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r Academy of Management Annals2018, Vol. 12, No. 1, 252–277.https://doi.org/10.5465/annals.2016.0049

OPPORTUNITY, MOTIVATION, AND ABILITY TO LEARNFROM FAILURES AND ERRORS: REVIEW, SYNTHESIS, AND

WAYS TO MOVE FORWARD

KRISTINA B. DAHLIN1

University of Oxford

YOU-TA CHUANGYork University

THOMAS J. ROULETKing’s College London

Although organizations and individuals tend to focus on learning from success, researchhas shown that failure can yield crucial insights in various contexts that range fromsmall mistakes and errors, product recalls, accidents, and medical errors to large-scaledisasters. This review of the literature identifies three mechanisms—opportunity, mo-tivation, and ability—through which individuals, groups, and organizations learn fromfailure, and it bridges the gaps between different levels of analysis. Opportunity to learnfrom failure mostly takes the shape of more information about errors and failures thatare generated by one’s own and others’ prior failures or near-failures. Motivation tolearn from failure is hindered by punitive leaders and organizations. Finally, the abilityto learn from failure partly relies on inherent attitudes and characteristics, but can befurther developed through thoughtful analysis and transfers of successful routines. Ourreview leads us to distinguish between erroneous versus correct processes and adverseversus successful outcomes to better understand the full gamut of events that are facedby organizations. We identify the existence of noisy learning environment, where spu-rious successes (when erroneous processes still lead to successful outcomes) and spu-rious failures (when correct processes are combined with adverse outcomes) lower theopportunity to learn. Considering noisy learning situations is helpful when under-standing the differences between slow- and fast-learning environments. We concludeour review by identifying a number of unexplored areas we hope scholars will addressto better our understanding of failure learning.

INTRODUCTION

Sometimes we may learn more from a man’s errors,than from his virtues.

Henry Wadsworth Longfellow

Individuals and organizations repeatedly confrontfailures that range from small technical errors andmistakes to product breakdowns to large-scale

disasters. Failure can stigmatize individual and or-ganizational reputations, and it can be extremelycostly for organizations and society. Failure is alsomore noticeable than success because negative in-formation is more salient than positive information(Ito, Larsen, Smith, & Cacioppo, 1998). As a conse-quence, individuals and organizations strongly pre-fer success, which makes learning from failuredifficult because both the reporting of errors andother failures as well as the correct analysis and re-sponse are risky and emotionally fraught. However,learning from failures is critical for both operationalperformance and safety—failure learning is neces-sary for quality improvements and efficiency gainsin production processes, and systematic failurereporting and analyses have been key for reductionsin transportation accidents and adverse events in

The authors are grateful for the valuable comments andsupport from Elizabeth George, the editor, as well as fromthe two reviewers. The authors have also benefited fromthe discussions with Joelle Evans, Kristin Bondesson,Linda Argote, Matt Cronin, Ned Welch, Shitij Kapur, andany health-care worker we encountered while working onthe manuscript.

1 Corresponding author.

252

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https://doi.org/10.5465/annals.2016.0049

hospitals. Because of failure’s significance, the re-search on the topic spans many fields such as psy-chology (Ellis & Davidi, 2005; Hofmann & Mark,2006), organizational studies (Reason, 1997; Zhao &Olivera, 2006), strategic management (Muehlfeld,Sahib, & van Witteloostuijn, 2012), sociology(Perrow, 1999), and health-care management (Hoff,Jameson, Hannan, & Flink, 2004; Kohn, Corrigan,Donaldson, 2000). Importantly, failures providevaluable learning opportunities: individuals andorganizations modify their practices to preventsimilar future failures and to improve performance(Sitkin, 1992). Without examining failure learning,our understanding of success learning is also in-herently biased (Baum & Dahlin, 2007; Denrell,2003). Recognizing the potential of failure to im-prove performance, recent studies on failure havebegun to shift their focus from why and how failureoccurs in organizations to how individuals and or-ganizations do (or do not) learn from failure.

Failure learning has become a distinct area in theorganizational learning literature, and it has attrac-ted growing attention from scholars who seek tounderstand the phenomenon in various contexts,such as product recalls (Haunschild & Rhee, 2004),project failure (Shepherd, Patzelt, & Wolfe, 2011),bankruptcies (Kim&Miner, 2007), health-care errorsand incidents (Chuang et al., 2007; Vogus &Sutcliffe,2007), and accidents (Baum & Dahlin, 2007;Haunschild & Sullivan, 2002). The studies on failurelearning cover multiple levels of analysis and drawfrom a variety of theoretical frames to understandhow actors do (or do not) learn from failure. Al-though an array of factors that affect failure learninghas been identified, there is a lack of systematic in-tegration across levels of analysis and settings;hence, the collectivewisdom about how to best learnfrom failure is limited and fragmented. Importantly,the studies on failure learning borrow much fromtraditional learning studies, but the links and dis-similarities are not clearly understood. Studies thathave attempted to combine success and failurelearning lack conclusive findings and theory ex-ploring how these events are related. To that end,there is a greater divide between traditional learningstudies and failure learning studies than is currentlybeing theorized.

We conduct a review of failure learning studiesand synthesize them to better understand the un-derlyingmechanisms that influence failure learning.We apply the framework of opportunity, motivation,and ability to integrate anddiscuss learning factors atthe individual, group, and organizational levels.

Specifically, opportunity represents a mechanismthat provides information or sufficient time to analyzethe cause–effect of failures; motivation captures dif-ferent actors’willingness to act on failure informationand to engage in failure learning activity; and abilityrepresents actors’ skills or knowledge base to changetheir actions based on failure information (Argote,2012; Argote, McEvily, & Reagans, 2003; Reinholt,Pedersen & Foss, 2011). Mapping the studies of failurelearning at multiple levels of analysis with this frame-work can generate new insights for future work.

We argue that it is important to clearly separatebetween processes and outcomes and to acknowl-edge that bad processes do not always lead to failedoutcomes and, conversely, that correct processesmight still result in failed outcomes. A traditionalfocus on “successful processes–successful out-comes” versus “failed processes–failed outcomes”overlooks the fact that “successful processes–failedoutcomes” and “failed processes–successful out-comes” are common. We call these last two process-outcome combinations spurious failures andspurious successes. When they are common, thesecombinations produce noise in traditional learningprocesses that negatively affects opportunity, moti-vation, and the ability to learn. A complete un-derstanding of process–outcome relationships inorganizational learning helps to address a funda-mental critical question of why we see a systematicdecrease in failures in one setting, whereas in an-other we do not. Although the risk of dying in a caraccident has diminished by 50 percent over the last25 years, and the risk of train accidents has been re-duced by 70 percent (NCSA, 2015; FRA, 2016), therisk of dying from a hospital error has increased by350 percent2 (Binder, 2013; Kohn et al., 2000).

Below, we begin our review by defining failuresand failure learning as well as errors and errorlearning, followedby a reviewof the failure and errorlearning literatures at three levels of analysis, clus-tering mechanisms under the opportunity–motivation–ability headings. We highlight the keyconstructs and mechanisms that can be identified asinfluencing learning processes and outcomes. Fromthere, we discuss how the four process–outcomecombinations influence the opportunity, motiva-tion, and ability that are associated with failure

2 Part of the increase is due to new definitions and bettermeasurement of preventable hospital errors. The numbersare also challenged; however, overall it is clear that there islittle to no improvement, which still demonstrates thecontrast with transportation accidents.

2018 253Dahlin, Chuang, and Roulet

learning. Finally, synthesizing and assessing the lit-erature as a whole, we identify the research chal-lenges in failure learning and discuss the promisingresearch opportunities that may advance our un-derstanding of failure learning.

DEFINING SCOPE: FAILURE, ERRORS, ANDFAILURE LEARNING

There is a literature on errors and another litera-ture on failures in organizations. They are related,and many of the mechanisms and findings overlap.In fact, they often use the same definition for errorsand failures that they “deviate from expected anddesired goals” (Leape, 1974; Rasmussen, 1982;Reason, 1990; Sitkin, 1992; Zhao & Olivera, 2006).The literatures do also differ as errors are incorrectlyexecuted tasks or routines (such as a train engineerwhodrives a train over the speed limit or anursewhogives the incorrect medication to a patient), whereasfailures are undesiredperformance outcomes (a trainaccident occurring instead of the train getting frompointA topointB asplanned; a patientwhodies aftersurgery instead of leaving the hospital healthier thanbefore entering it).

With regard to errors, they have been classified byRasmussen (1982) into rule-based errors (breakinga known rule), skill-based errors (making a mistakeor forgetting), and knowledge-based errors (notknowing enough). Another error typology iswhetherthe error is action-based or related to decision-making (Lei, Naveh, & Novikov, 2016; Zhao &Olivera, 2006). Nevertheless, not all errors, mis-takes, or incidents necessarily lead to failure. Someerrors and mistakes can even produce positive out-comes, such as the discovery of new organizationalprocesses and innovation, or be too insignificanthave any impact on an event’s eventual success orfailure (Cannon & Edmondson, 2005).

Failuresmay be caused by a combination of errors,such as incorrectly executed routines and tasks, vi-olations, risks, or chance factors (Frese&Keith, 2015;Hofmann & Frese, 2011). It can be avoidable or un-avoidable and intentional or unintentional. It caninvolve human action and organizational processesand arrangements (Ramanujam & Goodman, 2003;Reason, 1997).

We define error and failure learning as the processby which individuals, groups, or organizationsidentify error or failure events, analyze such eventsto find their causes, and search for and implementsolutions to prevent similar errors or failures in thefuture. This definition is consistent with the

definitions of learning in the organizational learningliterature (Argote, 2012). The outcomes of error andfailure learning can, therefore, include changes inunderstanding (Huber, 1991), behaviors (Chuang &Baum, 2003;Ginsburg et al., 2009; Shepherd, Patzelt,& Wolfe, 2011), or performance improvement(Cannon & Edmondson, 2001; Baum & Dahlin, 2007;Heimbeck et al., 2003; Zhao, 2011).

We aimed to be inclusive, if not exhaustive, inidentifying studies on error and failure learning andsearched keymanagement, healthmanagement, andsafety journals for relevant studies, focusing mostlybut not exclusively on the period from the year 2000.All identified articles were sorted by their level(s) ofanalysis. We reviewed the learning mechanisms inthe articles and categorized themaccording to failurelearning triggers, clustering them under three head-ings: opportunity to learn, motivation to learn, andability to learn.

OPPORTUNITY TO LEARN FROM FAILURE

Opportunity to learn refers to the scope of in-formation and the time that allows actors to learnfrom failure events. Information-based opportunityrefers to the amount of information that is availableabout similar failure events because such events canprovide information about failure causes (Argote,2012). Time-based opportunity refers to the time thatis given to actors to reflect upon failure events and toanalyze the information that can be derived from theevents to learn from them and the time in which toexecute an action that is related to a failure-learningactivity (Carroll, 1963). Information-based opportu-nities are usually studied by quantifying the amountof available information about similar failure events(number, frequency, and recency), information ac-cess owing to group composition, organizationalmembers’ networks, and information diffusion in-side or between organizations. By contrast, time-based opportunity refers to how much time that isavailable to process information and/or carrying outa task (Carroll, 1963).

Information-based learning opportunities are of-ten measured as one’s own or others’ prior experi-ences with similar failure events (counted as thenumber of events or thenumber of cumulative eventsin previous time periods) and as how organizationalstructures and routines influence the diffusion offailure-learning–related information. Experienceand its effect on learning are the most common em-pirical approach, and although this was first studiedinproduction settings that range fromclassic cases of

254 JanuaryAcademy of Management Annals

airplane construction to that of liberty ship builders(Wright, 1936; Thompson, 2001) and learningcurves, which refers to the idea that cumulative ex-perience affects performance at a decreasing rate, theexperience and its effect on learning have come to beapplied across levels of analysis, and used acrossa large number of settings—among them failurelearning.

Two different processes convert experiences intobetter performance: learning-by-doing and analyti-cal learning. Learning-by-doing is mostly automaticand tacit,whereas analytical learning involves activedecision-making that uses information about a priorevent to reshape future routines (Reason, 1990;Thomson, 2012). Failure learning theories are muchmore concerned with the active decision-makingtheory, especially on the individual and grouplevels, whereas few organizational learning-curvestudies separate empirically and theoretically be-tween learning-by-doing and analytical learning (seeLapre, Mukherjee, & Van Wassenhove, 2000; orSinclair, Klepper & Cohen, 2000 for exceptions).

Time-based, or temporal, learning opportunitiesare concerned with the amount of time that an actorhas to execute a routine or to process informationabout a routine that has gone wrong. Outside therealmof student learning, relatively few studies haveexamined temporal mechanisms. Exceptions infailure learning a small number of studies that ana-lyze the impact ofworkload and slack (Lawton et al.,2012; Malone et al., 2007) and how the speed of as-similating and analyzing relevant information af-fectswork processes and routines (Edmondson et al.,2001). In addition, autonomy provides the mentaland operational space that can allow individuals toprioritize their tasks, allowing them the time thatthey need to learn from failures (Kerr, 2009; Sternet al., 2008).

Individual and Group-Level Opportunities toLearn from Failure

A wide range of studies have examined howinformation-based and time-based opportunitieslead to failure learning. Although some learningopportunities lead to automatic reduction of errorsand failures, this concerns mainly errors such asslips and mistakes. By contrast, knowledge-basederrors and failures require more deliberate reflectionto reduce the likelihood of repeating them (Iedemaet al., 2006). A study examining the effect cardiacsurgeons’ prior experience had on learning dem-onstrated that past surgery failures improved a

surgeon’s future surgery outcomes (Diwas, Staats, &Gino, 2013). Moreover, other cardiac surgeons’ fail-ures interacted with the surgeon’s own prior failuresto further improve the surgeon’s surgery outcomes.In other words, one’s own and others’ failure expe-riences can have a joint effect on learning: any re-lated failure provides an opportunity to reflect onwhat has gone wrong and how to improve pro-cedures. Interestingly, the positive interaction effectsuggests that information-based experience canhavean increasing return on failure learning.

Temporal opportunities such as working condi-tions affect how experiences are converted intolower error rates. Residents with greater work au-tonomy, that is, they “perceive that they have thefreedom and discretion to plan, schedule, and carryout their jobs” were found to have higher error re-duction (Stern et al., 2008: 1554). Task autonomyallowed the residents to reflect on errors and learnfrom them bymaking procedural changes.When theorganizational climate was such that learning wasencouraged, the residents took more time to reflecton work processes and errors and further reducedtheir own errors, thus, motivation (climate) and op-portunity (time) interacted to accelerate learning(ibid.).

Teams play an important role in error learning(Edmondson et al., 2001); however, when the envi-ronment is ambiguous and changing, team-information processing becomes complicated,which hampers learning. Individual and grouplevels of learning are intertwined as individualcharacteristics and team composition jointly de-termine teams’ error reporting (Edmondson, 1996).Good member coordination (Baker et al., 2006) andcommon goals (Tjosvold et al., 2004) enable teams tobenefit from the full potential of each of their mem-bers. In the same way, team stability and work pro-cesses enable the group to better process information(Edmondson, 1996). When error information gath-ering activity is not a part of existing team routines(Lawton et al., 2012), or the team does not haveenough autonomy to allow it to collect critical in-formation (Kerr, 2009), learning might not occur. Inother words, having neither established nor impro-vised ways to gather failure information reducesopportunities to learn from failure experiences.

Groupcharacteristicscanalso influence information-based learning.Groupdiversityand intergroup linkagesare two mechanisms that provide teams with access toa wide range of information which, in turn, reducefailure rates (Chuanget al., 2007;Tucker&Edmondson,2003).Member rotation is anotherway to expose teams


to a greater flow of information that can enable them tobetter analyze problems (Argote & Todorova, 2007). Inaddition, the time aspect matters here—being exposedto many different but related experiences in a shortamount of time benefits learning. Experience alsotransfers across levels: group-level success experiencehasbeenfoundtohelp individual-level failure learning,which ultimately benefits the learning rate of the orga-nization as a whole (Zheng et al., 2013).

Organization-Level Opportunity to Learnfrom Failure

In our review of the literature, it emerged thatinformation-based opportunity is the most studiedorganizational learning mechanism. To summarizeacross the organizational opportunity studies, ex-perience matters in most cases and across a widearray of settings; however, it does so somewhat dif-ferently in different settings and organizations. Thenature of an experience event, its outcome, rareness,and complexity influence its learning impact. Atrend in organizational-level studies is to separateone’s own versus others’ experiences and to focus onin which case one matters more than the other andwhen (Baum & Dahlin, 2007; Chuang & Baum, 2003;Kim & Miner, 2007; Madsen & Desai, 2010; Madsen,Dillon & Tinsley, 2016).

Prior failure events provide opportunities to learn.Over time, as many problems are resolved or bettermanaged, we would expect a diminishing return toexperience, which yields a learning curve that issimilar to that in production learning. In fact, there isrich empirical support that shows that learningcurves have a similar shape with regard to failurereduction. Train, mining, and airline accidents havebeen found to reduce future accident propensity onthe industry and firm levels (Baum & Dahlin, 2007;Desai, 2016; Madsen, 2009; Haunschild & Rhee,2004). The failure-reducing effect is strongest forrecent accidents (Haunschild, Polidoro, & Chandler,2015), accidents of larger magnitude, which aremeasured in terms of accident cost and level of in-juries (Madsen, 2009), and events of high visibility(Desai, 2011), which are measured in terms of themedia scrutiny of the accidents. There are twomechanisms that can induce organizations to learnfrom highly visible failures; in such cases, the in-formation about causes and remedies is more avail-able; however, the motivation to counteractaccidents may also increase when outsiders paymore attention and reputations are at stake. Whenthere is more press attention to an accident or

a product recall, organizations invest more in activ-ities that can reduce the risk of future accidents, suchas a train line installing new track (Desai, 2011).

The effect of recency, thatmore recent events havegreater impact than older events on failure re-duction, can also have multiple explanations. Forinstance, there is the proposition that new routinesand practices make past experiences less relevant astime passes, or, as suggested by Haunschild et al.(2015) that an adverse event attracts an organiza-tion’s attention and motivates the organization toreduce the risk of future accidents; however, thismotivation weakens over time as other importantorganizational goals take precedence.

Complex problems provide a greater opportunityto learn. A notable study supporting the argumentthat complex challenges trigger faster and more ef-ficient learning is an investigation of British IVFclinics, which demonstrates howopportunity affectslearning (Stan & Vermeulen, 2013). The key perfor-mance metric of IVF clinics is live births per treat-ment cycle, and a key aim is to lower the number offailed cycles. Whereas private clinics could chooseto accept only patients with good prognoses, whichmeant fewer failures, public clinicswere not allowedto screen patients and, therefore, had higher failurerates at the start of their activities. However, workingwith more difficult cases enhanced the information-based learning of the public clinics, and they in-creased their ability to successfully treat any patient,which resulted in higher learning rates than those ofprivate clinics. Working on complex problems pro-vided greater opportunity to find solutions to diffi-cult problems and led to faster failure reductionacross problems. The concept that complex failuresituations provide richer information is in line withthe finding that airlines learned more from complexaccident cases than from simpler ones (Haunschildand Sullivan, 2002) and that this was especially truefor specialist airlines. Complex problems offeredmore venues for learning and counteracted simpli-fied cause–effect analyses (e.g., simplifying failureattributions to factors that are beyond the organiza-tion’s control such as patient age in hospital cases orto pilot error in the case of airlines).

Whose experiences matter. Not only do failuresthat are experienced by an individual organizationprovide opportunities to learn, but also the failuresthat are experienced by other organizations provideinformation for learning. Airlines learn both fromtheir own and other airlines’ accidents (Haunschild& Sullivan, 2002). Based on the behavioral theory offirm logic which states organizations are more likely


to look further for better solutions when their peersoutperform them, train companies that had moreaccidents compared with their peers were found tolearn more from such peers rather than from theirown accidents (Baum & Dahlin, 2007). Ontarionursing home chains learned from both their ownand others’ businesses with respect to naming theirunits. The learning effect or willingness to changewas dampened when the chain had followed a strat-egy for a long time (Chuang & Baum, 2003).

Learning from near-misses. It is not only failureevents that provide information-based opportunitiesto learn, but such information can also be gleanedfrom events that are neither purely successful norwholly failures (Rerup, 2006). Most attention hasbeen given to near-misses—when there is almosta failure but there are no direct negative conse-quences (Kessels-Habraken et al., 2010). The oppor-tunity to learn from near-misses (especially those ofothers), or what is also known as latent errors(Ramanujam, 2003; Reason, 1997), is only possible ifsuch actions are recorded or easily observed. KimandMiner (2007) used the ratings of banks to capturewhether they were close to failing and found thatnear-failures affected learning more than actualfailures did. Building on industry insiders’quotes, they found that near-failures have greaterinformational content—whereas failed firms disap-pear from the industry and are forgotten, near-failures remain and can tell their story and at thesame time they also remind others of their survival.Perhaps more importantly, near-failures combinebad performance with remedies for restoring per-formance after a period of trouble. Rather than tellinga story of how to fail, they tell a story of redemptionwhen one is close to failure. The greater in-formational content of the near-failures means thatthere is a greater opportunity to learn from them.However, the reporting of near-misses and thus theinformation gathering process is more difficult be-cause of the challenges in defining the scope of suchevents (Kessels-Habraken et al., 2010).

MOTIVATION TO LEARN FROM FAILURE

Motivation is the desire or willingness to act ina certain way and, in the context of error and failurelearning, the desire or willingness to invest in re-ducing adverse event frequency. Motivation to learnfrom failure, therefore, refers to the resource levelsthat are devoted by individuals and organizations tofailure learning activity; such resources include at-tention and operational investments (Kanfer &

Ackerman, 1989). Effective learning processes re-quire individuals and organizations to allocate cog-nitive resources to (i) correctly identify and analyzeerror and failure causes and (ii) search for and im-plement solutions that prevent similar errors orfailures in the future. Failure learning studies con-cerning motivation address contextual factors suchas safety climate, psychological safety, leadershipstyle, and attitudes; cognitive and emotional barrierssuch as attribution errors (internal versus external); in-formation processing in groups and, for organizational-level studies, factors that trigger motivation such ashigh visibility events and public attention. Otherorganization-level motivation triggers include threatsto ones’ reputation, social comparisons (doing worsethan the competition), the recency of an event, andclimate variables.

Individual and Group-Level Motivation to Learnfrom Failure

Most individual-level studies on error and failurelearning study motivational factors. In two labora-tory studies, using a computer simulation task, Zhao(2011) found a positive association between partici-pants’ self-reported motivation to learn from errorsand their actual failure learning. In a field study of anAustralian hospital, employees’ safety motivationhad a positive effect on their safety participation,which was measured as behaviors that promotea safety-oriented environment (Neal &Griffin, 2006).Although individual motivation affects learning be-havior in both the lab and inside organizations, or-ganizational factors affect individual motivation tolearn from failure. For example, safety climate,which is defined as “perceptions of policies, pro-cedures, and practices related to safety” (Neal &Griffin, 2006: 956), plays an important role in moti-vating individual learning. Group-level safety pro-motion is positively associated with individualsafety motivation and safety participation in hospi-tals (Buljac-Samardzic, van Woerkom, & Paauwe,2012). There are both bottom-up and top-down pro-cesses between individuals and groups that help toproduce a safety climate (Neal & Griffin, 2006).Group climate has been found to affect both indi-vidual motivation and individual behavior, and ina study of 33 organizations, individuals were morelikely to report accidents when supervisors enforcedsafety policies (Probst, 2015). Adding the organiza-tional level of analysis, individual attitudes to safetyaffects organizational learning by promoting a safetyclimate at the group level (Zohar & Luria, 2005).


Individual safety motivation and engagement infailure learning activities both affect and are affectedby group, managerial, and organizational attitudes.

Motivation to learn from failure is also driven byindividuals’ causal attributions concerning failures.In attribution theory, individuals cope with out-comes by making causal attributions to guide theirfuture behaviors (Ilgen and Davis, 2000; Nisbett andRoss, 1980). Causal attribution involves how an in-dividual allocates causes for a certain outcome,which in turn, influence his or her motivation tolearn. Specifically, individuals tend to attributesuccess to internal causes such as ability and effort,and failure to external causes such as environmentalfactors and luck (Jones & Harris, 1967). At the sametime, individuals often turn this around and attributeothers’ successes to external causes and others’ fail-ures to internal causes. Such attributions affect in-dividual motivation to engage in failure learningactivity (Chuang et al., 2007; Zhao and Olivera,2006). Attribution theory helps us to better un-derstand the previously discussed result that sur-geons’ cumulative number of successful cardiacprocedures had a greater impact on their futuresuccess rate than failed procedures had, whereasother surgeons’ cumulative number of failed pro-cedures significantly helped to improve a cardiacsurgeon’s subsequent success rate (Diwas et al.,2013). One can argue that surgeons attributed theprior successes to their own effort and actions,whereas the prior failures were attributed to un-controllable factors. Thus, the surgeons assumedthat there was little to learn from own failed pro-cedures. By contrast, other surgeons’ failures wereattributed to their efforts and actions, which madethe focal surgeon more willing to review and learnfrom others’ failure causes. In a similar vein, in-dividuals who work in teams attributed failure toactions that are taken by other individual teammembers rather than by the team when they experi-ence poor team performance in a laboratory setting(Naquin and Tynan, 2003). However, this tendencydecreased as subjects’ knowledge of teamwork in-creased. Another sign that attribution biases can bealleviated is a study inwhich individualswere betterable to make internal attributions after failing ina task when they received after-event reviews thathelped them to understandwhat contributed to theirperformance (Ellis, Mendel, and Nir, 2006). In-dividuals who made more internal than external at-tributions improved their performance more. Ina study of CEO attributions and organizational per-formance, Salancik andMeindl (1984) foundan even

stronger learning effect from internal attributions:when CEOs attributed poor firm performance to in-ternal causes even when the low performance wasclearly caused by an external factor, their firms per-formed better in subsequent periods. Internal attri-butions are clearly important for behavioral change.

Failures often generate strong negative emotions(Paget, 1988) as individuals experience feelings ofguilt, embarrassment, or fear when they are involvedin failures or make errors (Edmondson, 1996; Paget,1988). Negative emotions prompt individuals to be-come more risk averse (Loewenstein et al., 2001),affect judgment (Forgas, 1995), and lower engage-ment in failure learning. In general, failure-inducednegative emotions should lower individuals’ moti-vation to learn. However, in a laboratory study,negative emotions were instead positively associ-ated with the motivation to learn (Zhao, 2011). Theauthor explains this effect by suggesting that thestrength of emotions matters, not just whether anemotion is negative, and negative emotion only af-fects learning above a relatively high threshold. If thestrength of emotion matters, the finding that mana-gerial error intolerance increases negative emotionssuggests that managers’ attitudes can push staff intonon-learning (Keith & Frese, 2005).

There are individual differences in the emotionalresponse to failure and individual differences incoping orientations that help to explain the differentemotions that are generated by failure as well asdifferences in the motivation to learn from failure:Individual affective organizational commitment—how loyal they feel toward their organizations—decreased with negative emotions about failure(Shepherd et al., 2011); however, this link betweennegative emotions and failure was moderated bycoping orientations when dealing with failure. Neg-ative emotions also decrease when individuals per-ceive that failure is anormal occurrence in theirworkenvironment (Shepherd et al., 2011).

Individuals’ motivation to learn from failure is af-fected by psychological safety. Not to be confusedwith safety climate, psychological safety refers to theperception that it is safe to take interpersonal andprofessional risks in the workplace (Edmondson,1996; Edmondson and Lei, 2014). Psychologicalsafety has been found to positively influence failurelearning by increasing an individual’s motivation toengage in failure learning activity (reporting failuresand errors; willingness to discuss possible solutions)because the fear of negative consequences to self-image, status, or career are lower when psychologi-cal safety is high (Edmondson, 1996; Tjosvold et al.,


2004). Surveying three firms in the software, elec-tronics, and finance industries Carmeli and Gittell(2009) found thatpsychological safetywaspositivelyassociated with failure-based learning behaviors.

At the group level, group norms shape learningfrom failure: compliance with norms motivates in-dividuals to identify and record failures and to act toprevent them (Katz-Navon et al., 2009; Vogus &Sutcliffe, 2007; Zohar, 2002). When studying groupsin different functions in the same organization,sharing the same beliefs about how to cope withfailure exerted great influence on failure learning(Cannon & Edmondson, 2001).

Information sharing and interpersonal relation-ships within groups is another key element impact-ing how motivated teams are to learn from failures.Teams with cooperative rather than competitivegoals learn more (Tjosvold et al., 2004). Trust be-tween the members of a team affects learning moti-vation (Carmeli et al., 2012). Comparing hospitalswith similar characteristics but different failurelearning rates, Edmondson et al. (2001) found thatteam member error information sharing affected thelearning rate. In a similar fashion,managerial safetypractices also affect the motivation to learn—managers who demonstrate that safety is importantpositively affect error reduction (Katz-Navon et al.,2009). Lack of information sharing depends on theroutines, awareness of others’ knowledge, and statusdynamics within groups: a study found that slower-learning groups had teammembers that did not bringattention to errors as they assumed, often incorrectly,that other team members are already aware of them(Cannon & Edmondson, 2001; Edmondson et al.,2001).

According to Gersick and Hackman (1990), rou-tines are double-edged swords: on the onehand, theycan prevent team members from addressing failureby reducing theirmotivation to learn because changewouldupset the routine; however, on the other hand,routines create comfort within a group and lead toa climate where group members are more comfort-able sharing what went wrong.

A high team workload not only decreases the op-portunity to learn, but also considerably reducesteam motivation to learn from failure (Lawton et al.,2012), and it may ultimately affect the ability tochange because the team might be unaware of theneed to improve existing routines (Edmondson et al.,2001). Team’s ability to manage its workload is alsoimportant, and teams that do better at this werefound to improve their error rate faster (Lawton et al.,2012). To reduce errors and failures routines often

need replacing, but renewing routines is cumber-some, with preexisting routines being obstacles tochange; thus, unless there is a clear rationale for whyroutines must change, groups are likely to resistchange and may even implement defensive strate-gies with respect to learning processes, for example,by shifting responsibility and finding arguments todefend failing mechanisms (Hodgkinson & Wright,2002). Workload is a time-dependent opportunityand also impacts motivation to learn because de-cisions that are made under time pressure may re-quire individuals and groups to focus on a shorterterm horizon (Malone et al., 2007).

Organization-Level Motivation to Learnfrom Failure

Most of the studies on howmotivation affects errorand failure are conducted in the health-care context.There is a very real concern that hospitals are notreducing their error rates and are causing patientharm at a high rate (Kohn et al., 2000; Leape, 1994).The focus is often on how individual motivation toreport errors is affected by organizational factors.The problem of non-reporting is high (Ramanujam,2003; Zhaou & Olivera, 2006), which renders theanalysis of error causes incomplete. The reason foran individual to not report an error that he or shemade, or whichwasmade by someone close, is oftena lack of trust that responsible managers in the or-ganization will conduct proper analyses to de-termine the true causes. It is a common and simplesolution for organizations to blame an error on anindividual (Hofmann&Stetzer, 1996; Rathert &May,2007). Data supports organizational members’ beliefthat organizations often blame individual operatorsrather than trying to find true accident causes: A re-examination of industrial accident causes foundthat, in contrast with original analyses that had at-tributed 70 to 80 percent of accidents to operatorerrors, less than half of that, or 30 to 40 percent of theaccidents, were caused by operator errors (Perrow,1999).

In line with the tendency to blame the person whois closest to an error, factors that impede errorreporting and analysis at the organizational level canbe linked to the culture of blaming individuals ratherthan exploring other error causes (Khatri et al., 2009),ward climate (Lawton et al., 2012), whether health-care work units are patient-centered (work is plannedaround the patient’s needs) (Rathert & May, 2007)and that when greater distance between profes-sional groups in healthcare reduces the willingness


of orderlies and nurses to admit to or discuss errorswith doctors (Khatri et al., 2009). Organizationalclimate is a determinant that trickles down toaffect both group and individual-level learningfrom failure.

Motivational factors that have been studied out-sidehealthcare are different. Basedon the behavioraltheory of firms, which states that organizations aremoremotivated to act andchangewhen theyperformworse than they did in the past orwhen they performworse than their competitors or peers (Cyert &March, 1963), Baum and Dahlin (2007) found thattrain companies that have higher accident costs thantheir peers learn faster, but mainly from others’ ex-periences. Learning from others’ and not one’s ownexperiences might be due to poor failure perfor-mance: the organizationmight need external ideas ofhow to change behaviors. Another motivation studyfound that when an automobile manufacturer wasforced to recall products and thus its reputationwas challenged, the likelihood of future recalls waslowered (Haunschild & Rhee, 2004; Rhee, 2009). Theeffect in the auto-recall case was curvilinear: low-and high-reputation car companies reduced recallsless than companies with medium reputations did.In a study that focused on how motivations changeover time, Haunschild et al. (2015) found that a re-cent failure (in their case, a pharmaceutical productrecall) made the organization work to reduce futuresuch events; however, as time passed, other perfor-mance metrics, such as profits, reclaimed the orga-nization’s attention and lowered the focus on failurereduction.

Some findings on how attention impacts motiva-tion contradict one another. Although more mediacoverage after an accident motivates greater in-vestment in infrastructure for train companies, thusreducing accident risks (Desai, 2011), more mediacoverage of the near-misses involving air-trafficcontrollers instead reduced the effect of prior near-misses on learning (Desai, 2014). This divergencecould be explained by near-misses looking alarmingto outsiders as an actual failure (airline accident)would have catastrophic consequences, whereasorganizationalmembers, by contrast, see a near-missas a success as an errorwas rectified and a failurewasavoided (Dillon & Tinsley, 2008; Kessels-Habrakenet al., 2010).

ABILITY TO LEARN FROM FAILURE

Ability to learn from failure concerns the capacityto identify and report failure; understanding failure

leads to finding and implementing solutions to pre-vent future failures. Individual and group-levelstudies are concerned with training, emotional re-sponses, shared goals, and managerial style, as wellas the interaction between ability and motivation.Organization-level studies rarely measure directlyability, but rather conclude that the unobservablevariances across units or organizations are due todifferences in ability. In healthcare, where geogra-phy makes competition between hospitals less of anissue, checklists are used for the transfer of bestpractices across units, which raises the ability tolearn in organizations.

Individual- and Group-Level Ability to Learnfrom Failure

The question of how to improve individuals’ability to learn from their errors and failure experi-ences has led to a series of studies that focus oncomparing different trainingmethods and the role ofpost-event reviews. Keith and Frese (2005) com-pared error management training, which explicitlyaddresses individuals who make errors duringtraining and uses these errors as learning exercisesand error avoidance training, which instead focuseson preventing participants frommaking errors. Errormanagement training enhanced individuals’ capac-ity to cope with, and generate solutions to, newproblems. Error management training also led tobetter emotion control and metacognitive activity,which in turn improved individuals’ ability to copewith new problems. Individual differences interactwith training method so that individuals with bettercognitive ability and higher openness to experiencewere found to perform better during error manage-ment training than if they had received no training oronly training with a focus on error avoidance situa-tions. Error management training not only providestraineeswith opportunities tomake errors but also toreceive informative feedback; overall, these pro-grams increase an individual’s ability to learn fromfailure. Thus, the ability to learn from failure can beactively developed, and such training is more effec-tive when it is paired with high motivation to learnfrom failure (Naveh et al., 2005).

A laboratory study onhow to enhance individuals’ability to draw lessons from previous experiencesfound that after-event reviews of both successful andfailed events had a positive impact on individuallearning (Ellis and Davidi, 2005). The after-eventreviews followed a similar but more extensive pro-cess than that of error management training because


it also included successful events. Although theparticipants’ mental models of failure events werericher than those of success events, the performanceimprovements were greater when the after-event re-views focused on both failures and successes. Ina follow-up study, any type of after-event review(success, failure, or both types of events) was foundto lead to better performance comparedwithno after-event review. Interestingly, for individuals who ex-perienced successful events, the after-event reviewsthat focused on the failure factors that led to thegreatest performance improvements had a greaterimpact than no reviews, reviews that focused onsuccess factors, or on both success and failure factors(Ellis et al., 2006).

Individual differences in how failure is processedhave an impact on failure learning. Studying threetypes of coping orientations loss, restoration, and os-cillation, Shepherd et al. (2011) analyzed how theseorientations affected (self-reported) learning. Lossorientation refers to the explicit processing of a failureto break the emotion that is associatedwith the failure(a failed project). Restoration orientation refers tosuppressing feelings of loss and instead proactivelyfocusingon the tasks that arise as a consequenceof thefailure rather than preventing future failures. An os-cillation orientation refers to moving back and forthbetween lossand restorationorientations. Individualswho have stronger loss and oscillation orientationsreported a better ability to learn fromprevious projectfailure than those with a restoration orientation. Ap-parently, the necessary element is the capacity toemotionally disconnect from the failure, which sug-gests that effective learning involves managing theemotions that are evoked by a failure.

An individual’s ability and motivation to learnfrom failures are affected by emotional states. Theability to learn is enhancedwhen the environment isemotionally supportive: Individuals must feel com-fortable applying their knowledge and acquiringnew knowledge to learn from failures. An in-dividual’s perception ofpsychological safety and thequality of his or her relationships with others in anorganization are positively associated with failurelearning (Carmeli and Gittell, 2009). High-qualityrelationships, which are manifested in shared goals,shared knowledge, and mutual respect, not onlypromote psychological safety, but also enhance in-formation processing and coordination capacity,which in turn have positive effects on the capacity tolearn from failure.

Individuals can also encourage failure learning atthe group level with a positive leadership style,

which includes clear direction and effective coaching(Cannon & Edmondson, 2001; Katz-Navon et al.,2009): leadership style can motivate learning;however, it also reflects the group’s ability to learnand a leader’s ability to enhance group learning.For example, when a CEO fosters psychologicaltrust in a top management team (between the CEOand the team, and within the team), the team ismore likely to engage in failure learning and toproduce high-quality strategic decisions (Carmeliet al., 2012).

Because individuals in organizations are embed-ded in groups, studies have stressed teamwork to bean important factor for failure learning (Baker et al.,2006; Morey et al., 2002). Many studies of abilityinvolve overlapping and interactive effects betweenmotivation and ability, and some determinants arerelated to both mechanisms. Efficient teamwork re-lies on coordination and communication, which canbe activelypromoted (Baker et al., 2006) using formaltraining to improve team collaboration (Morey et al.,2002). Ability to foster cooperative goals also triggersfailure learning (Tjosvold et al., 2004). Most ability-type factors can be taught, and they can, in turn,nurture groupmotivation to learn. Other group-levelfactors that improve group learning ability are groupmember stability, understanding of team processes,and training to reduce attribution biases (Buljac-Samardzic et al., 2012; Morey et al., 2002; Naquin &Tynan, 2003).

A process study of how ability is developed fo-cuses on Israeli fighter crews (Ron, Lippschitz, &Popper, 2006). To reduce errors in flight procedures,crews not only relied on their own learning based onflying more missions and thus learning through di-rect observation and experience; but also becausethey knew that subjective perceptions are in-complete and sometimes faulty, given the intensityand massive information processing that is requiredwhen piloting a fighter jet, after each mission theentire cadre held a debriefing. In the debriefing,crews watched footage from aircraft cameras whiletalking through their perceptions of what had oc-curred.The reviewprocess allowed them to comparetheir perceptions with the footage, clearly see howimprecise real-time perceptions are, which helpedthem to make corrections, and convert subjectiveexperiences to objective ones. The review processand the debriefing are imposed by the organiza-tion, it enhanced the teams’ abilities and alsoincreased information-based opportunities as thedebriefing sessions provided the crews with addi-tional information.


Organization-Level Ability to Learn from Failure

We found fewer studies on organizational-levelability than on organizational-level opportunity ormotivation. A study analyzing failure reduction acrossclinics that use a new heart surgery method used anindirectmeasure of clinical ability (Pisano et al., 2001).Carefully establishing that given the same opportunity(cumulative number of procedures, which had a largeimpact on learning) and motivation, organizationsdiffered in how well they converted experience intohigher performance: the 16 clinics in the study dem-onstrated different learning-curve slopes. After rulingout other opportunity-based explanations, the con-clusionwas that differences across clinicsmust be dueto (unobserved) ability. An article argues that organi-zational form, whether an airline is a specialist orgeneralist, can influence its ability to learn from failure(Haunschild and Sullivan, 2002). Generalists havea more complex organizational structure with the po-tential to complicate information processing and co-ordination, which in turn hampers learning. In theairline industry, specialists comparedwith generalistswere better able to learn from failures with heteroge-neous, that is,more complex causes. Similarly, smallerhospitals (more specialized), compared with largehospitals (moregeneralists), havebeen found toengagein more learning behaviors related to major adverseevents and near-misses (Ginsburg et al, 2010).

Moredetailedmeasures of abilitydemonstrate thatability affects the absolute number of errors aswell asthe reduction of errors: hospitals achieve fasterlearning and fewer errors by implementing protocolsand checklists developed from best practices acrossthe industry (WHO, 2017; Thornlow & Merwin,2009; Thomassen, Storesund, Søfteland, & Brattebø,2014: 2–13). Checklists were originally introducedin aviation, where they have been partly creditedwith the rapid decline in accidents (Clay-Williams &Colligan, 2015). Hospitals with patient-centeredunits were better at processing and reporting errorsand near-misses, which suggests that the organiza-tion of work matters for an organization’s ability tomanage failures (Rathert & May, 2007).

Table 1 summarizes reviewed papers along the twodimensions we used to structure the literature review:learning mechanism (opportunity-motivation-ability)and level of analysis (individual-group-organization).

SYNTHESIS ACROSS LEVELS OF ANALYSIS

A large body of literature finds that individuals,groups, and organizations learn from prior failure

experience. Information opportunities positivelyaffect learning rate. The richer the information, thefaster the reduction in errors and failure. Failureexperiences generally contain more informationthan successful experiences (Kim & Miner, 2007).Most successes are “business as usual” contributingto learning-by-doing and other automatic responses,thus enforcing existing routines. By contrast, failurestrigger analyses and have greater potential to im-prove routines (Stan & Vermuelen, 2013). A lack ofroutines for error and failure management stops theinformation from passing through an organization(Gersick&Hackman, 1990;Kim&Miner, 2007). Largermagnitude, more frequent, and salient errors havegreater information content and, hence, learning op-portunities (Baum & Dahlin, 2007; Chuang & Baum,2003; Desai, 2011; Madsen, 2009). Near-misses alsoprovide information about how to recover from a badsituation, which increases opportunities to learn (Kim& Miner, 2007). In general, studies find that “more isbetter” in regard to information-based learning oppor-tunities; however, wewould expect an overloadwhenfailures and errors are too frequent (Dahlin & Roulet,2014). Information overload that leads to limited fail-ure learning can hamper the ability to learn from fail-ure. Accepting frequent errors due to informationoverload, that is, not learning from them, can also bea sign of low motivation to learn. These two factors—low ability and low motivation to learn—may be dif-ficult to distinguish (Baum & Dahlin, 2007; Dahlin &Roulet, 2014).

Time-based, or temporal, learning opportunitiesoperate in the same direction and in a similar fash-ion as information-based learning opportunities:a lower workload leads to fewer errors and failuresas workload reduces the time that is available forlearning (Lawton et al., 2012; Malone et al., 2007).Nevertheless, some groups are able to analyze andprocess information faster, taking advantage of tem-poral learning opportunities to enhance their futureperformance (Edmondson et al., 2001). Whether tem-poral opportunities can trigger learning also dependson whether individuals in organizations have auton-omy to process and reflect on the errors and failuresthat they encounter (Kerr, 2009; Stern et al., 2008). Tothat end, organizational design (e.g., workload, taskautonomy) has the potential to lead to latent failurethat hampers learning opportunities.

Even when the opportunity to learn from failure—whether information- or time-based—is high, learn-ing may not occur. Although opportunity studiesare more concerned with what enables learning, mo-tivation studies focus on why learning does not


TABLE 1Factors Investigated in Failure Learning Studies Classified by the Level of Analysis and Mechanisms

Opportunity Motivation Ability

Individual level Perceived autonomy (Sternat el., 2008)

Motivation to learn (Zhao, 2011) and safetymotivation (Buljac-Samardzic et al., 2012;Neal & Griffin, 2006; Probst, 2015)

Training (Gully et al., 2002; Keith& Frese, 2005)

Situational learning (Stern at el.,2008) Attribution (Ilgen and Davis, 2000; Nisbett

andRoss, 1980;Ellis,Mendel,&Nir, 2006;Diwas et al., 2013; Naquin & Tynan, 2003)

After-event reviews (Ellis &Davidi, 2005; Ellis, Mendel, &Nir, 2006)Experience (Diwas, Staats, &

Gino, 2013)Emotion (Edmondson, 1996; Paget, 1988;

Keith & Frese, 2005; Neal & Griffin, 2006;Shepherd et al., 2011; Zhao, 2011)

Coping orientation (Shepherdet al., 2011)

Psychological safety (Carmeli & Gittell,2009)

Relationships with others(Carmeli & Gittell, 2009)

Perception of outcomes (Dillon & Tinsley,2008)

Coping orientation (Shepherd et al., 2011)Group level Member rotation (Argote &

Todorova, 2007)Group norms and team orientation (Katz-

Navon et al., 2009; Tjosvold et al., 2004;Vogus & Sutcliffe, 2007; Zohar, 2002)

Failure climate (Buljac-Samardzic et al., 2012)

Group diversity and intergroup-linkages (Chuang et al., 2007;Tucker & Edmondson, 2003)

Psychological safety (Edmondson et al.,2001)

Leadership style (Buljac-Samardzic et al., 2012; Cannon& Edmondson, 2001;Zhao,2011)Team stability (Edmondson,

1996)Tacit belief about failure (Cannon &

Edmondson, 2001; Edmondson et al.,2001)

Training (Morey et al., 2002)Routines to gather information(Lawton et al., 2012;Edmondson et al., 2001;Tucker & Spear, 2006)

Leadership style (Cannon & Edmondson,2001; Carmeli et al., 2012; Katz-Navonet al., 2009)

Membership stability (Buljac-Samardzic et al., 2012)

Autonomy (Kerr, 2009) and load(Malone et al., 2007)

Safety climate (Zohar & Luria, 2005)

Understanding of team process(Baker et al., 2006)

Status dynxamics (Edmondson et al., 2001)Workload (Lawton et al., 2012)

andwork process (Edmondson,1996)Routines (Gersick & Hackman, 1990)

Coordination andcommunication (Baker et al.,2006) to develop cooperativegoals (Tjosvold et al., 2004)

Debriefing and reviewing abilities (Ronet al., 2006)

Resistance to change (Hodgkinson&Wright,2002)

Workload (Lawton et al., 2012) andautonomy (Kerr, 2009)

Organizationallevel (includesinter-organizationalfactors)

Own and others’ failureexperience and processes tocollect information on thosefailures (Baum & Dahlin,2007; Chuang & Baum, 2003;Haunschild & Rhee, 2004;Haunschild & Sullivan, 2002;Kim&Miner, 2007;Madsen&Desai, 2010;Madsen,Dillon&Tinsley, 2016; Tucker &Spear, 2006)

Performance aspirations (Baum & Dahlin,2007)

Organizational form (Hanschild &Sullivan, 2002)

Own success experience(Madsen & Desai, 2010;Pisano et al., 2001)

Media attention (Desai, 2011, 2014;opposite results)

Failure climate (Khatri et al.,2009)

Recency of event (Haunschild,Polidoro & Chandler, 2015)

Reputation (Haunschild & Rhee, 2004;Rhee, 2009)

Error management culture (vanDyck et al., 2005)

Magnitude of event (Desai,2011; Madsen, 2009)

Safety climate (Hofman & Stetzer, 1996) Leadership style (Desai, 2015)

Complexity of problems (Stan &Vermuelen, 2013)

Other’s similar errors (Mitsuhashi, 2012) Culture, workload (Kralewskiet al., 2005; Malone et al., 2007)

Organizational size (Desai,2015; Slonim, 2007)

Post-experience reviews (Ron,Lipshitz & Popper, 2006) andexternal pressures (Haunschild& Rhee, 2004).

Near-failures/near-misses (Kim& Miner, 2007; Kessels-Habraken et al., 2010)

Patient-centered hospitals,climate (Rahert & May, 2007;Lawton et al., 2012)

Geographic proximity (Kim &Miner, 2007)

Standardized procedures/protocols (Thornlow&Merwin,2009)

Organizational size (Ginsburget al., 2010)


happen. Conditions under which individuals attri-bute the causes of errors and failures to other factors,groups attribute the causes of errors and failuresto factors other than the group collective, and orga-nizations attribute the causes of errors and failuresto individuals rather than organizational factors re-duce the motivation to learn, which leads to lowerror and failure reporting and lower learning rates(Chuang et al., 2007; Diwas et al., 2013; Ellis et al.,2006).A cost-benefitmodel that is proposed byZhouandOlivera (2006) offers anoverarching explanationto this problem: the unbiased reporting of errors isonly expected when the perceived reporting cost islow toboth an individual andhis or her organization,and the perceived benefits for reporting is high toboth as well as to any victim(s) who are associatedwith the errors. All other combinations of costs andbenefits distort the motivation to report and whatwill be reported, which in turn affects the opportu-nity to learn from failure. In empirical studies, cli-mate or psychological safety can be thought of asa reporting cost. Patient-centered climate (Rathert &May, 2007), low hierarchical distance, non-blamingcultures (Khathri et al., 2009), and high psycholog-ical safety (Tjosvold et al., 2004) lead to betterreporting and learning. The perception of the bene-fits to reporting is affected by the same factors;however, this side of the explanation for failurelearning is less investigated.

On the organizational level, conflicting goals, of-ten safety and profitability, affect the motivation tolearn from failure: A recent failure highlights safetygoals for employees; however, as time passes, thefocus reverts to financial performance metrics(Haunschild et al., 2015). This highlights the risk oftaking failure reduction for granted, assuming thatlearning is irreversible. Unless learning is embeddedin physical artifacts (better brakes, a new IT tradingsystem) or it becomes a part of organizational rou-tine, there is always the risk that error and failurerates will reverse.

Ability studies show that training programs andafter-event analyses can improve individual and groupabilities to correctly analyze situations (Keith & Frese,2005). It is clear that such training is quite common,with checklists being used in different industries, suchas aviation and healthcare, to enhance organizationalsafety work (Clay-Williams & Colligan, 2015).

Integration across Mechanisms

Most studies focus on one or (at most) two mech-anisms, opportunity, motivation, or ability, and we

know that they all matter for failure learning. Weknow less about how the mechanisms affect oneanother. Whereas some studies have consideredmoderating effects (Diwas et al., 2013; Madsen &Desai, 2010; Shepherd at al., 2011), mediation ismore seldommentioned. If opportunity to learn setsthe stage (providing information for failure analysisthat can improve routines and failure responses),motivation causes actors to be willing to attend tosuch information, and ability is the conversion ofopportunity into higher performance. What we donot clearly know is how, and whether, these threemechanisms jointly affect failure learning.

Theoretical arguments about how to combine thedifferent mechanisms argue for a three-way in-teraction (Blumberg & Pringle, 1982; Reinholt et al.,2011). However, it may just as well be a moderation–mediation process where the opportunity to learninteracts with the motivation to learn, and such in-teraction is mediated by the ability to learn, whichresults in failure learning. The empirical research fo-cuses on one or two of themechanisms, atmost testingtwo-way interactions (for instance, how ability andmotivation jointly determine learning). The notionthat reality is complex is illuminated by differentfindings, some of which find interactions betweenfactors, others of which find that one factor affectslearningbut ismediatedbyasecondfactor.Motivationis thus found to affect ability, but ability also affectsmotivation (Lawton et al., 2012; Hofman & Stetzer,1998) with both paths leading to learning. Further-more, motivation and opportunity jointly lead tolearning (Haunschild et al., 2015), opportunity affectsability, which leads to learning (Stan & Vermuelen,2013), and opportunity and motivation interact toproduce learning (Baum&Dahlin, 2007). It is clear thatthe interplay between the mechanisms is more com-plex than we originally thought, and this promisesmany different ways to stimulate failure learning.

MOVING BEYOND THE THREE FAILURELEARNING MECHANISMS

The articles in the review section covered both errorand failure reduction. Although there are many simi-larities between them, errors and failures are differenttypes of events (Zhao & Olivera, 2006). Errors are mis-takes, slips, or violations of procedures, and theymightor might not lead to an adverse outcome (Rasmusen,1982; Reason, 1990). Failures are adverse outcomes,3

3 We use the terms adverse outcomes, failures, and un-desirable outcomes interchangeably.


such as accidents, unexpected patient deaths, orbankruptcies (Tucker & Edmondson, 2003). Althoughreducing errors should reduce failures, in this sectionwe will discuss how these two types of events haveacomplex relationship; throughabetterunderstandingof this relationship, we can also better understand thedifferences in learning rates.

Differentiating Process and Outcomes

Whenwe study failures, that is, adverse outcomessuch as product recalls, accidents, bankruptcies, orunexpected hospital deaths, we assume that thecause of an adverse outcome is an erroneous process.Correspondingly, when we study traditional learn-ing with successful outcomes, we assume that pro-cesses that lead up to the outcomes are correctlyexecuted. Questioning the strong link between thecorrectness of processes and outcomes, there is anincreasing interest in situations in which erroneousprocesses can still result in good outcomes, such asnear-misses (Kim & Miner, 2007) and latent errors(Ramanujam, 2003; Reason, 1990). It is also possible,but rarely discussed, that a correct process can leadto an adverse outcome, such as a patient dying, evenafter a well-executed surgery.4 We propose that tobetter understand failure learning, we must de-couple processes and outcomes, or more precisely,independently assess whether a process is correctlyperformed and whether an outcome is desirable. Wealso argue that different settings have different fre-quency distributions for the process–outcome pairsand that opportunity, ability, andmotivation to learnfrom failures (and successes) depend on this distri-bution, which explains why lessons in failurelearning in one setting can be difficult to translate toanother.

If we regard processes as either correct or errone-ous and outcomes as successes or failures, we end upwith four possible process–outcome combinations(see Table 2).

Success and failure learning. A correct processwith a favorable outcome represents traditionallearning in which the outcomemotivates actors whocontinuously improve and/or exploit the process tofurther enhance the outcome. This is whatwe expectfromproduction learning curves (box 1 inTable 2). Afaulty process with an undesirable outcome repre-sents the “normal” failure learning case—an error is

made, which yields a bad result (a train driver fallsasleep, ignores a signal, and collides with anothertrain; box 4). The main thrust in success learning ison how to improve existing processes to enhance thenumber of successful operations per time unit andthus lower the cost per unit produced (Argote, 2012;Yelle, 1979). The main thrust in failure learning isthe analysis of how to improve the processes, reduceerrors, and thereby lower the risk of failure or reducethe number of failed operations per time unit (or asa share of all operations) (Reason, 1997).

The assumption is that box 4 is a good represen-tation of failure situations: if there is a failure out-come, there must be a preceding error. Asa consequence, failures should be reduced whenerrors are reduced, and this approach of error re-duction lies behind much successful failure re-duction (Reason, 1997; van Dyck et al., 2005).Similarly, the assumption is that successful out-comes are due to an error-free process. However,there are two other possibilities in the matrix thatcomplicate learning: the off-diagonal combinationswhere (1) an error has no effect on the outcome, thatis, does not lead to a failure (Ramanujam, 2003) or (2)when there is a failure outcomewithout an error thathas been committed (e.g., an “act of God”). The veryexistence of the off-diagonal combinations weakensthe link between a process as being either correct ornot, and its outcome as being either successful or not.We label the off-diagonal combinations spurioussuccesses and spurious failures. An increase inspurious events leads to noisier learning processesbecause the cause–effect analysis becomes morecomplex, which in turn introduces difficulty inlearning fromboth successes and failures.We expectthat both spurious successes and failures complicatelearning in general and failure learning in particular.Spurious successes and failures are likely to shift theattention of individuals and organizations awayfrom the “true” causal effects of failure, makingfailure learning challenging. Because successes tend

TABLE 2232 Table of Four Process and Outcome Combinations

Outcome of event

Success Failure

Process/behavior/routine

Correct 1. Traditionallearning

2. Spuriousfailure

Faulty 3. Spurioussuccess

4. Traditionalfailurelearning

4 Over time, the number of different health interventionshas greatly increased, many of which have been directedtoward terminally ill patients (Maile, 2012).


to be vastly more common than failures, failurelearning should be more sensitive to the occurrenceof spurious events.

Spurious success. Not all errors lead to bad out-comes. We expect that both the motivation andability to learn from an erroneous process is lowerwhen an organization experiences a spurious suc-cess, that is, there is no negative outcome (Table 2,box 3). Further lowering the learning ability, manyprocess errors are unreported (and sometimes alsounobserved), and unreported errors are known aslatent errors (Ramanujam, 2013; Reason, 1997). La-tent errors can lower failure reduction becausea near-failure without an adverse outcome canstrengthen an erroneous behavior. The lower thelikelihood that an errorwill lead to a failure, themorethe error is accepted and the lower the motivation tocorrect the error or to learn from it is (Dillon &Tinsley, 2008; Banja, 2010). The motivation to re-duce errors is also compromised because risk per-ception changes when actors experience errorswithout negative effects (a train driver falls asleepbut wakes up before missing the signal, or, if thedriver misses the signal, no other train is on the line,thus, there is no collision—the conclusion is thatbeing tired on the job is not such a dangerous thing).Japanese nuclear firms did not learn from otherfirms’ errors without adverse outcomes when theseerrors were similar to non-adverse outcome errorsthe focal firm had experienced itself (Mitsuhashi,2012). This suggests that spurious success alsolowers the motivation to learn from others’ errors:Knowing that other firms in the industry experiencesimilar errors without adverse consequences signalsthat these errors pose no real risk and thus requirelittle attention. When latent errors start being ac-cepted by organizational members as not leading toadverse events, we obtain what is called normaliza-tion of deviance (Banja, 2010; Vaughan, 1996). Nor-malization of deviance involves accepting errors andrule breaking. Deliberate rule-breaking is usuallya dismissal of rules that are considered to be ill-conceived or overly complex. It is a gradual process(Vaughan, 1996) and inmost of the cases, normalizeddeviance has no negative outcome.

Normalized deviance is often exposed after a dra-matic failure event leads to patient death (Maxfield,Grenny, Patterson, McMillan, & Switzler, 2005),nuclear meltdown (Dekker, 2011), spectacular trad-ing losses (The Economist, 2014), or the crash ofa space shuttle (Vaughan, 1996). Normalized de-viance means that there is an implicit or explicitagreement among organizational members to ignore

certain safety procedures or regulations. One effectof normalized deviance is that it complicatescause–effect analyseswhen a failure strikes: it is easyfor analysis to focus on the deviance behavior, whichmight not be the main cause of the failure. After all,some procedures or regulations are probably out-dated or ineffectual and ignored for good reasons. Inhealthcare, normalized deviance often involves theviolation of safety rules that impede work flow andsignal a lack of trust in operators (Banja, 2010).Normalized deviance can also come from in-stitutional logics that became dominant over timeinside an industry despite their clashing withbroader order values and beliefs that lie outside theindustry (Roulet, 2015; Shymko & Roulet, 2017).

Despite a weak link between latent errors andfailure outcomes (Dekker, 2011), some settings stillexhibit strong learning under such circumstances(airline safety and automobile safety), and it wouldenhance learning if we can determine factors thattrigger learning from latent errors. First, the humanerror and safety literature is focused on errors re-gardless of outcomes, with a clear acceptance thatadverse outcomes are quite infrequent; however,despite the low probability of an error that leads toa failure, there is nevertheless a link (Reason, 1997).When the focus is on errors rather than outcomes,both motivation and ability to learn should be en-hanced. Creating agencies whose mission is errordetection and reduction, such as the NTSB in trans-portation, leads to a low tolerance for errors. Someagencies have the power to close down or fine error-prone organizations with the aim of limiting suchtrade-offs in organizations, thus enhancing the or-ganizations’ motivation to engage in failure re-duction.Organizations’ and theirmembers’ ability toreduce errors is also improved as regulators helpwith cause–effect analyses and recommend or reg-ulate safer behaviors (FRA, 2016).

Spurious failures. The fourth process–outcomecombination is the case where a faultless processproduces an adverse outcome (Table 2, box 4). Forinstance, well-executed surgery can still lead toa patient dying, or correct driver behavior can pro-duce an accident.We call this a spurious failure, andthis particular outcome is problematic for failurelearning for a number of reasons. A failure outcomeoften triggers a search for causes even if there is none.Such a search risks misattributing the process asfaulty, and there is a risk that the organization willreplace a good routine with a worse one or makeinefficient changes, and thus the ability to learn iscompromised. The good-process–bad-outcome


option is fairly rarely studied; however, it is likely tobe frequent in complex settings where many pro-cesses co-occur and involve many different actors.Because organizations are twice as likely as an im-partial observer to assign blame to an operator aftera failed event (Perrow, 1999), spurious failures arerisky for individuals who might be unfairly blamedfor adverse outcomes when the organization looksfor failure causes. Thus, spurious failures can lead tolower trust and lower reporting of actual errors,which lowers the motivation to learn (Hofmann &Stetzer, 1998).

Healthcare is an obvious case where we expectfrequent spurious failures because very sick patientseventually tend to die regardless of treatment. Staff isacutely aware that many failures (such as death) oc-cur for reasons that are unrelated to any proceduresthat they perform, and this leads them to accept badoutcomes as an unavoidable part of everyday activ-ities. Although the acceptance of adverse outcomesis necessary, there is a risk of acceptance spillingover and allowing errors and latent errors to be for-given: Because death is an expected and even un-avoidable outcome formanypatients, evenwhen thecause of death is an error instead of an underlyingdisease and the outcome should be recorded asa failure, the high incidence of spurious failuresmight mean that errors are not detected. In addition,even if an error is detected, it might be ignoredand, therefore, not corrected (Kohn, Corrigan &Donaldson, 2000). In other words, normalized de-viance can be affected by spurious failures as well asby spurious successes.

In summary, the more frequent spurious learningand failure, or bad–good combinations, there are(boxes 2 and 3), the more difficult it is to performacorrect causal analysis. The combination of processerror—goodoutcome isprobablymore common thanthat of the process error—bad outcome. An analysisof airline crews found that an error was made in thecockpit during a flight at least every fourminutes butthat very few incidents or accidents resulted (Bird,1966; Reason, 1997). Similarly, nurses commit errorsonce every hour; however, this rarely leads to badoutcomes (Tucker & Spear, 2006). By contrast, wehave almost no information about how often a cor-rect process results in an adverse outcome. Learningmotivation is low when the off-diagonal events arefrequent, as is the opportunity to learn given thenoisy information.

When there are many processes that can simulta-neously go wrong, attending to all potential errors be-comes cumbersome, and this also makes cause–effect

analyses difficult. Simulations are well suited to in-vestigating these trade-offs as well as experimentingwith the complexity of tasks, the number of involvedparties, risk levels, and how these factors affect moti-vation and ability to learn (Denrell, 2003).

The Impact of Spurious Successes and SpuriousFailures on Slow- and Fast-Learning Settings

Noise and uncertainty in the process–outcomerelationship help to explain learning-rate differencesacross settings (Rasmussen, Nixon, &Warner, 1990).In settings withmore off-diagonal outcomes (boxes 2and 3), we expect lower learning rates as both abilityand motivation to learn are lowered, and causal in-ferences aremore difficult to draw. Spurious success(box 3), for example, is more likely to occur in situ-ations in which tasks are easy and the rate of successis naturally high, whereas spurious failures (box 2)will, by contrast, be more common when tasks arecomplex, the chance of making an error is high andthe risk of failure is also high. In the health-caresector, relatively routine procedures would be ex-pected to lead to a higher rate of spurious success. Bycomparison, complex and high-risk surgical opera-tions are more likely to lead to spurious failures.Those contexts offer fewer opportunities to learnbecause there is less information on which to drawfor further success. The learning process is alsohampered by the difficulty in assessing and takingstock of ability: the more spurious failures there are,the greater the doubt about current abilities will be.We also expect that motivation to learn will be lowerin settings with high rates of spurious failure andsuccess given the ambiguitywith regard to cause andeffect.

FUTURE RESEARCH DIRECTIONS

How can organizations best learn from errors andfailures? We will suggest a number of approachesthat are based on the ideas of maximizing the triad oflearning mechanisms, opportunity, ability, and mo-tivation while taking into account the role of spuri-ous failures and spurious successes.

Opportunity to Learn from Failure

Using multiple sources of event information andthe role of regulations. What can organizations dowhen they experience few failures but still want toreduce future failure risk? There is an increasingemphasis in learning studies on vicarious learning in


the form of organizations’ learning from similarothers’ failures, successes, and near-miss experi-ences (Baum&Dahlin, 2007; Haunschild & Sullivan,2002; Kim & Miner, 2007; Madsen & Desai, 2010),and thus one way to increase learning opportunitiesis to learn from others. Moreover, information aboutevents is also provided by an array of industrystakeholders such as the press, unions, regulators,industry associations, insurance companies, equip-mentmanufacturers, trade press, and academics. Allof these groups have an interest in failure reduction,and some are mandated to collect data, investigateaccidents, and issue recommendations. They pro-vide information, analysis, and suggestions on howto reduce failures, and some of them are very active.However, management scholars have mostly ig-nored these stakeholders and the role that theyplay in identifying, analyzing, and suggesting rem-edies for failures. As a consequence, there is anomitted variable bias in many studies, which over-estimate the effect of one’s own or others’ ability tolearn from failures (Dahlin & Roulet, 2014). In addi-tion, it would be interesting to examine the relativeimpact of different learning sources on failurelearning to better understand stakeholder roles. Wecall for future studies in management to includemore industry stakeholders, or at least to control fortheir actions to better understand the sources oflearning. Although policy studies are engaging withthis question to study the effect on an entire industry(Silbey, 2009; Dekker, 2011), they rarely analyzefirm-level factors.

Some key questions to ask when investigating therole of multiple parties are the following: Given dif-ferent learning rates across industries; are therefewer sources of learning in slow-learning settings?Can a slower learning rate be explained by a loweropportunity to learn?

Noise also lowers the opportunity to learn: Ifslow-learning settings are more likely to combinea high failure volume and a high spurious failure vol-ume, is it possible that lower failure learning rates aredue to the difficulty to learn from spurious failures?Spurious failures create a great deal of noise, whichmakes cause–effect analyses complicated and makesmore common failure cases (erroneous process—adverse outcome) difficult to analyze. Exploring thesequestions can further advance our understanding ofthe differences in failure learning across settings.

Opportunity and the transfer of learning.One way to transfer best practices developed in ahigh-performing organization or industry to a lower-performing organization or industry is to use

checklists (Degani & Wiener, 1993; Thomassen,Storesund, Søfteland, & Brattebø, 2014). Checklistsare required in aviation and their success has led totheir adoption in healthcare. They are, for example,increasingly used in surgical procedures. Checklistshave been effective in accelerating learning, withmore rapid failure reductions in units that use sur-gery checklists than in units that do not use surgerychecklists (Walker, Reshamwalla & Wilson, 2012).However, in a meta-analysis of checklist studies,Thomassen et al. (2014) reported either improve-ment or no effects in the use of checklists. Whensome non-learning situations were more closely an-alyzed, it was found that the checklists were notproperly implemented—some surgeons have resis-ted their use (Leape, 2014).

Checklists are also used for data collection andanalysis by US transportation safety agencies, suchas the National Transportation Safety Board whenthey investigate accidents and have helped to reduceairline and train accidents (NTSB, 1998). As thetransportation sector is heavily regulated, allagencies are tasked with safety interventions andtrained in using systematic tools, although it is notclear if enforced checklists use by external agencieswould be as effective in the health-care setting.Maybe healthcare is too complex (different pro-fessional groups, hierarchical structures that com-plicate communication between groups, a largenumber of diagnostic and treatment options, andcomplex information flow), thus requiring differenttools to facilitate failure learning beyond learning onthe procedural level? At the same time, there are fewother tools to facilitate information transfer that havebeen as extensively developed and whose impact isas well understood as the learning effects of check-lists (Leape, 2014). We wonder what other methodsand/or tools could be used to increase learning op-portunities in general and which methods and/ortools might be effective for slow-learning environ-ments in particular.

Ability to Learn from Failure

Ability is the learning mechanism that is mostdifficult to investigate, andwe find a large gap in ourunderstanding of how to improve ability. Althoughthere are case studies that conclude that failurelearning is difficult, with many factors that are usedto explain when learning will not occur (Baumard &Starbuck, 2005), there are fewer studies that explainhigh learning rates (i.e., a better ability to learn fromfailure).


Can ability to learn from failure be improved?After-event analysis and error management are waysto improve failure learning. Our review revealed thatpsychological safety and non-hierarchical environ-ments with good communication and coordinationwithin and between teams also lead tomore learningfrom errors and failures. This result then begs thequestion for organizations that lack good communi-cation and have low psychological safety: how canthey increase their failure learning ability? Can psy-chological safety be promoted by the same managerwho pushes the norms of non-reporting?What typesof action are necessary for organizations that wish toadopt the climate and norms of more successfulfailure learning organizations? Conversely, in orga-nizations with good managerial support for errorreporting andpsychological safety, howcan learningability be further enhanced?

How much of learning is automatic? How doesability evolve? Some performance improvements inthe learning-curve literature are virtually automatic;learning-by-doing is played out at the individuallevel (Thompson, 2012), and learning-by-doing im-plies that individuals’ abilities increase by them-selves (but plateau after fairly few experience cycles,ibid.). How important is automatic learning in thecontext of failure learning? We expect some types oferrors to diminish as operators gain experience. Forinstance, car drivers lower their accident risk withinthe first five years of driving, and we tend to ascribethis success to experiences. However, the failurelearning studies have ignored the automatic learningeffect. A general assumption is that performanceimprovements are due to active learning attempts.One of the most studied groups in the error researchis nurses (Tucker & Edmondson, 2003; Tucker &Spear, 2006); however, we see little discussion withregard to whether nurses’ error rates go down withtenure in the profession. Surgeons demonstrate fail-ure reductionover time, thus itwouldbe surprising ifwe would not see the same for nurses with respect toerrors. However, if the link between errors and fail-ures isweak, the effect of professional tenuremaynotbe a reduction in errors but a reduction in errors thatresult in failed outcomes, that is, in converting po-tential failures into near-miss events.

Again, considering the difference between set-tings, is there less room for automatic learning inslow-learning settings, thus requiring more de-liberate learning for failure reduction? Examiningthese questions is both theoretically and practicallymeaningful. These questions help to advance ourunderstanding as to how different types of learning

occur. The answers to these questions have impli-cations for practices as they help to develop in-tervention in organizations to enhance failurelearning.

Motivation to Learn from Failure

Scholars assume that safety and risk avoidance arecentral to any organization, such as airlines, miningcompanies, hospitals, or banks, and theyoften ignorethat most organizations to some degree of acceptedfailure. Safer practices compete with productivity-enhancing investments and as viable operationsare necessary for the future of organizations, pro-ductivity is usually prioritized over the potential riskof a future failure, impacting themotivation to invest infailure reduction (Haunschild et al., 2015). This is il-lustrated by airlines that experience more accidentsafter filing for Chapter 11 protection (ABCnews, 2005)and by a famous quote in the freight rail industry:“Uphill slow, downhill fast, freight comes first andsafety last” (Ahear & Schick, 2014). Low-prime–lending practices are risky behaviors that lead toshort-term gains but jeopardize organizations andthe banking industry. In other words, failure re-duction attempts will almost always compete withother activities, which is a situation that we mustacknowledge when theorizing about and investi-gating failure learning. In addition, some failuresare seen as being unavoidable by managers, andthey are relegated to productivity equations’ errorterms andmore or less accepted as a necessary evilrather than something to improve upon (Jovanovic& Nyarko, 1995). Trade-offs, therefore, help to ex-plain the low motivation to reduce failure risks(Haunschild et al., 2015). In many settings, regu-lators move in to change the balance, for instance,imposing fines if safety targets are not met. How-ever, it ultimately comes down to decisions that aremade by individuals and organizations on the coststhat they are ready to allocate to learning and fur-ther improving their learning rate.

This leads us to ask if slow-learning settings facestronger trade-offs between productivity-enhancingactivities and failure reduction.

Motivation and the Case of Non-Learning: IsMotivation the Key Factor in Failure Learning?

A handful of empirical studies describe settingswithout failure learning even when similar eventsprovide learning opportunities (Baumol & Starbuck,2005; Tucker & Edmondson, 2003). Their findings


led some scholars to question if failure learning isgenerally to be expected at all (Baumold & Starbuck,2005). Analyzing these studies, we find that theydescribe situations with low motivation to learn(Baumold & Starbuck, 2005; Eggers & Song, 2015),limited opportunity to learn in combination withlow motivation to solve underlying problems(Tucker & Edmondson, 2003), and potentially aninability to learn due to low motivation (Eggers &Song, 2015). A conclusion is that failure learningis difficult as it is only likely to happen when allthreemechanisms are sufficiently activated, and thatmotivation is a necessary condition for deliberatelearning.

In an in-depth case study of a large Europeantelecom firm that suffered 14 strategic failures withno learning, Baumol and Starbuck (2005) describelack of systematic reporting about failed projects,lack of interest in better understanding what wentwrong, and managers making external attributionsto explain away bad outcomes caused by internalfactors. The authors find opportunity but low-to-nomotivation to learn, which in the end resulted in nolearning. Low motivation involved managersexpecting that admitting failure would have nega-tive career ramifications and possibly also harmthe organization (many projects were imposed byexternal stakeholders and the firm was publiclylisted). Applying Zhao and Olivera’s (2011) cost-benefit reasoning, the cost to the individual man-agers was high, the benefit to the organization notclear, and hence, lowmotivation to report was to beexpected.

We find a similar argument around individualsusing external attributions rather than changing theirown behavior among Chinese serial entrepreneurs(Eggers & Song, 2015). Entrepreneurs with failedstartups were less likely to alter the way they struc-tured their companies than were entrepreneurs withsuccessful start-ups. Rather, they pursued the samefirm strategies and structures when launching a newventure which, in turn, increased the risk for failure.They preferred a new industrial setting which madethe authors conclude that this inability to learn is inline with self-serving attribution theory: Using ex-ternal attributions to avoid altering ones’ method ofworking is an individual-level defense mechanismdemonstrating both lack of motivation and inabilityto learn. By contrast, entrepreneurs with successfulventures who stayed in the same industry keptenjoying more success.

Tucker and Edmondson (2003) report high in-cidences of errors without organizational-level

learning in a study of nine hospitals known fornursing excellence. Front-line nurses that constantlysaw patients were confronted with many problems,such as errors and mistakes made by others, whenexecuting their tasks. Surprisingly, the nurses’ errorlearning was low despite their skill and motivation.The reason for low error learning is that the nursessolved theproblems themselves on anadhocbasis asthey rarely had time to deal with the underlyingcauses. As a consequence, the same errors were re-peatedly made and failure rates remained high. Theinsight from this study is that the nurses were highlymotivated to execute their work, got a confidenceboost in effectively managing problems created byothers, and had little time to provide feedback to theorganization. This is both a story of lack of motiva-tion that emphasizes that the motivation in questionis about addressing underlying problems caused byothers, and that the support structure did not offersufficient opportunity to get to the root cause of theproblems, both in terms of managerial support butalso the lack of time-based opportunity to analyzeerrors and come up with solutions to reduce theirrecurrence.

Across these three non-learning cases, motivationplays different roles: ego protection, career pro-tection, protecting the organization and focus onother parts of the job than error, or problem re-duction. In what way besides motivation do thesenon-learning cases contrast with learning cases? Arethere more competing motivations in non-learningcases?

Is it an illustrationof howmotivation is anecessaryfactor for learning to occur and without it neitheropportunity nor ability matter? Also, how commonarenon-learningoutcomes andwhendo theymatter?In the telecom case, the firm still didwell and clearlywas not very concerned with the failed strategicprojects. When do organizations ignore failures?

The Nature of the Failure Event

We suggest three areas of research to expandhow we study failure events to improve causalityin studies: using counterfactuals by applying theprocess–outcomematrix in Table 2 when selectingevents; extending our failure measures to alsoconsider per-event learning; and standardizing orat least improving the measures of the learningprocess.

Selection of the dependent variable. Few learn-ing studies allow for both successes and failures tobe key events in the research design (Denrell, 2003).


Most studies choose to focus on either successful orfailed outcomes (sometimes controlling for theother), and the set of factors that cause such out-comes. However, to extend our understanding of therelationship between processes and outcomes, theinclusion of both types of events in the research de-sign would allow for stronger causal linkages. Pres-ently, this is done in studies inwhich an interventionis randomly assigned to different organizationalunits and the outcomes are monitored, such as theintroduction of checklists in healthcare into onesubset of hospitals, while the comparison groupworks as before, and the reduction of adverse eventsis monitored (Walker et al., 2012). In checklist stud-ies, an outcome is either a success or a failure, thusthe rate includes both possibilities, although the fo-cus remains on the effect of a single mechanism, theuse of a checklist or not. The initiation of suchstudies is usually third-party organizations, such asthe WHO, in the health-care checklist example(WHO, 2017).

Including both successful and failed outcomeswould also allow for a study of the role that is playedby noise in the learning process (the off-diagonaloutcomes in Table 2) to cover the full range ofprocess–outcome combinations, thus allowing usto better understand disturbances in learningprocesses.

Extending failure measures. The failure learningliterature covers a diverse set of events, from bank-ruptcies to patient deaths to large-scale accidentsthat involve hundreds of victims who have been in-jured or killed. Some events occur once per hour andsome occur once per week, whereas others occuronce per decade. Although the magnitude of eventsmatter for learning, higher-impact events motivateorganizations to respond more forcefully. An im-portant question to ask is whether the sensitivity tofailure events varies across industries, and if so,whatdetermines the level of sensitivity. In settings withmany adverse events that are caused less by errorsand more by the nature of operations (very elderlypatients dying in a healthcare, for instance), we ex-pect a greater insensitivity to the adverse events thatare caused by errors. Three dimensions that shouldmatterwhen considering events are (1) the frequencywith which failures occur; (2) the frequency withwhich adverse events that are NOT caused by errorsoccur; (3) the magnitude of the failure, including thefailure magnitude when compared with industryaverages (killed, injured, failure costs). Few studiesshow us how much a single failure event affectslearning (see Dahlin & Baum, 2003 for an exception),

which might provide a clue to how frequency andmagnitude matter. A per-event measure would alsomake comparisons across setting more applicable.Here, a meta-analysis could reveal the different ef-fects of failure events.

We expect the industries that experience a combi-nation of high failure volume and high spuriousfailure volume to have an elevated error acceptance.Elevated error acceptance reduces the motivation tolearn, which would explain a lower learning rate insuch settings.

Are learning rates overestimated? An articlecritiquing the methods used in econometric studiesof failure learning argues that statistical estima-tions used in failure learning studies are prone toyield falsely positive results (Bennett & Snyder,2017). The authors point out that the classicallearning model where current performance (costper unit) is a logarithmic function of accumulatedexperience (number of units produced) has someeconometric issues when translated to failurelearning and risk overstating the case for failurelearning. Similarly, they point out the risk for falsepositive coefficient results increase when includingsuccessful as well as unsuccessful learning oppor-tunities in the same equation. They recommend us-ing moving time windows of past failures, andseparating the success and failure opportunitiesinto different estimations. Most studies already usemoving windows and also discount events furtherback in time (albeit they do this for theoretical anddata rather than estimation reasons) (Baum&Dahlin,2007; Kim & Miner, 2007).

Even if Bennett and Snyder overstate the risk offalsely positive results, their article raises a morefundamental question about econometric failurelearning models. Usually a learning curve is as-sumed (often without this functional form beingestablished as the one best-fitting the data) whichmeans that a key assumption is that success andfailure learning follow the same learning pattern.Similarly,when including both successful and failedprior events, these are seen as additive which is anuntested assumption. For instance, in traditionalsuccess learning, failed outcomes can be seen as re-ducing the learning rate, but it is usually relegated tothe error term. To model the interaction betweensuccess and failure, we need to develop newmodelsaccounting for both failure and learning as potentialcomplements rather than substitutes. This way, wecould, for instance, see spill-overs from a number ofsuccessful repetitions in learning-by-doing (such asdriving a car)—when skills increase, errors and


mistakes decrease as a function of more successfulevents. Rather than substitutes, there is comple-mentarity with success learning leading to fewerfailures and also enhancing failure learning. Con-versely, we can observe that failure has a complexeffect on normal operations, slowing down successlearning as overall output might be reduced aftera serious accident. The recommendation fromBennett and Snyder (2017) is to, for econometricreasons, not include both successful and failedevents in the same estimations, we would argue thatthey should also be estimated separately for theo-retical reasons—we simply don’t know how theyrelate.

Measuring failure and its responses. The em-pirical studies on failure learning face two mea-surement issues. First, individuals andorganizationsoften fail to identify or report all failure events. Thismakes examining failures’ effects on learning pro-cesses more of a challenge. Common approaches tocollecting failure and error data include using publicarchival data (Baum & Dahlin, 2007; Desai, 2015;Lawton et al., 2012), experimental designs (Dillon &Tinsley, 2008; Ellis et al., 2006), or employee recall(Ginsburg et al., 2010). Archival data and employeerecall increase under reporting biases, and thequestion is how to account for thiswhen interpretingstudies. Audits are, for instance, undertaken ina number of industries, with the Federal RailroadAdministration performing spot-checks on regula-tion compliance (FRA, 2016). Legal caseswhere non-reporting lead to penalties usually state the expectedunderreporting. Union representatives also haveaddressed underreporting.

Second, our review revealed that there is a lack ofcommon measures with regard to failure learningbehaviors, such as the schemas that are used whenresponding to failures. Without common measures,it is difficult to compare the results from studieswith different research settings. With few excep-tions (Ginsburg et al., 2010; Shepherd et al., 2011),most studies measure broad learning behaviors,such as whether employees can challenge workprocesses or if they have improved work pro-cedures, rather than learning behaviors that arespecifically related to failures, such as the identi-fication of an adverse event, cause analysis, andcorrective action. Most importantly, to better un-derstand errors and failure learning requires a re-search design with an explicit link between failureevents and the different elements of learning behavior,such as Israeli flight crews’ post-event analyses (Elliset al., 2006).

CONCLUSION

Reviewing the literature on error and failurelearning, we clustered learning mechanisms intothree categories of opportunity to learn (the factors ofinformation and time), motivation to learn (willing-ness to act), and ability to learn (competence to act).Failure and error learning have been studied acrossacademic areas and address multiple levels of anal-ysis, from individual, group, and organizationalperspectives. Although motivation and ability fac-tors dominate studies at the individual and grouplevels of analysis, opportunity factors dominatestudies at the organizational level. Studies in healthmanagement are also concerned with procedure-level studies, with a wealth of data showing timetrends for procedures on the national level, wherelearning diverges greatly across treatment types fromnegative, over no improvement, to some positivelearning (Downey et al., 2012).

In summary, the findings suggest that more in-formation about errors and failures in the form ofone’s own and others’ prior failures or near-failuresfacilitate learning. They go on to report that leadersand organizations with a punitive attitude towarderrors and failures obtain less information becauseindividuals in such organizations consider the costof reporting to be too high. In addition, they state thatthe ability to process and learn from errors and fail-ures is partly based on attribution and inherent atti-tudes; however, this ability can be boosted throughactive post-event reviews. We argue that the cross-industry differences in learning rates depend ona number of factors. Among those factors, a noisylearning environment in which organizations expe-rience spurious successes and spurious failures ex-erts a strong influence on failure learning. Noisyinformation about cause–effect makes failure in-formation more difficult to interpret, which lowerslearning opportunities and ability. Spurious suc-cesses and failures also create ambiguity, whichlowers themotivation to learnbecause the awarenessof weak error-failure connections risks leading tonormalized deviance, and it can also lower in-dividuals’ motivations to report errors and failures.

An organization that seeks to enhance error andfailure learning should analyze the causes of itsmostcommon errors and failures. To maximize the op-portunity to learn, the organization should not onlystudy its own but also the events of similar organi-zations. Furthermore, including near-misses wouldadd information and suggestions forways to avoid anadverse outcome after a process error has occurred.


When attributing causes, management must ensurethat operators are not unduly blamed and if the causeis found to be operator error, look for systematic sucherrors to find if there are structural or organizationalfactors leading to operator error.

We propose a number of unexplored researchareas in error and failure learning, some of whichare related to a lack of linkages across academicdisciplines. In short, management scholars tendto ignore the role that is played by regulators infailure reduction; policy scholars tend to ignoreorganizational differences and the role that isplayed by management in failure reduction; safetyscholars have devised methods for the transfer oflearning and for the development of ability; how-ever, they have not yet fully established the effi-cacy of these tools. To that end, scholars acrossacademic disciplines have “failed” to learn fromeach other’s failure research. Through our review,we have found fruitful opportunities for future re-search to learn from the extant failure studies toenhance our understanding of failure learning inorganizations.

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