goal hierarchy: improving asset data quality by improving motivation

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Goal hierarchy: Improving asset data quality by improving motivation Kerrie Unsworth a,n , Elisa Adriasola a , Amber Johnston-Billings a , Alina Dmitrieva a , Melinda Hodkiewicz b a UWA Business School, University of Western Australia, Crawley, WA 6009, Australia b School of Mechanical Engineering, University of Western Australia, Crawley, WA 6009, Australia article info Article history: Received 7 September 2010 Received in revised form 24 May 2011 Accepted 3 June 2011 Available online 8 July 2011 Keywords: Data quality Manual data collection Motivation Asset management abstract Many have recognized the need for high quality data on assets and the problems in obtaining them, particularly when there is a need for human observation and manual recording. Yet very few have looked at the role of the data collectors themselves in the data quality process. This paper argues that there are benefits to more fully understanding the psychological factors that lay behind data collection and we use goal hierarchy theory to understand these factors. Given the myriad of potential reasons for poor-quality data it has previously proven difficult to identify and successfully deploy employee-driven interventions; however, the goal hierarchy approach looks at all of the goals that an individual has in their life and the connections between them. For instance, does collecting data relate to whether or not they get a promotion? Stay safe? Get a new job? and so on. By eliciting these goals and their connections we can identify commonalities across different groups, sites or organizations that can influence the quality of data collection. Thus, rather than assuming what the data collectors want, a goal hierarchy approach determines that empirically. Practically, this supports the development of customized interventions that will be much more effective and sustainable than previous efforts. & 2011 Elsevier Ltd. All rights reserved. 1. Introduction Evidence suggests that even though most organizations have a lot of data on their assets, they do not have enough accurate, reliable and timely data to drive reliability and safety decisions [1,2]. As Levitin and Redman [3] put it so succinctly, ‘‘Most enterprises have far more data than they can possibly use; yet, at the same time, they do not have the data they really need’’. Furthermore, the trustworthiness of the data within an organization is also often questioned: Lin et al. [2] suggest that only 26% of the data collectors themselves have confidence in the data that are in the system. If not identified and corrected, these poor-quality data can lead to decisions being made more on the basis of subjective judgments, rather than data-driven judgments [4] with grave repercussions for the reliability of infrastructure and operating assets. As we will discuss, previous research in this area has predo- minantly looked at this problem from an input control perspec- tive; that is, restricting the data collection processes through either structured input devices or increased use of sensors to eliminate the need for manually acquired data (e.g., [5,6]). How- ever in many cases, sensors are not appropriate and restricting or controlling the input does not help to improve the quality of manually acquired data. Instead, we need to look at the issue from a different angle – rather than examining ‘manually acquired data’ we can examine ‘data collected by a person’. Once this shift in perspective is made we then start to realize that the psychological issues are imperative and motivation may be much more potent than control. While a few studies have looked at the motivation of data collectors, most of these have focused on only a few aspects of motivation (e.g., [2,7,8]) while only one has taken a theoretical approach [9]. We move the literature one step further again by providing an alternative theoretical approach, which can both incorporate all the previous findings and can lead to the derivation of the most appropriate solutions for each individual organization or site. 1.1. What is data quality and what do we know about it? Data quality involves ‘‘getting the right and correct data in the right place at the right time to complete the task at hand’’ [10]. Many have tried to delineate exactly what we mean by ‘‘quality’’; to date, the most widely used attributes come from Wang and Strong [10] and Lee and Strong [7]. These attributes include contextual data quality, representational data quality, accessibil- ity data quality and intrinsic data quality as second-order factors for more specific attributes such as accuracy, timeliness, precision, reliability, currency, completeness and relevance [11] as well as accessibility and interpretability [12,13]. Moreover, we Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/ress Reliability Engineering and System Safety 0951-8320/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ress.2011.06.003 n Corresponding author. Tel.: þ61 8 6488 7224; fax: þ61 8 6488 1086. E-mail address: [email protected] (K. Unsworth). Reliability Engineering and System Safety 96 (2011) 1474–1481

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Page 1: Goal hierarchy: Improving asset data quality by improving motivation

Reliability Engineering and System Safety 96 (2011) 1474–1481

Contents lists available at ScienceDirect

Reliability Engineering and System Safety

0951-83

doi:10.1

n Corr

E-m

journal homepage: www.elsevier.com/locate/ress

Goal hierarchy: Improving asset data quality by improving motivation

Kerrie Unsworth a,n, Elisa Adriasola a, Amber Johnston-Billings a,Alina Dmitrieva a, Melinda Hodkiewicz b

a UWA Business School, University of Western Australia, Crawley, WA 6009, Australiab School of Mechanical Engineering, University of Western Australia, Crawley, WA 6009, Australia

a r t i c l e i n f o

Article history:

Received 7 September 2010

Received in revised form

24 May 2011

Accepted 3 June 2011Available online 8 July 2011

Keywords:

Data quality

Manual data collection

Motivation

Asset management

20/$ - see front matter & 2011 Elsevier Ltd. A

016/j.ress.2011.06.003

esponding author. Tel.: þ61 8 6488 7224; fax

ail address: [email protected] (K.

a b s t r a c t

Many have recognized the need for high quality data on assets and the problems in obtaining them,

particularly when there is a need for human observation and manual recording. Yet very few have

looked at the role of the data collectors themselves in the data quality process. This paper argues that

there are benefits to more fully understanding the psychological factors that lay behind data collection

and we use goal hierarchy theory to understand these factors. Given the myriad of potential reasons for

poor-quality data it has previously proven difficult to identify and successfully deploy employee-driven

interventions; however, the goal hierarchy approach looks at all of the goals that an individual has in

their life and the connections between them. For instance, does collecting data relate to whether or not

they get a promotion? Stay safe? Get a new job? and so on. By eliciting these goals and their

connections we can identify commonalities across different groups, sites or organizations that can

influence the quality of data collection. Thus, rather than assuming what the data collectors want, a

goal hierarchy approach determines that empirically. Practically, this supports the development of

customized interventions that will be much more effective and sustainable than previous efforts.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Evidence suggests that even though most organizations have alot of data on their assets, they do not have enough accurate, reliableand timely data to drive reliability and safety decisions [1,2].As Levitin and Redman [3] put it so succinctly, ‘‘Most enterpriseshave far more data than they can possibly use; yet, at the same time,they do not have the data they really need’’. Furthermore, thetrustworthiness of the data within an organization is also oftenquestioned: Lin et al. [2] suggest that only 26% of the data collectorsthemselves have confidence in the data that are in the system. If notidentified and corrected, these poor-quality data can lead todecisions being made more on the basis of subjective judgments,rather than data-driven judgments [4] with grave repercussions forthe reliability of infrastructure and operating assets.

As we will discuss, previous research in this area has predo-minantly looked at this problem from an input control perspec-tive; that is, restricting the data collection processes througheither structured input devices or increased use of sensors toeliminate the need for manually acquired data (e.g., [5,6]). How-ever in many cases, sensors are not appropriate and restricting orcontrolling the input does not help to improve the quality of

ll rights reserved.

: þ61 8 6488 1086.

Unsworth).

manually acquired data. Instead, we need to look at the issue froma different angle – rather than examining ‘manually acquired data’we can examine ‘data collected by a person’. Once this shift inperspective is made we then start to realize that the psychologicalissues are imperative and motivation may be much more potentthan control. While a few studies have looked at the motivation ofdata collectors, most of these have focused on only a few aspectsof motivation (e.g., [2,7,8]) while only one has taken a theoreticalapproach [9]. We move the literature one step further again byproviding an alternative theoretical approach, which can bothincorporate all the previous findings and can lead to the derivationof the most appropriate solutions for each individual organizationor site.

1.1. What is data quality and what do we know about it?

Data quality involves ‘‘getting the right and correct data in theright place at the right time to complete the task at hand’’ [10].Many have tried to delineate exactly what we mean by ‘‘quality’’;to date, the most widely used attributes come from Wang andStrong [10] and Lee and Strong [7]. These attributes includecontextual data quality, representational data quality, accessibil-ity data quality and intrinsic data quality as second-orderfactors for more specific attributes such as accuracy, timeliness,precision, reliability, currency, completeness and relevance [11]as well as accessibility and interpretability [12,13]. Moreover, we

Page 2: Goal hierarchy: Improving asset data quality by improving motivation

K. Unsworth et al. / Reliability Engineering and System Safety 96 (2011) 1474–1481 1475

believe that these attributes are more likely to be related toparticular stages in the data process affecting particular groups ofpeople (see Table 1). For instance, contextual data quality isprobably more likely to be necessary when executives aredetermining which data are needed, while intrinsic, representa-tional and accessibility data quality are probably needed whenthe technocrats are setting up the data capture system. In thismanuscript, we are most interested in the data collection phaseand thus are most interested in intrinsic data quality – that is, thedata’s believability, accuracy, objectivity and reputation [10].

Maintaining the intrinsic quality of asset data is often recog-nized as crucial to effective asset management [14–16], but it isalso seen as challenging and problematic, particularly whencollecting data involves dealing with people. As can be seen inTable 1, while data have traditionally been collected by people(usually maintenance or production technicians [17]) there hasbeen a move in many sectors to try and replace the ‘‘human’’diagnostic element with sensors. We believe, however, that thereare some elements of the failure diagnostic process that aredifficult to substitute. For example, when a maintainer arrivesat a failed unit (e.g., a motor) he/she can make a number ofobservations about the operating environment: is the motorcaked in dirt so that cooling is impacted? Are the seals leaking?Are bolts loose?, etc. Capturing these observations (i.e., manuallyacquired data) is important to understand the potential causes offailure. Sensors can only measure discrete data associated withpre-identified symptoms of the failure such as temperature,vibration and so forth, but an experienced maintainer/operatorcan record their observations and postulate a failure cause. Their

Table 1Integration of data quality literature.

Task Most relevantdimension of DQa

Who?b How addressed?

Strategic

determina-

tion of data

to be

collected

Contextual DQ Executive Indirectly addressed as data

define data quality, fitness

what data is needed. Usefu

content of data

Setting up data

capture

system

Intrinsic DQ, then

representational DQ,

then accessibility DQ

Technocrat Rules; input control (restric

processes; sensors)

Data collection Intrinsic DQ Operator/

collector

Input control (restricted co

processes; sensors)

Motivation of data collector

Data storage Representational DQ,

then intrinsic DQ, then

contextual DQ

Technocrat/

custodian

Data cleansing

Data use/

decisions

made

Contextual DQ, then

representational DQ, then

intrinsic DQ, then

accessibility DQ

Executive/

consumer

a Assumptions based on Wang and Strong, 1996 [10]; Lee and Strong, 2004 [7].b Murphy, 2010 [38]; Lee and Strong, 2004 [7].

ability to convey this to others (supervisors, planners, engineers)assists in ensuring improvement efforts are targeted at the causeof failure.

Capturing this knowledge is a key part of the data collectionprocess but research points to many examples of the factors thatcan potentially impede the quality of this manually acquired data.These include inadequate training and procedural guidelines fordata collectors and inadequate management structures to pro-mote accurate, complete and timely reporting of data [8], lowpriority assigned to data quality, failure to determine the appro-priate level of data quality [18], problems with personnel’sinterpretation of complex equipment and difficulties in securingpersonnel with adequate competence for the role [19], andinsufficient operator feedback and operator time pressures [9].

For instance, Lin et al. [2] used a four-phase empirical studythat included literature review and analysis, pilot case study,national data quality survey and case studies to identify factorsfor ensuring high quality of manually acquired data. The findingspoint to a number of factors that hindered data quality, such asmotivation, lack of communication and management feedbackand inadequate education and training about the importance ofdata quality. Additionally, Lin et al. [2] found that despite havingdata management strategies in place, many engineering organi-zations failed to implement the required improvement solutions.Tee et al. [20] also identified a number of factors that influencedata quality. The researchers conducted a data quality survey ofsenior managers and general users as well as interviews withsenior managers. The results of their study revealed that manage-ment’s commitment to data quality, effective communication

Current literature Critique of current literature

consumer:

for use and

lness and

Bendall, 1988 [47]; Wang and

Strong 1996 [10]; Madnick

et al., 2004 [5]; Kukla et al.,

1992 [48]

Does not address

organizational/strategic

approach to motivate manually

acquired data quality

ted collection Sandtorv et al., 1996 [19]; Lee,

2004 [16]

Does not address need for

manually acquired data

collection and/or psychological

effects of control

llection Bendall, 1988 [47]; Sandtorv

et al., 1996 [19];

Lee, 2004 [16]

Does not address need for

manually acquired data

collection and/or psychological

effects of control

s/operators Knowledge of data collectors/

operators (Lee and Strong,

2004 [7])

Does not address all aspects of

motivation

Atheoretical lists of enablers/

barriers

(Lin et al., 2007 [2];

Tee et al., 2007 [20])

Unlikely to address all aspects

of motivation

Theoretically derived enablers/

barriers (Murphy, 2009 [9];

This Paper)

Addresses all aspects of

motivation but needs to be able

to clearly derive the most

appropriate solutions

Bendall, 1988 [47]; Cappiello

et al., 2004 [6]; Madnick et al.,

2004 [5]

Occurs too late in the process

Page 3: Goal hierarchy: Improving asset data quality by improving motivation

Data Quality Awareness &

PrioritiesFeedback Communication

GroupSupport

Management

Commitment

Knowledge

& Skills Training

Proceduralguidelines

Staffing & time

constraints

Orgstructures

Attitudestowards Data

Quality

SocialSupport for Data Quality

Control over Achieving

Data Quality

Motivationfor Data Quality

Motivationfor Core Job

Tasks

Motivationfor Non-

Work Tasks

???

??? ???

Fig. 1. Integration of factors affecting manually acquired data quality.

K. Unsworth et al. / Reliability Engineering and System Safety 96 (2011) 1474–14811476

among stakeholders and data quality awareness are importantorganizational elements, which, if not in place, can potentiallyreduce the data quality.

As can be seen (and as outlined in both Table 1 and Fig. 1), ourunderstanding of the factors affecting the data-collecting employ-ees themselves is quite limited. To our knowledge, the only otherpaper that addresses this psychological approach is Murphy [9].He used a theory from social psychology, the Theory of PlannedBehavior [21], to identify a range of recommendations forimproving manually acquired data quality. These recommenda-tions included improving attitudes to data quality (e.g., throughestablishing target goals around data quality compliance, ensur-ing a consistent and strong message about the value it places onquality data acquisition and improving feedback mechanisms toeducate operators of the consequences and outcomes of theirbehavior). He also recommended a number of ways to improvegroup support for data quality, which included understanding thelevel of group identification within the operator cohort anddetermining that group’s attitude towards manual data collec-tion; and holding regular open forums to discuss collection issues.Finally, Murphy identified structural solutions for improving dataquality, which included process re-engineering and job redesignto emphasize the critical nature of the task; job redesign toincrease operator autonomy and their ability to adjust behaviorbased on feedback; and using alternate types of technologies (e.g.,personal digital assistants; PDAs) to promote or assist with datacollection.

1 Many thanks to the Reviewer who made this observation.

2. Critique of the current literature

The steps that Lin et al. [2], Murphy [9], Tee et al. [20] andothers have taken us in our understanding of the psychologicalfactors affecting data collectors are highly valuable. However, asour understanding grows we need to be more rigorous in ourapproach. First, it is difficult to determine the degree to which thelist of factors that has emerged from this literature is based onmanagerial perceptions of what is affecting the employees ratherthan a comprehensive list of all factors affecting the datacollector. Tee et al.’s [20] research was focused specifically on

managers and data consumers, and while Lin et al. [2] surveyeddata collectors there is no information in their study to determineexactly how many data collectors were surveyed, their industry ortheir level within the organization. Furthermore, by taking anatheoretical approach, these empirical studies are open to ques-tion about the comprehensiveness of their list of factors.

On the other hand, while Murphy [9] addressed this byidentifying a robust psychological theory, which could incorporatea number of factors affecting the attitudes, social environment orperceived control of the data collector, meta-analyses of TPB havefound that only 30% of the variance in actual behavior can beaccounted for [22]. In other words, we are not able to predict theirbehavior 70% of the time. As identified in Fig. 1, it is likely that thisis because TPB looks at only one intention or goal at a time ratherthan looking at all the goals that an individual at work might have(i.e., while a maintainer may have some motivation to collect data,this may not be as large as their motivation to fix the equipment –when these two tasks are in conflict then the maintainer is likelyto focus on fixing equipment and not the data collection).Furthermore, there may well be different goals even within theone goal of ‘‘data collection’’: For instance, how does collectingdata on assets that the maintainer is responsible for differ tocollecting data on assets for which they are not responsible?1

Finally, it is impossible to determine from Murphy’s long list ofrecommendations, which ones to use in any given situation; andwould each of them be equally effective for each site or group ofpeople? For instance, we conducted initial interviews and focusgroups with senior managers and supervisors of maintainers inone organization. The most common reason that emerged fortheir poor data quality was that they perceived that the datacollectors did not believe that collecting data was ‘‘important’’.At face value, this seemed easy to solve and related to attitudes [9].However, once we started to think about potential interventions itbecame much more complicated. This is because we need to knowwhat is meant by ‘‘important’’. Is it that the data collector does notthink that the organization values the data, or is it that the datacollector does not value it himself/herself? Furthermore, if the data

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K. Unsworth et al. / Reliability Engineering and System Safety 96 (2011) 1474–1481 1477

collector does not value it himself/herself, why not? Is it because it isnot important for keeping his or her job (and not being fired), orbecause it is not important in helping the crew? Could it be becauseit is not important in providing self-respect or independence? Orany of a range of other different reasons?

We then decided that we needed to find the data collector’sperception of why they sometimes did not collect high qualitydata. In focus groups with them, the data collectors themselvesproffered a range of reasons why they did not and do not intendto collect data. Many stated that ‘‘they did not consider it part oftheir job’’, ‘‘that it was just additional paperwork’’, ‘‘that theywere not very good at that kind of stuff’’, or that they feared thatdata collection ‘‘could be used against them’’ (if they are reportingfaults then the fault is then associated with them).

We therefore realised that we needed an employee-drivenapproach that would: (1) be theoretically based so as to be moregeneralizable than an inductive list; (2) be able to examine data-collecting goals in the context of all the other goals the employeemight have at work; (3) be able to incorporate a wide variety offactors associated with manually acquired data; but (4) allow us todetermine, which of those factors was most appropriate for theparticular individual or situation (see Table 1). Given these issues, weset out to identify a complementary way of understanding theprocess of manually acquiring high-quality data – a theory thatwould both integrate a wide range of potential factors and that wouldoffer an organization a method of identifying the most effectiveinterventions for improving the quality of the data collected.

2.1. Goal hierarchy theory

The premise that goals are at the heart of motivation is nowwell-accepted: people are motivated to act in order to achievetheir conscious or subconscious goals [23]. Similarly well-accepted is the premise that goals exist in a hierarchy withhigher-order, more abstract, long-term goals sitting at the topof the hierarchy and more concrete day-to-day task goals sittingat the lower end of the hierarchy [24,25]. Cropanzano et al. [26]illustrate a hierarchy with values at the top, followed by

Value 1 Value 2

Identity 1 Identity 2 Identity

PersonalProject 1

PersonalProject 2

PersonProjec

Task goal 1 Task goal 2 Task goal 3

Fig. 2. Conceptual

self-identities, followed by personal projects (i.e., long-termgoals), followed by task or day-to-day goals (see Fig. 2). Generally,lower-order goals are chosen in order to meet the higher-ordergoals; however, little more is known in the literature about goalhierarchies in and of themselves. Therefore, we now draw upon awide variety of literatures to discuss each element of goalhierarchies before discussing the characteristics of hierarchies asa whole; it is important to note that it is the overall pattern ofconnections and goals, which is important to predicting wheneach individual will collect high quality data rather than anysingle element.

For instance, let’s say that Joe is a maintenance staff memberworking for a large mining organization, Mines Inc (see Fig. 3).When at work Joe has values of family security, self-respect,happiness and true friendship. He sees himself as a father,husband, employee, team member, musician and friend and theseidentities all help to fulfill his values. At work his project goals areto get promoted, to stay safe, to get ready for the next plannedshutdown and to enjoy himself; while his day-to-day work tasksinclude inspecting and repairing equipment, acquiring and enter-ing data on assets (performance, condition and/or failure obser-vations) into Mining Inc’s reliability database, and attending teammeetings and his other day-to-day goals include chatting withmates and having some personal time.

The figure above is a simplified representation of Joe’s goalhierarchy. As you can see, inspecting equipment has strong linksto getting promoted, staying safe and preparing for the shutdown,which are then linked to his employee and team memberidentities (which, in turn are linked to his values, particularlyvia his team member identity). When he needs to choose, it islikely that he will choose the goal of inspecting and repairingequipment and that he will set a high standard of achievement forthat goal. However, collecting data is only weakly related topreparing for the shutdown and is not related to any other projectgoal. When it comes to a choice of goals and behavior, it is likelythat he will not choose to collect high-quality data because it doesnot help fulfill any of the higher-order concepts or if he does so,he will set only a low standard of achievement.

Value 3 Value 4

3 Identity 4 Identity 5

alt 3

PersonalProject 4

PersonalProject 5

Task goal 4 Task goal 5 Task goal 6

goal hierarchy.

Page 5: Goal hierarchy: Improving asset data quality by improving motivation

Familysecurity Self-respect Happiness

TrueFriendship

Father/Husband Employee

Teammember Musician Friend

GetPromoted Stay safe

Prepare for shutdown

Enjoymyself

Inspect,repair

equipment

Acquire & Enter Data

Teammeeting

Chat with mates

Personaltime

Fig. 3. Example of a simplified goal hierarchy.

K. Unsworth et al. / Reliability Engineering and System Safety 96 (2011) 1474–14811478

Thus, there are different levels of goals from abstract values tospecific task goals, but on top of this, the goals can also becharacterized by a number of dimensions. First, previous theorysuggests that goals at all levels will have different levels ofrelevance and importance [24,26]. For instance, the self-identityof team-worker might be more relevant and important to Joe themaintainer than his self-identity as an employee of Mines Inc – inother words, his role within his crew is very important to him, butit does not matter much to him whether he works for Mines Inc orwhether he works for their competitor, Competitor Corp.

Second, similar to Murphy [9] we suggest that goals will havedifferent levels of feasibility and perceived control (cf. [27]). Wesuggest that these perceptions of feasibility will be related to thedegree to which the individual makes attributions of accountability[26] and the degree to which he or she feels capable of achievingthe goal (cf. [28]). In other words, Joe may feel as though attendingmeetings is completely within his control because he can be heldaccountable for not turning up and because they are held first thingbefore the shift starts; however, he may feel that collecting datadepends upon whether he is accountable for collecting it (i.e., willanybody check?), whether he feels he is actually capable ofcollecting the data (in our interviews we found that members ofmaintenance crews often felt that they were ‘‘not good at writingand filling in forms’’), and whether he has time to collect thatinformation.

Finally, we propose that the goals will have different levels ofenjoyment value – for instance, repairing equipment, a job forwhich Joe has aptitude, training and skill, might be more intrin-sically enjoyable than collecting data [29]. Taken together, wesuggest that a combination of these three dimensions makescertain identities, personal projects or task goals more salientthan others (and therefore more likely to be acted upon). Ifcollecting data can be linked to these salient higher-order goals

then Joe is more likely to set a goal of collecting intrinsically highquality data.

As well as the goals themselves, there are also connectionsbetween these goals. These connections have been neglected inthe research to date; however, there are some propositions we canmake about them. First, one lower-order goal might be linked tomore than one higher goal (multipotentiality) and a number oflower goals might be linked to one higher-order (equifinality) [30].Furthermore, we suggest that the connections between the goalswill have different strengths, contingent upon the dependency ofthe higher-order goal on the lower-order goal or the degree towhich the lower-order goal can help fulfill the higher-order goal [31];e.g., if Joe feels that the judgment of whether or not he is a goodemployee of Mines Inc is not related to whether he enters high-quality data or not, then the connection is likely to be weak.

Therefore, we see a pattern of relationships between the goalconstructs and the connections between them (see Fig. 3). It isthis pattern, and most importantly the relative density of con-nections linking to the ‘‘collecting data’’ task in comparison to theother tasks, that results in actual behavior. In other words,following Hanges et al. [32], we suggest that goal hierarchiescomprise a connectionist framework in which the overall sub-symbolic pattern is what is most important rather than anyparticular piece of the puzzle.

The pattern is similar to a neural network in that there is aspreading activation of all relevant connections and goals – overtime and repeated activations, each individual pattern becomesrelatively stable [33]. These stable patterns become scripts, whichwe follow without thinking (see [34]). However, given ourattentional abilities, we, as people, can only focus on a smallportion of our overall self-schema at any one point in time [35],therefore, the hierarchy pattern will change depending uponwhich goal, and which level, is salient at the time [26,30,36,37].

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K. Unsworth et al. / Reliability Engineering and System Safety 96 (2011) 1474–1481 1479

Thus, while the goal content may stay roughly the same in theoverall goal hierarchy, different patterns will emerge with differ-ent weights attached to the different goals and different strengthsof connections, according to whatever happens to be salient. Inother words, if Joe is reminded about his desire to get a promotionthen that project goal and all connections, including potentialconnections with collecting data, will become salient; but if he isthinking about playing music that identity – and all connections –will become salient (and data collection will not be relevant).

It is important to note that the hierarchy is holistic in that ittakes into account contextual (work environment and homeenvironment) and environmental influences (e.g., leadership,culture) such as those recently proposed by Murphy [38]. First,the connections between nodes are changeable and can beinfluenced to become automatically activated as is the case withhabit-directed behavior [39]. For instance, if the supervisors atMines Inc consistently emphasize high quality data collection asimportant to the company then data collection tasks will becomestrongly and habitually connected to the maintainer’s ‘‘employee’’identity. Second, goal choice may also be affected by the specificcontext [40]. Cues from the environment [39] can cause noncon-scious goal activation [41], which can directly affect goal choice,as in the case of the near miss causing safety-task goal choice.Finally, external constraints might limit the task being carried out[42], which will also affect the weighting of each node and thestrength of each connection in the goal hierarchy. In other words,if a person repeatedly found that they could not engage in thechosen goal (e.g., collecting data) due to external constraints (e.g.,not enough time), then the connection between the task goal andthe higher-order goals would weaken.

2.2. Evidence for goal hierarchy theory

Although it is intuitively plausible and supported by neuro-cognitive research, empirical research testing goal hierarchytheory in the workplace is relatively thin on the ground. However,the research that has been done indicates some validity for theidea that workplace values, self-identities and behavior are linked[43–46]. Some work has looked at the top-half, namely the

Teammember

Leader/supervisor

Securitypurchases

PermanentJob

Haveshutdown

ready

Book fligh

Fig. 4. Actual goal hierarchy ob

relationship between values and identities [43], while others havelooked at the bottom-half, namely the relationship between goalorientation, the content of the personal project goals, and perfor-mance amongst MBA students [44]. Interestingly, Brett andVandeWalle [44] found that although all students were assignedthe same goal they interpreted the goal differently such that theyactually had different goal content – supporting the need to dofurther work investigating goal interpretation and choice (cf. [19]).

Other studies have attempted to look at a more completeoperationalisation of the hierarchy. Sheldon and Kasser [31]found that vertical coherence (the degree to which personalprojects were connected to future identities) was related toengagement in meaningful activities for undergraduate students.Bateman et al. [45] elicited managerial goals and found that a goalhierarchy could be imposed. Finally, Sosik et al. [46] examinedand found relationships between the managers’ values, theirrelational self-concepts (e.g., the degree to which they seethemselves as related to others) and their altruistic behavior.

In the realm of manually acquired data quality, our initialresearch suggests that goal hierarchies can be elicited from datacollectors. Fig. 4 is one example of a goal hierarchy that weobtained from a maintenance worker in a mining organization.(As you can see, it does not include the values level of the goalhierarchy as this was included in a separate survey; nonetheless itadequately captures the individual’s goal hierarchy. Also collectedin the survey were ratings of importance, control and enjoyment.)

The above goal hierarchy comes from a maintenance workerthat we will call Mike. When at work, his most valued identity is ateam member, followed by being competent, being a supervisor,and finally being a friend. Booking his flight was the mostenjoyable and the most controllable activity, followed by gettingthe shutdown ready, followed lastly by data collection. All threework-related identities (and particularly being competent) wererelated to his long-term projects of getting a permanent job andgetting job recognition; avoiding wasting time and buying asecurity purchase (a house) were also related to these identities.Finally, collecting data was related to getting a permanent job, jobrecognition and avoiding wasting time. Looking at the overallpattern, it suggests that the task of getting the shut ready will

Competent Friend

Jobrecognition

Avoidwasting

time

my t

Collectinggood

quality data

tained from a maintainer.

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take the highest priority as it is more densely connected than datacollection; however, given the relatively high density for datacollection, it is likely that Mike already collects data adequately. IfMike is reminded that it is a key part of his job role (i.e., linking itto the security goal) or enabling him to control the data collectionto a greater extent (i.e., increasing its controllability) then it islikely that he will engage in this behavior to an even greaterextent. We believe that our initial foray suggests that goalhierarchy may work for an understanding of motivating datacollectors to collect intrinsically high quality data and that thiswill be a fruitful arena for future research.

3. Theoretical and practical implications

How does this help us in moving forward with improving dataquality for better asset reliability? First, we can use goal hierarchytheory to incorporate previous research findings. Overall attitudesand subjective norms for collecting data (cf. [9]) can be seen asthe density of the specific pattern connected only to the datacollection task; and data quality awareness, feedback, manage-ment commitment and communication (cf. [2,19,20]) can be seenas some of the ways in which this density can be increased.Control over collecting data (cf. [9]) can be seen in the linksbetween collecting data and higher-order goals and in the ratingsprovided for that task; again, knowledge and training (cf. [2,7])are some of the ways in which these links or ratings could bestrengthened.

Most importantly, though, rather than taking a one-size-fits-allapproach, using goal hierarchy allows us to determine the mostappropriate intervention for a particular group of people. As youcan see from the above examples, different hierarchies result indifferent potential interventions, yet previous research has advo-cated solutions regardless of the individual (e.g., increase trainingor increase supervision). Because each person’s goal hierarchy isdifferent, it might be that increasing supervision of data collectionmight improve the standard of the goal set for one person, but itmight not improve the standard for another. If we are able todetermine each person’s goal hierarchy within a group, we canthen identify the similarities and choose the most appropriateintervention for that group. Instead of being faced with numerousreasons for why people are not collecting data, we can pick one ortwo salient reasons for that group and design an intervention onthat basis. It will be necessary for future research to identify theboundaries of goal hierarchy theory and the specific dimensionsfor the goals and the connections that are most important indifferent contexts.

So how do you identify employees’ goal hierarchies? To begin,the content of the goals across the target population is needed:the population might be data collection staff (whether they bemaintenance, operations or planning staff) within one site oracross the whole organization. This can be done by interviewing awide sample of the population to determine the most commonidentities, personal projects and task goals for that group –approximately 10% of the population could be interviewed in thisway. Once the goal content has been determined standardizedquestionnaires can be created. This questionnaire can measurethe different dimensions of the goals (e.g., enjoyment, controll-ability) at the different levels as well as the standard set of thedata task goals and in particular the data collection goal (e.g., towhat standard do you want to achieve the following task goals –from ‘‘Very poorly’’ to ‘‘Very well’’). Finally, participants can mapout the connections between the goals using different styles ofline to indicate different strengths of connections. If the ques-tionnaire is conducted online, mapping software can be used, or ifthe questionnaire if conducted using traditional paper-and-pencil

techniques then a standard hierarchy can be printed out andparticipants can be asked to draw in the connections.

Using this information it is possible to identify similaritiesacross groups and therefore to identify the most appropriate andeffective intervention for that group. For instance, it could be thatin Site X of Mines Inc there is a strong crew member identitypattern with high autonomy values while in Site Y there is a strongMines Inc employee identity pattern with more conservativecompliance values. A customized intervention for Site X mightbe to systematically link data collection to crews in an autono-mous fashion (e.g., in pre-start meetings the crew discusses thework orders and decides themselves, which data will be particu-larly important for their ongoing work) while an intervention forSite Y might be to publically recognize those individuals who haveconsistently recorded high-quality data over a period of time.

Of course, given that each individual’s goal hierarchy isdifferent, it could be that there are few similarities within anygroup. While this is highly unlikely given the effects of culture innormalizing goal hierarchies [32], in this instance we wouldsuggest that there needs to be two stages in improving motivationfor data collection. First, leadership training for supervisors andcrew leaders should be undertaken to help those managers andsupervisors learn and recognize the individual differences of theirfollowers. An understanding that each person is different is veryeasy to enunciate, but much more difficult to enact in a leadershipcapacity; nevertheless it is imperative that this is done in order tocreate and maintain the initial motivation of the followers. Thesecond stage is to begin to shape the followers’ goal hierarchies tocreate some overlaps. For instance, if most people within thegroup see their crews as being somewhat important, then bottom–up processing can strengthen these identities and their connectionwith data collection tasks (e.g., by implementing crew-basedmonitoring of data collection).

4. Conclusion

Work by Lee and Strong [7] has shown that data collectors areperhaps the most critical component in producing better qualitydata. The goal hierarchy framework provides a compelling depic-tion of the circumstances under which these data collectors do anddo not collect high-quality data. It is a very individualistic, psycho-logical approach, but it is this very nature that allows it to provideclear practical implications for those working within organizationswho wish to improve the quality of data collection. We believe thatit also has clear implications for building research in this area as westart to understand more about the psychological drivers for thosewho collect manually acquired data. We hope that this overviewprovides some new insight and some more ideas for solving thisintractable problem.

Acknowledgments

This project is funded by CRCMining.

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