotted results in Figure 2.

We performed Simonsohn’s (2018) two-lines testso as to be maximally conservative in our interpre-tation of the relationship between perceptions ofothers’ performance and individuals’ motivation tolearn. We used Simonsohn’s online calculator(which uses the RobinHood estimation procedure togenerate a breakpoint in the curve and subsequentlycalculates the necessary linear effects, with hetero-skedastic robust standard errors, to test the slopes ofeach linear effect; available at http://webstimate.org/twolines/) to perform the test in Study 2. This generateda breakpoint for the data at 0.61, and yielded an averageslope for the linear relationship on the low end of theperceived performance spectrum (i.e., from 0 to 0.61)


http://webstimate.org/twolines/

http://webstimate.org/twolines/

TABLE3

Study2:

Descriptive

Statistics

Mea

nSD

12

34

56

78

910

Con

trol

Variables

1.Age

54.76

14.72

–

2.Hea

lthprofessional

a0.09

0.29

20.09

–

3.Exc

eption

alsu

ccessstorya

0.34

0.48

20

0.02

–

4.Succ

essstorya

0.31

0.46

20.09

0.02

20.5*

**–

5.Failure

storya

0.34

0.48

0.10

†20.03

20.5*

**20.5*

**–

6.Empathy

4.21

1.51

0.00

0.09

0.22

***

0.15

**20.36

***

(0.96)

Indep

enden

tVariables

7.Perce

ption

sof

perform

ance

0.73

0.29

20.02

0.04

0.67

***

20.01

20.66

***

0.41

***

–

8.Surprise

3.95

1.45

0.07

0.01

0.37

***

20.21

***

20.16

**0.24

***

0.34

***

(0.83)

9.Interest

5.14

1.21

0.14

*0.06

0.10

†0.03

20.13

*0.43

***

0.22

***

0.20

***

(0.91)

Dep

enden

tVariable

10.M

otivationto

learn

5.85

1.02

0.12

*0.00

0.00

0.05

20.04

0.36

***

0.12

*0.15

**0.67

***

(0.95)

Note:

n5

296.

aDummyva

riab

le.

†p<

0.10

*p<

0.05

**p<

0.01

***p

<0.00

1


that was negative but not statistically significant (B 5–0.60, p 5 .49), while the slope of the line for highervaluesof perceivedperformance (from0.61 to1.20)waspositive and significant (B 5 0.96, p 5 .05; see dottedlines inFigure2).These results (similar to thoseofStudy1) are consistent with the idea that perceiving others’more exceptional success is associated with greatermotivation to learn, relative to more normal success.However, they also suggest that, at least in this sample,there isnostatisticallysignificant increase inmotivationto learn as individuals perceive others’ performance asdemonstrating greater failure (vs.morenormal success).

Mediation and moderation results. To explorethe potential mediating and moderating effects ofsurprise and interest on the relationship betweenperceptions of others’ performance andmotivation tolearn, we used Hayes’ (2013) MEDCURVE macro (toexamine mediation) and multiple regression (to ex-amine moderation). We considered mediation andmoderation for both variables. We examined eachvariable as a mediator first. As can be seen in Table 5,the first model reveals that perceptions of perfor-mance had no effect on surprise (Bmain effect 5 –0.67,p 5 .66; Bquadratic effect 5 1.13, p 5 .25), the secondmodel reveals that surprise had no effect on motiva-tion to learn (B 5 0.06, p 5 .19), and the 95% boot-strapped confidence intervals for the instantaneousindirect effect of perceptions of performance on mo-tivation to learn (through surprise) contain 0 at low,medium, and high levels of perceptions of perfor-mance (low5 [–0.04, 0.18], moderate5 [–0.01, 0.19],high 5 [–0.02, 0.34]). These results suggest that

surprise did not mediate the effect of perceptions ofperformance on motivation to learn.

Table 5 also displays the analyses for interest me-diating the relationship between perceptions of per-formance and motivation to learn. The third modelreveals that perceptions of performance had signifi-cant direct and quadratic effects on interest (Bmain effect5-3.96, p 5 .002; Bquadratic effect 5 2.98, p , .001), thefourth model reveals that interest has a significanteffect onmotivation to learn (B5 0.55, p, .001), andthe 95% bootstrapped confidence intervals for theinstantaneous indirect effect of perceptions of per-formance on motivation to learn excluded 0 at highlevels of perceptions of performance, but not low ormoderate levels (low 5 [-1.61, 0.06], moderate 5[–0.25, 0.61], high 5 [0.48, 1.79]). This pattern of re-sults suggests that interest mediated the effect of per-ceptions of others’ more exceptional success onmotivation to learn, but not the effect of perceptions ofothers’more normal success or failure.

Table 6 displays the results of our moderationanalyses. The first model reveals that there was asignificant interaction of surprise with both percep-tions of performance (B 5 1.60, p 5 .04) and percep-tions of performance squared (B 5 -1.05, p 5 .03).Plotting this interaction (see Figure 3), and probingslopes using Dawson’s indirect calculation of simpleslopes (see Dawson, 2014) at each value of surprise

TABLE 4Study 2: Regression Models Predicting Motivation to Learn

MODEL 1 MODEL 2 MODEL 3

Parameters Estimate SE t Estimate SE t Estimate SE T

ControlsConstant 4.32 0.28 15.27*** 4.14 0.33 12.59*** 5.08 0.49 10.29***Age 0.01 0.00 1.95* 0.01 0.00 1.91† 0.01 0.00 1.86†

Health professionala 20.07 0.19 20.37 20.08 0.19 20.39 20.07 0.19 20.36Exceptional success storya 20.11 0.14 20.83 20.20 0.16 21.26 20.31 0.16 21.89†

Failure storya 0.13 0.15 0.91 0.20 0.16 1.28 0.06 0.17 0.34Empathy 0.27 0.04 6.74*** 0.26 0.04 6.25*** 0.26 0.04 6.43***

Main Effect TermPerceptions of performance 0.33 0.31 1.06 22.36 1.11 22.14*

Quadratic TermPerceptions of performance squared 1.79 0.71 2.54**R2 0.15*** 0.15*** 0.17***DR2 0.00 0.02**

Note: n5 296.a Dummy variable.†p < 0.10*p < 0.05

**p < 0.01***p < 0.001

Was there anything that surprisedyou about the findings?





plotted (i.e., mean, 1 SD below mean, and 1 SD abovemean), revealed that at low (1 SD below mean) andaverage (mean) levels of surprise, the relationship be-tween perceptions of others’ performance and moti-vation to learn took the anticipated curvilinear shape(with coefficients for the quadratic effect of B 5 3.18p5 .001 at low surprise and B5 1.66, p5 .02 at meansurprise). However, at high levels of surprise (1 SDabove mean), the effect changes shape and signifi-cance, reflecting a nonsignificant (i.e., flat) effect ofperceptions of performance on motivation to learn(with no significant coefficients for either the linear orquadratic effect of perceptions of performance whensurprise was high; p> .89 for both).

The second model in Table 6 reveals that interestdid not moderate the relationship between percep-tions of performance and motivation to learn, as canbe seen by the interaction effects of interest withperceptions of performance (B 5 0.05, p 5 .94) andwith perceptions of performance squared (B5 –0.04,p 5 .94). However, the main effect of interest onmotivation to learn was again significant (B 5 0.53,p 5 .03), consistent with our mediation analysis.

Discussion

Our scenario-based study replicated the curvi-linear relationship between perceptions of others’

performance on an individual’s motivation to learnobserved inStudy1, andplotting the results revealeda similar U-shaped relationship. Specifically, theresults of this study replicated the beneficial effectsof exceptional success observed in Study 1, suchthat perceiving others’ performance as more of anexceptional success was associated with greatermotivation to learn, relative to perceiving others’performance asmore of a normal success. Perceivingothers’performance asmore of a failure seemed to beassociatedwith highermotivation to learn; however,the linear slope over this portion of the performance-perception continuum was not statistically signifi-cant in Simonsohn’s (2018) two-lines test.

Our analyses also revealed key mediating andmoderating roles for interest and surprise, respec-tively, that help shed additional light on thenature ofthe relationship between perceptions of others’ per-formance (and specifically exceptionally successfulperformance) and individual motivation to learn.Specifically, these results revealed that perceptionsof exceptional successmotivated learning (at least inpart) through the interest individuals felt in the task,which in turn positively impacted motivation tolearn. Results also revealed that surprise signifi-cantly moderated the effect of performance percep-tions onmotivation to learn, such that the curvilineareffect was strengthened at low levels of surprise,

FIGURE 2Study 2: Effect of Perceptions of Performance on Motivation to Learn

120

115

110

105

100

95

90

85 80

75 70

65

60

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45 40 35

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6.5

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5.5

5.0

4.5

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Mot

ivat

ion

to

Lea

rn

Notes: Plot depicts predicted values (gray circled points) and quadratic effect (solid black line) from the final regression model (Model 3 inTable 4) tested in Study 2. Dashed gray lines depict the linear effects generated for Simonsohn’s (2018) two-lines test. Quadratic and lineareffects are depicted with all continuous control variables set to their sample means and all categorical control variables set to 0.


TABLE5

Study2:

Exp

loration

ofSurp

rise

andInterest

asMed

iators

oftheEffec

tofP

erce

ption

sof

Perform

ance

onMotivationto

Lea

rn

Surp

rise

Motivationto

Lea

rnInterest

Motivationto

Lea

rn

Param

eters

Estim

ate

SE

TEstim

ate

SE

tEstim

ate

SE

tEstim

ate

SE

t

Con

stan

t2.39

0.69

3.46

***

4.95

0.50

9.83

***

4.29

0.56

7.71

***

2.74

0.43

6.39

***

Con

trol

Variables

Age

0.01

0.01

1.00

0.01

0.00

1.78

0.01

0.00

2.44

*0.00

0.00

0.44

Hea

lthprofessional

20.03

0.27

20.13

20.07

0.19

20.35

0.14

0.22

0.65

20.15

0.15

20.97

Exc

eption

alsu

ccessstory

0.81

0.23

3.55

***

20.35

0.17

22.12

*20.25

0.18

21.33

20.17

0.13

21.35

Failure

story

0.45

0.24

1.90

†0.03

0.17

0.19

20.05

0.19

20.27

0.09

0.13

0.64

Empathy

0.15

0.06

2.70

**0.25

0.04

6.15

***

0.34

0.05

7.41

***

0.08

0.04

2.17

*Indep

enden

tVariable

Perce

ption

sof

FP’sperform

ance

20.67

1.54

20.44

22.33

1.10

22.11

*23.96

1.24

23.18

**20.20

0.89

20.23

Perce

ption

sof

perform

ance

squared

1.13

0.99

1.15

1.73

0.71

2.45

*2.98

0.80

3.75

***

0.16

0.57

0.29

Med

iators

Surprise

0.06

0.04

1.32

Interest

0.55

0.04

13.24*

**F

9.75

***

7.75

***

13.62*

**33

.99*

**R2

0.19

0.18

0.25

0.49

Bias-co

rrec

tedbo

otstrapco

nfiden

ceinterval

forinstan

taneo

usindirec

teffec

twhen

:Perce

ption

sof

perform

ance

50.44

0.02

,CI5

[–.04,

0.18

]20.73

,CI5

[-1.61

,0.06]

Perce

ption

sof

perform

ance

50.73

0.05

,CI5

[–.01,

0.19

]0.20

,CI5

[–0.25

,0.61]

Perce

ption

sof

perform

ance

51.01

0.09

,CI5

[–.02,

0.34

]1.13

,CI5

[0.48,

1.79

]

Note:

n5

296.

†p<

0.10

*p<

0.05

**p<

0.01

***p

<0.00

1


and reduced to nonsignificance (for both linearand quadratic effects) at high levels of surprise.Interpreting the plot of this effect suggests that whenparticipants were highly surprised after readingabout a normal success, theirmotivation to learnwaseffectively equivalent to that arising from readingabout more exceptional success (where motivationto learn seemed to bemore consistent across levels ofperceived performance). In other words, as long asparticipants felt a high level of surprise, perceivingany level of others’performancehad a similar impacton their motivation to learn (reflected in the non-significant slope of performance perceptions onmotivation to learn at high levels of surprise). Wefurther discuss the implication of these exploratoryanalyses, in concertwith ourprimaryanalyses, in thegeneral discussion.

GENERAL DISCUSSION

Across two studies, we found that when individ-uals encounter and perceive others’ exceptional suc-cess, the individuals are more motivated to learn,relative to perceiving others’ performance as demon-strating more normal, expected success. We also ob-served limited (and mixed) evidence for the effectof others’ failure, relative to normal success, onindividuals’ motivation to learn across the twostudies. The effect of failure on motivation to learn is

consistentwithemotion-as-feedback theory (Baumeister,Bratslavsky, Finkenauer, & Vohs, 2001), which pro-poses that negative emotions have a stronger impactthan do positive emotions on information processing,motivation, stereotyping, and other psychologicalphenomena. However, our results suggest a modifi-cation to this theory, at leastwith regard toour contextand topic. If we restate our results in the language ofemotion-as-feedback theory, we could say that whenit comes to motivating individuals’ learning fromothers’ experiences, bad might be (somewhat) stron-ger than good, but exceptionally good is also (signifi-cantly) stronger than good. Our second study alsoexplored interest and surprise as potential mediatorsand moderators of the effects of others’ performanceonmotivation to learn. Specifically, we observed thatinterestmediated the relationship between perceivedperformance and motivation to learn, whereas sur-prisemoderated this relationship.When surprisewashigh, normal success had effectively the same impacton motivation to learn as did failure and exceptionalsuccess.

The curvilinear relationship observed in ourstudies between perceptions of others’ perfor-mance (ranging from failure, through success, toexceptional success) and motivation to learn isimportant—and even more intriguing—given themany recent findings that have highlighted the valueof learning from others’ failures, though typically

TABLE 6Study2: Explorationof Surprise and Interest asModerators of theEffect of Perceptions of PerformanceonMotivation toLearn

Parameters Estimate SE t Estimate SE t

Constant 6.89 1.15 5.97*** 2.81 1.26 2.23*Control Variables

Age 0.01 0.00 1.96† 0.00 0.00 0.44Health professional 20.03 0.19 20.18 20.15 0.15 20.97Exceptional success story 20.39 0.17 22.33* 20.18 0.13 21.34Failure story 0.01 0.17 0.08 0.09 0.13 0.64Empathy 0.26 0.04 6.26*** 0.08 0.04 2.11*

Independent variablesPerceptions of PF’s performance 28.43 3.13 22.70** 20.46 3.56 20.13Perceptions of performance squared 5.80 2.00 2.91** 0.36 2.41 0.15

Moderating VariablesSurprise 20.48 0.30 21.61Surprise 3 perceptions of performance 1.60 0.79 2.04*Surprise 3 perceptions of performance squared 21.05 0.49 22.15*

Interest 0.53 0.24 2.22*Interest 3 perceptions of performance 0.05 0.68 0.07Interest 3 perceptions of performance squared 20.04 0.45 20.08F 6.74*** 27.01***R2 0.19 0.49

Note: n5 296.†p < 0.10*p < 0.05

**p < 0.01***p < 0.001


without examining motivation to learn. Indeed,much research on how people learn in organizationshas typically assumed that learning is motivated buthas not explicitly examined that motivation, or hasexamined behaviors that might be considered prox-ies formotivation to learn (e.g., the valueor relevanceof particular information [Borgatti & Cross, 2003;Quinn & Bunderson, 2016]). This is particularly trueof studies thathave examinedhow individualsmightlearn from others’ knowledge, experience, and per-formance, which have implicitly assumed somelevel of individual motivation to learn or improve,but generally relied on observed behavior change orimproved performance displayed by the individualas an indirect indicator of this motivated learning.Manyof these studies have argued that trying to learnfrom others’ exceptional success is more likely togenerate biased conclusions (Denrell, 2003; Denrellet al., 2013; Denrell & Liu, 2012), or that greater un-derstanding and performance improvement occursin response to others’ failure than in response toothers’ success (e.g., Joung, Hesketh, & Neal, 2006;KC, Staats, & Gino, 2013).

In contrast, our results highlight interesting ob-servations and possibilities for future exploration.For example, on one hand, greater motivation tolearn from exceptional success that generates biasedconclusions could be quite dangerous. On the otherhand, analyzing and understanding exceptionallysuccessful cases is also known to promote deeper

understanding of substantial learning opportunities(Starbuck, 1993), greater firm innovation (Lilienet al., 2002), and the scaling of idiosyncratic suc-cesses through organizations and communities(Cooperrider & Whitney, 2005; Pascale, Sternin, &Sternin, 2010). The validity of the conclusionsdrawn from another’s experience will inherentlydepend on how it is adapted and applied to somefuture task, so it may be the case that some conclu-sions are not relevant for an individual’s immediatecontext, but may in fact be valuable as novel, inno-vative solutions to some future problem.

Moreover, our results suggest the existence of atleast three distinct levels of performance in worktasks (failure, success, and exceptional success) andreveal that considering them simultaneously helpsdevelop more nuanced understandings of individ-uals’motivation for learning. For instance, in studieswhere peoplewere observed to learnmore (or at leastimprove their performancemore) fromothers’ failurethan fromothers’ success, itmay be that the researcherswere comparing failurewithnormal success rather thanwith exceptional success. Alternatively, moderatingfactors, such as surprise, may be at play; it may be thatthey were examining performance that was not sur-prising, and if the performance was surprising, theywould find that perceived normal success leads to asmuch learning as perceived failure does when the per-formance is surprising. It may also be simply that theperformancebenefits of others’ failureobserved in these

FIGURE 3Study 2: Quadratic Effect of Perceptions of Performance on Motivation to Learn Moderated by Surprise

4.0

4.5

5.0

5.5

6.0

6.5

7.0

0 10 20 30 40 50 60 70 80 90 100 110 120

Mot

ivat

ion

to

Lea

rn


High (+1 SD) Surprise

Low (−1 SD) Surprise

Average (Mean) Surprise

Note: Effects are depicted with all continuous control variables standardized and all categorical control variables set to 0.


studies occurs through a mechanism other than in-creasedmotivation to learn, inviting further explorationof what factors undergird these complex vicariouslearning processes.

Mechanisms

Exploringpsychologicalmechanisms, our resultsdoallow us to begin identifying, as well as ruling out,potential explanations for why exceptional successincreased motivation to learn. For instance, our fieldstudy revealed some evidence that an individual’sbaseline motivation to learn and empathy influencetheir motivation to learn from a story of another’s ex-perience, but that profession and typical levels oflearning behavior do not. This suggests that some in-dividual difference measures likely matter in under-standing this relationship, but that motivation to learnis not purely learner-driven, nor purely socioemo-tional. Indeed, we observed the effects of perceptionsofperformanceaboveandbeyond thebeneficial effectsof individuals’ empathy for the protagonist of a story ofperformance (indicative of their social or emotionalconnectedness to the protagonist). The motivatingvalue of others’ exceptional success for individuallearning thus seems likely to operate through a morenuanced mechanism of aspirational information pro-cessing, rather than through one that is purely due toindividual differences or relational characteristics.

We also found evidence for an indirect curvilineareffect of performance perceptions on motivation tolearn through interest, suggesting that high levels ofperceived performance can pique an individual’sinterest and curiosity, motivating them to learn.People become interested in events when thoseevents are novel or complex, but comprehensible(Silvia, 2008). Exceptional performance is novel be-cause it does not happen as often, by definition, asnormal success does, and it may also be complex.Further, we examined how exceptional success af-fects motivation to learn among people performingthe same or similar tasks, which means that their ex-ceptional success was likely to be comprehensible tothem. This may explain why it interested them andsubsequently motivated them to learn. Alongside theidea that achieving exceptional success is desirable tomost people, and that if someone else has achievedsuccess, itmay seemsafe topursue similar success, thisadds to people’s motivation to learn. The result thatsurprise moderates the curvilinear effect of performance

on motivation to learn augments these arguments.Motivation to learn was equally high for all levels ofperformance when people were surprised, but the cur-vilinear pattern of motivation to learn—with greatermotivation at the tails of the performance spectrum—

emerged at average and low levels of surprise.Exceptional success stories may also increase moti-

vation to learn for identity-focused reasons. When ex-ceptional events happen, they can cause people toquestion accepted beliefs, including beliefs about theirown identity (e.g., Weick, 1993). For example, if anemergency-department doctor hears about how a pa-tient lived after being impaled through the stomach(knowing thatmostpeopledie fromsuch injuries), theymay wonder whether they could save a patient in asimilar situation. If they are not sure, theymaywonderwhat that says about their identity as an emergencymedicine physician. Because humans feel a need toperceive themselves as positive, competent, and con-sistent (Erez & Earley, 1993), this doctor would likelyfeel the need to try to reestablish or rebuild theiridentity. Since “[i]dentities are constructed out of theprocess of social interaction” (Weick, 1995: 20), thedoctor would be motivated to talk with others aboutwhat this storymeans for them (e.g., Mead, 1934; Pratt& Barnett, 1997). Because these conversations areabout the doctor’s capability, they are likely to in-volve making sense of how the exceptional successof their colleague who saved that patient was ac-complished. The doctor’s desire to continuouslyestablish and reestablish their identity as a compe-tent professional thus motivates them to learn.

Arenas for Learning

The impactofperceptionsofothers’performanceonaperson’smotivationtolearnhasimplicationsforanarrayof topics studied by organizational scholars in additionto vicarious learning and training. One such topic isleadership. For example, if perceptions of performanceinfluence motivation to learn by giving employees amore aspirational orientation to their work, then excep-tional performance may be one way for a leader (or po-tential leader) to lift the vision of followers (or potentialfollowers). Research on leader vision communication(e.g., Stam, Van Knippenberg, & Wisse, 2010) has ten-ded to focus on how leaders frame their appeals tofollowers. However, another—and perhaps morepowerful—way to lift the vision of followers is byachieving exceptional success, because it motivatesfollowers to learn, increasing the chance that theymayachieve exceptional success as well.

The relationship between exceptional success andmotivation to learn may also have implications for re-search on creativity and innovation. Innovative outputis created by recombining ideas across social domains

Author’s voice:What is the social relevance of yourresearch?







(Hargadon, 2003), and exceptional success is an im-portant cue for decidingwhich ideas in other domainsto pay attention to when trying to learn (Lilien et al.,2002). Even so, creativity and innovation can be diffi-cult and even frustrating processes (Van de Ven,Polley, Garud, & Venkataraman, 1999). If exceptionalsuccessmotivates learningbecause it increases interest(as suggested by our exploratory results), then excep-tional success is not only a cue for which ideas to payattention tobut alsoasourceofpositivemotivation thatmay help during an innovation process that mightotherwise be difficult or frustrating.

Limitations and Future Directions

As with any exploratory, abductive study, the resultswe present here are preliminary, and require more re-search to fully establish them.For example, althoughwefound that there is a curvilinear relationship betweenperceptions of performance andmotivation to learn thatis moderated by surprise and mediated by interest, it ispossible that other variables that we did not include inour study also influence these relationships. For exam-ple,motivation to learnmaybe influencedby individualcharacteristics such as openness to experience or con-scientiousness, both of which we might expect to posi-tively influence motivation to learn and may alsoinfluence judgments about others’ performance. Wecontrol for several individual differences across our twostudies, including individuals’ degree of domain exper-tise (via the control for physician [vs. other health pro-vider]participants inStudy1andhealthprofessionals inStudy 2), baseline learning motivation and behavior (inStudy 1), age (in Study 2), and empathy with the storyprotagonist (in both studies), but additional characteris-tics may be at play when individuals’ perceive others’performance and motivate their resultant learning fromit. Our limit to the individual differences examined is inpart inherent in our design, as the self-report nature ofour studies might have introduced concerns aboutcommonmethod variance inflating our results if we in-cluded these other measures (though, as noted earlier,such concerns do not impact our primary results, ascurvilinear effects cannot suffer from common methodvariance[Siemsenetal.,2010]).Therefore,weencouragefuture research using a broad range of study designs,including laboratory experiments, to further examinethis relationship.

In addition to considering additional individualcharacteristics, it is important for future research toexamine these effects in other contexts with varyingsituational characteristics, and to explore boundaryconditions for this phenomenon. For example, thereliability of the source of information about theperformance, the similarity between the person whoachieved exceptional performance and the individual

perceiving that performance, and the similarity be-tween the performer’s work to the work of the indi-vidual may all influence this relationship. If peopleperceive exceptional performance in tasks that aretoo different from their own, or exceptional successof people who they believe are too different fromthem, they may assume that the exceptional perfor-mance is not relevant to them. Probing these factorsand influences—alongside other questions, such asexamining one’s own exceptional successes as amotivator of learning, would likely yield importantscholarly insights into how and when individualslearn vicariously from others’ performance at work.

Practical Implications

Beyond these theoretical contributions and provoca-tions, the idea that exceptional success stories motivatelearning offers a practical and useful insight, because itsuggests that managers can inspire learning in theiremployees without having to rely solely on stories orexamples of failure (a popular recommendation in priorresearch and managerial discourse). In this way, thepossibility that exceptional success stories canmotivatelearning provides a relatively safe tool for managers toencourage continuous improvement in employees. Ourresearch findings suggest that managers could motivatelearning in their employees by sharing stories of excep-tional success as part of the normal workday routine.Failure stories have been shown to better motivatelearning in classroom or training settings com-pared to success stories (Bledow, Carette, Kuhnel,& Pittig, 2017; Joung et al., 2006), and there arecertainly appropriate times for sharing failurestories. However, failure stories can be problem-atic if they cause people to feel threatened (Cannon& Edmondson, 2001). Eliciting a reciprocal coop-erative interpersonal process (Kopelman, 2020) ofsharing stories of exceptional success would mo-tivate learning and foster beneficial group out-comes, while being less likely to promote individualdefensiveness and vulnerability. The implications ofour findings viewed through a positive lens for orga-nizational scholarship (Cameron, Dutton, & Quinn,2003; Spreitzer, Myers, Kopelman, & Mayer, 2019)suggest that sharing exceptional success stories is apositiveorganizationalpractice that emphasizeswhatis going well and encourages learning that promotesfuture success.

CONCLUSION

Opportunities for learning abound for individuals atwork. Understanding how perceptions of others’ per-formance have a curvilinear effect on individuals’ mo-tivation to learn provides unique insights for advancing


ourknowledgeofhowpeople learn fromothers’ failures,successes, and exceptional successes. Given that peoplecan benefit significantly by learning from others’ expe-riences, but that this learning can be quite difficult toaccomplish, continuing to study the motivating effectsof perceiving different levels of performance in others’work will expand both our theoretical and practicalunderstanding of vicarious learning in organizations.

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Ryan W. Quinn ([email protected]) is an associ-ate professor of management in the University ofLouisville’s College of Business. In his research, Ryan ex-amines questions of how to unleash the potential of peopleand organizations, focusing on topics such as leadership,change, learning, flow, courage, and conversation.

Christopher G. Myers ([email protected]) is an assistantprofessor in the management and organization disciplineat the JohnsHopkinsUniversity CareyBusiness School.Hereceived his PhD from the University of Michigan RossSchool of Business. His research explores processes of in-dividual learning and development in knowledge-intensive work organizations.

Shirli Kopelman ([email protected]) is a professor ofmanagement and organizations at the University ofMichigan’s Ross School of Business. Kopelman’s researchfocuses onmindfully aligning emotions in negotiation andfostering cooperation in social dilemmasandmulticulturalgroup settings. Practical implications enable navigatingresource-abundant conversations to pursue sustainablesolutions for all.

Stefanie Simmons ([email protected]),MD, Fellow of the American College of EmergencyPhysicians, is vice president of patient and clinician expe-rience at Envision Physician Services. Dr. Simmons leads anational multi-specialty group of over 20,000 cliniciansin strategies for relational excellence, including patient–clinician communication, professional well-being, and cli-nician engagement. She leads coaches thatwork to improveclinician retention and skill development.


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APPENDIX A

PERCEPTUAL MEASURES

All perceptual measures used in both studies are presented here.

Perceptions of Performance—Study 1

How successful were the people in this story at achieving the goals of the emergency department?

Motivation to Learn—Study 1 (adapted from Colquitt & Simmering, 1998; Noe & Schmitt, 1986)

To what extent do you agree or disagree with the following statements?

• This week, I intend to exert considerable effort on activities related to learning and development.• I try to learn as much as I can from my work in the emergency department.• I have a strong desire to learn and develop new skills through my work in the emergency department.

Learning Behaviors—Study 1 (Edmondson, 1999)

How often do you. . .

• Seek out and take advantage of learning opportunities.• Take initiative to learn new skills to expand your contributions to the organization.• Plan ways to develop your capabilities.• Make active efforts to acquire new knowledge.• Regularly take time to reflect on ways to develop and improve.• Get all the information you possibly can from others to learn and develop.• Seek out new information that leads to gains in personal knowledge and skills.• Frequently seek new information that leads to growth.

Empathy—Study 1 (adapted from Parker & Axtell, 2001)

Please answer these questions quickly and honestly.

• I felt concern for the protagonist of this story.• It pleased me when the protagonist did well.• I understand the problems the protagonist experienced.• The protagonist did the best they could, given the circumstances.• If the protagonist made a mistake, it was probably not their fault.• The protagonist worked just as hard as I would have.

Perceptions of Performance—Study 2

How successful was FP at achieving the goals of this task? Move the slider on the scale below to identify theappropriate level of success.


Motivation to Learn—Study 2 (adapted from Colquitt & Simmering, 1998; Noe & Schmitt, 1986)

To what extent do you agree or disagree with the following statements?

• I intend to exert considerable effort on learning and developing in this task.• I will try to learn as much as I can from my work on this task.• I have a strong desire to learn and develop new skills through my work on this task.

Empathy—Study 2 (Advertisement response empathy scale [Escalas & Stern, 2003])

Please answer quickly and honestly based on your impressions of FP after reading the summary.

• I felt as if FP’s experience was really happening to me.• I felt as though I were FP.• I experienced many of the same feelings that FP probably experienced.• I felt as if FP’s feelings were my own.• I felt as if what happened to FP was happening to me.

Surprise—Study 2 (Izard, 1977; Kotsch et al., 1982)

Please indicate the degree to which each of these words describe how you feel about the HJB identifying taskafter reading FP’s story.

• Surprised• Amazed• Astonished

Interest—Study 2 (adapted from Mitchell, 1993)

Please indicate the extent to which you agree with each of these statements.

• Identifying HJBs should be fun.• I actually look forward to identifying HJBs.• Identifying HJBs should be dull [reverse coded].• I think I will like identifying HJBs.• I don’t find anything interesting about identifying HJBs [reverse coded].• Other activities would be more interesting than identifying HJBs [reverse coded].


Documents

How Did you Do That? Exploring the Motivation to Learn