Using Targeted Analytics to Improve Talent Decisions.

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    Using Targeted nalytics to ImproveTalent DecisionsB y A l e c L e v e n s o n C e n t e r f o r E f fe c t iv e O r g a n i z a t io n sU n i v e r s i t y o f o u t h e r n C a i i f o r n i a

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    past decade saw the em ergence ofanalyticsas a potential force for driving data-on making in H R Lawler, Levenson, andBoudreau, 2004; Levenson, 2005). At

    of the decade, hum an resources analytics wasnot part o f the language ofToday,at the end of the decade, aGooglesearch for the same term produces mor

    When the topic ofHR analytics w as raised at the Center for Effective annuai sponsors meeting in 2003, it w as not part of the formal agend a and ther

    shed courses or seminars o n the topic in the HR consulting and training space. anever expandingarray ofprovidersand conten t work to train and ce rtify practitione rs

    analytics and automate HR analysis.et despite the appare nt progress, therestill is much uncertainty regardinghow best to design, apply and inte-orkings of the

    skills that can be used to achieve

    ed in HR, tend to be concentrated in HR

    ict HR professionals ability to do mean-

    rgy and resources to complete but

    ed to make better on-the-spot decisions,n in situations w here thereislittle time for

    (a) The C apability-O pportunity-M otivationmodel for diagnosing work-related behav-ior and productivity, a model that can beused for job design;(b)Alabor m arkets model of external oppor-tunities and career development, whichcan be used to analyze the cost-benefit ofjob design, staffing and talent manage-ment decisions; and(c) An organization design m odel for diagnos-ing structural barriers to enterprise-widecollaboration and performance.When HR professionals master these modelsand apply them to everyday decision making,two things happen: 1) the path to identifyingwhich analytics to apply becomes clear, and,2) if there is no time for intensive analytics,the modelstheir logic and the empiricalevidence behind themare effective substi-tutes that improve the accuracy and impactof talent and organizational decisions.

    Analytic Competenciesin the HR functionHuman capital analytics are most powerfulwhen they help tell and validate a story thatillustrates the driving forces behind individu-als and groups behaviors and performance.As Boudreau and Ramstad (2006) point out,analytics need to be embedded within a logicframework that is linked to the business; anda change process is needed so they are usedin a way that ensures maximum impact. Thelogic framework ensures that the analyticsare focused on the right issues and are set upto maximize the discovery of data and analy-sis results that are actionable. The process for

    using the results of the analytics ensures thdata is turned into action.The first challenge in applying analytics is inchoosing from the wide array of statisticaand analytic techniques that are availableProviding an exhaustive list of techniquewould be overkill. Instead, Table 1 lists categories of analytic competencies divided btype and level of complexity. The categorieare drawn in part from Rothwell and Sredl(1992) competencies for a Human ResourcDevelopment Researcher and in part from mpersonal experience conducting and traininothers in human capital analytics and statistcal analysis.The to p panel of Table focuses on analyticcompetencies related to statistical techn iqueand the bottom panel contains other analyticompetencies. The second column provideexamples of techniques and concepts, whilthe third column provides a rough approxmation (my own calculations) of thcoursework and on-the-job experience needed to become proficient for each competencas well as the general education level assocated with people w ho are proficient. Note thlatter does not imply an educational requirement. Instead, it can be thought ofasa proxfor the population characteristics that aorganization might target when recruiting foa role that required that competency.Of course, this begs of the question of juhow p revalent these skills are in tod ayworkforce and especially in HR functionWhile definite answers are hard to come bTable 2 provides data on the proficiency otwo different groups of HR professionalpeople who w ork in HR analytics groups anothers in HR outside of HR analytics group

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    T A B LE 1 : HR ANALYTICAL COMPETENCIESAnalytical competencies reiated to

    CategoryBasic dataanalysis

    intermediatedataanalysisBasicmuitivariatemodeisAdvancedmuitivariatemodeis

    Examples Mean Median Minimum & maximum ; range Percentiles Correlation Statistically significant differences Standard deviation ANOVA / ANCOVA Regression Factor analysis Structural equations models Hierarchical linear models Blvariate / muitivariate choice models Cross-level mo dels, includingadjustments for grouped and non-normal errors

    Levai of statisticai expertise requiredand approximate educationai equivaient) Beginning course in basic statistics Minima l on-the-job experienc e applying

    the techniques High schoo l / un dergrad uate leveleducation One to two courses in basic statis tics 3-6 mon ths on-the-job experience High school / undergraduate education Course in advanced statistics 1-2 years on-the-job experience Undergraduate / MBA educ ation Degree or concentration in statisticalmethods Substan tial experience applying the

    techniques on-the-job (multiple years) Graduate degree (Masters or Ph.D.)

    Other analytic competenciesDatapreparation

    Root causeanalysis

    Researchdesign

    Surveydesign

    Quaiitativedatacoilectionand anaiysis

    Identify data for analys is Prepare / clean the data for analysis(transform, identify outliers, etc.) Identify caus al paths Six Sigma analysis

    Treatment vs. control groups Experimental design (exogenous

    variation created by researcher) vs.natura l experiments (exogenousvariation that already exists inthe data) Sample selection Survey item design; validity;reliability Interview techniques Interview coding Content analysis

    One to two courses in basic statistics 3-6 mon ths on-the-job experience High school / undergraduate education One to two courses in basic statistics 6-12 mon ths on-the-job experience High school / undergraduate education Course in advanced statistics 1-2 years on-the-job experienc e applying

    the techniques Undergraduate / MBA education

    Course in advanced statistics 1-2 years on-the-job experience Undergraduate / MBA education Course in research design 1-2 years on-the-job experienc e Undergraduate / MBA education

    The data in Table 2 are from Levenson,Lawler and Boudreau (200 5), and they werecollected in 2005 from a survey of HR ana-lytics professionals and people who workwith them; there were 47 respondents from40 companies. Given the relatively smallsample size, the results in Table 2 are usefulfor identifying potential patterns in the HRprofession. They should not be taken as thefinal wordfor that, we need more compre-hensive data.Despite the limitations of the data, the pat-terns in Table 2 are consistent withconventional views of the HR function, par-

    PEOPLE & STRATEGY

    ticularly the gap between the analytic skillsof HR professionals versus what their orga-nizations ask of them. Eor example, HRprofessionals outside of analytics groups areoften called upon for basic analytics taskssuch as conducting root-cause analysis, cal-cula t ing univar ia te s ta t i s t ics (means ,percentiles, etc.), and communicating theresults of statistical analyses in a clear andunderstandable way. Yet only a minority ofHR professionals has the skills needed toperform those tasks.The good news is that the skills gap for HRanalytics group members for those tasks is

    much smaller: The vast majority of analgroup professionals can do those basic lytics tasks. Yet the gap persists for madvanced, muitivariate analytics tawith less than half of analytics grou p mbers possessing at least an intermedability to execute them . This suggests a damental gap for the HR function oveConducting muitivariate analyses curreis not a core competency, even for manyanalytics groups.

    Conducting muitivariate analysecurrently is not a core competencyeven for many HR analytics groups.

    This presents a challenge for conductinganalytics in some cases but not, as we discuss below, a barrier to insightful anain all cases.Analytics group members' skills in anokey area match up well with the dem andsupon them: identifying the data neededanalysis and o btaining it from othe rs. In trast, when HR professionals outsideanalytics groups are called upon to demstrate these competencies, about two thof the time less than one third of themcapable of doing so . Does this make the ghalf empty or half full? It depends on you look at it. On the one hand, analygroup membersin organizations whsuch groups existtend to have the skneeded to get the requisite data. On the ohand, analytics group members are respsible for acquiring the data only some oftime. Non-analytics group members are often called upon to identify and acquiredata needed for analysis (about tw o-thirdthe time).This presents a key challenge: HR busipartners typically have the best accesidentify and obtain the right data, are ocalled on to do so, and yet they typicallynot proficient at these tasks. Even in orgzations with HR analytics groups, the smsize of such grou ps, relative to the to tal nber of HR professionals in the organizatgreatly restricts their ability to engageorganization in analytical analysis inbroad array of issues that are ripe for exanation. The inability of HR professiooutside of analytics groups to define guide others down analytical paths is a mimpediment to deeper insights.

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    ABLE 2: APPLICATION OF HR ANALYTIC COMPETENCIES . w ^Mam . . . . . . . a_E .= jj i.tnn

    Analytical competency..uper data for analysis

    Data access / influence skills:obtaining data from othersRoot cause analysisBasic univariate statistics (mean,percentile, etc.)Advanced univariate statistics(correlation, differences, etc.)Basic multivariate statistics(ANOVA, regre ssion , etc .)Advanced multivariate statistics(structural equations, etc.)Writing / comm unication (makestatistical results understandable)Presentation or public speaking(effectively present analysis tocross-disciplinary audience)

    Proficiency100.078.984.279.068.442.127.889.5

    89.4

    Frequency100.089.573.779.057.938.911.284.2

    79.0

    Proficiency27.938.530.723.710.28.62. 918.4

    45.9

    Frequency65.164.151.351.311.18. 60.0

    63.2

    65.7

    ciency = percent indicating in termed iate or advanced skills; excluded categories: none and basicquency = percent indicating sometinnes or frequently used In work; excluded categories include never and 'rarely

    Lawler. Boudreau 2005)

    Which human capitalanalytics and analyticsstrategies have thegreatest potential toaffect business results?Table 3 reports on analysis pplic tions fornumber of HR processes. Most functionalldriven processes are subjected to data-baseanalysis with a high degree of frequencyincluding both intermediate and advanceanalysis. Compensation is the clear winnehere with 98 percent of respondents sayinthey use HR analytics in this area. M oreove44 percent need an advanced level of datanalysis to deal with compensation-relateissues. hisis a key application of HR analyics of course but just one of many.Despite this apparent abundance of analysiTable 4 shows that analytics is much lescommonly applied to decision making wherit matters the most:

    TABLE 3 : DATA ANALYSIS APPLICATIONS THAT ORGANIZATIONS ADDRESS WITH HR ANALYTICS ^Percentages ^ R ^ ^ l

    a. Compensationb. Employee attitu de surveysc. Recruitmentd. Diversity/affirmative actione. Benefitsf. Selectiong. HR plannin gh. Succession planning/ leadership supplyi. Workforce planning

    Organization developmentk. S trategic plannin g1. Employee training / educationm. Performance managementn. Promotions0. Organization designp. Competency / talen t assess mentq. Management developmentr. Change manageme nts. Downs izing layoffst. Career planningu. Union/labor relations

    HMbased analysis^ ^ f conducted '97.893.591.386.786.482.682.681.080.480.480,080,079,569.869.668.263.660.954,552.227.9

    ^^t to f sop

    4,711.931.720.58,3

    44.124.345.244.131.320.052.931.430.029.642.950,032.037.545.580.0

    Intermediate51,245,248.846.247,235.356,838,741,246.951.423.551.443.348.132.128,640.033,336.4

    0

    Advanced44.242.919.533.344,420.618.916.114,721.928.623.517.126.722.225.021.428.029.218.220.0

    Results used in reports, dashboards or scorecards

    76,783.782,989.767.671.471.153.162.948.669.466.777.176.744,858.667.934.672.043.545,5

    Lawler Boudreau {2005}VOLUME 34/ISSUE 2 20 11

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    TABLE 4: HOW EXTENSIVELY ANALYTICS IS USED FOR HR DECISION MAKINGPlease indicate tlie extent to whiciiHR analytics is used to:

    a. Measure routine HR process execution(payroll, benefits, commun ication, etc.)b. Assess and improve the HR departme ntoperationsc. Support organizational change effortsd. Measure the cost of providing HR services?e. Make recommendations and decisions thatreflect your company's com petitive situationf. Evaiuate the effectiveness of HR program sand practicesg. Contribute to decisions about businessstrategy and human capitai managementh. Measure the effects of HR program s onthe workforce in terms of competence,

    motivation, a ttitudes, behaviors, etc.?i. Measure the business impa ct of HRprograms and processes?

    Assess and improve the human capitaistrategy of the companyk. Identify where talent has the greatestpotentiai for strategic impact1. Connect human capitai practices toorganizationai performance?m. Conduct cost-benefit anaiyses (also caiiedutiiity anaiyses) of HR programs?n. Assess HR program s before they areimplemented - not just after they are

    operationai0. Evaiuate and track the performance ofoutsourced HR activities?p. Assess the feasibiiity of new businessstrategiesq. Pinpoint HR programs that shouid bediscontinued

    Not At Ali(1 )

    8.710.910.910.913.66.8

    15.2

    17.4

    17.415.920.525.027.3

    25.0

    26.739.537.0

    SomeExtent

    (2 )23.919.621.726.122.736.430.4

    2 6 . 1

    26.131.818.225.025.0

    36.4

    40.027.928.3

    ModerateExtent(3)26.132.623.923.927.327.313.0

    23.9

    21.713.631.818.225.0

    18.2

    13.311.621.7

    ConsiderateExtent

    (4 )21.715.22 6 . 117.422.715.928.3

    13.0

    17.422.718.225.09.1

    13.6

    13.34.78.7

    VeryGreatExtent

    (5 )19.621.717.421.713.613.613.0

    19.6

    17.415.911.46.8

    13.6

    6.8

    6.716.34.3

    Don'tKnow

    2.12.12.12.16.44.32.1

    2.1

    2.16.46.46.46.4

    6.4

    4.36.42.1

    Average

    3.203.173.173.133.002.932.932.912.912.912.822.642.572.412.332.302.15

    Percenindicatin consideror highe41.336.943.53 9 . 136.329.541.3

    32.6

    34.838.629.631.822.7

    20.4

    20.021.013.0

    Source: Levenson,Lawier Boudreau 2005)

    (i) to aid decisions tha t reflect the organiza-tions' competitive situation;(ii) to identify whe re talent has the greatestpotential for strategic impact;(iii) to connect human capital practices toorganizational performance;(iv) to assess and improve the human capita lstrategy of the company; and(v) to assess the feasibility of new businessstrategies.Therefore, the problem for HR is not a lackof analysis but the inability to target thatanalysis so it creates the kind of insights thatmatter most to the organization.

    PEOPLE & STRATEGY

    nexample illustrates these challenges. Turn-over reports are commonly used as a type of temperature gauge for wh at is happeningwith employees. High turnover at face valueis usually interpreted as bad because talent isbeing lost. Yet turnover is a function of bothjob fit and job dema nds. Ifarole is staffed bypeople who are average to below-averageability for the job responsibilities, both vol-untary turnover and productivity will be low.If the job demands are raised, then both turn -over and productivity should increase. And ifthe time to productivity in the role is short(i.e. very little on-the-job training is neededfor a new employee to become fully produc-tive), then high job demands and highturnover may be the right choice, dependingon the pool of people available to be hired.

    Indeed, for certain roles, the only wayattract high productivity people may bhire them with the knowledge that they leave afteraspecified period of time (andhave higher turnover than lower-productipeople who are happy to stay): they monly choose to come work for you if thera clear career path to other jobs they can mon to that build on the skills and experiengained while working in the role. For exple, early-career school graduates are owilling to trade lower compensation andsecurity for the reward of building skills experience they need for higher level ptions.Thus, low turnover can signal productivity is low and high turnover signal that productivity is high. Turnoreports alone cannot provide the full pic

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    deeper understanding

    ced statisticals needed, the number crunching

    stat isti cal exper ts and that is atforward task. The real challenge lies

    the analysis results into meaningfulfor the stakeholders and deci-

    g two examples, bothm a financial services firm. The first case

    me and sales performancer a key role in the organization. The analy-

    was conducted by a dedicated HRby the

    avior in the role. Yet the general

    gift-wrapped resultscomplete rejection of the analysis and a

    it. The key

    nd case was a straightforwardin the midst of a

    responses to the changes and thers success in mitigating the negative

    because the questions were designed todirectly address employee responses to acritical transition period, the survey resultswere given great weight in the leaders pro-cess of assessing and adapting their approachto managing the changes.So advanced statistical techniques are notnecessarily a prerequisite for deep insightsand actionable information. They are, how-ever, often a key part of a comprehensive setof analyses that may be needed for the deep-est insights.Two case studies illustrate this point. Bothstarted as problems that appeared related tocompensation. After conducting extensiveanalyses, meaningful insights to the limits ofcompensation as a solution were achieved, andactions were taken that led to lasting positiveimpacts. Both cases included the following:a) advanced statistical techniques for one

    part of the work;b) simple data analyses for other parts of the

    work; andc) introspective, thoughtful consideration of

    causal factors using logic only and virtu-ally no (new) data analysis for theremaining parts of the work.

    PricewateriiouseCoopersCase StudyThe first case comes from a talent manage-ment and retention challenge faced byPricewaterhouseCoopers (PwC). Extensivedetails of the case are described in Levenson,Fenlon and Benson (2010). Here, I brieflydiscuss the issues addressed and focus on thetypes of analytics used and insights derivedfrom them.PwC had relatively high turnover for a keytalent pool: senior associates, the secondstage in the career ladder that starts at entry-level associate and ends at partner. Deferredcompensation was a solution under consider-ation to improve retention: Offer the promiseof greater pay in the future for those whostayed longer with the firm. The firm also hadanecdotal evidence that people who left thefirm at later career stages (after achievingmanager or senior manager status) had bettercareer outcomes in the long run, such asachieving CFO, compared to those who leftat earlier career stages (associate or seniorassociate). What the firm needed was evi-dence on whether a deferred compensationprogram would work as a retention tool, and

    whether more accurate informationdatabased, not anecdotalon career outcomeafter leaving the firm might cause people tchoose to stay voluntarily without the addtional incentive of a deferred compensatioprogram.PwC collected data by surveying current anformer employees on their experiences at thfirm and, for those who left, career progresion outside the firm. Some of the modifficult parts of the project included identifying the right samples of people to surveamong the former employees, getting them trespond, and figuring out which responsewere best to use for the analysisnone owhich required doing any advanced statistcal analysis. To start, this required deeknowledge of the firm s culture and relationships with former employees. That led tidentifying offices that were representative othe firm s business that had stronger networkamong the former employees. It also requireknowledge of how to get the former employees to respondthat is, by appealing to theongoing goodwill with those relationshipand to former employees satisfaction wittheir developmental experiences at the firand what those experiences delivered terms of career success.PwC used basic statistical techniques to estmate the total number of former employeeto survey, based on typical responses rates focomparable surveys, and to determine whicresponses to use in the analysis. The finanalysis sample focused on former employeewho had left the firm more recently (withithe prior 15 years), because their responsrates were higher and more representativand because their recollection of their experences at the firm was subject to less recall bia(versus those who had left more than 15 yeaprior to the survey). Advanced statisticatechniquesmultivariate regression waused to compare the following:(a) the career outcomes among forme

    employees who left at different careestages;

    (b) work-life balance for former versus curent employees at comparable careestages; and

    (c) drivers of retention for current employeeFor all of the analyses, multivariate regresion enabled an apples-to-apples comparisoby controlling for factors that might othewise have led to perceived differences amonthe groups and between individuals such a

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    level of education, whether the person had aCPA or other professional certification, gen-der, race, office location, total years of workexperience, and the line of service in whichthe person worked at PwC before leaving. Forthe retention models, multivariate regressionfurther enabled an analysis of which factorswere more important in driving employeedecisions to leave; this was essential to iden-tifying non-compensation factors, such aswork-life balance, that figured prominentlyin the process.The combined efforts oftheanalysis and sub-sequent retention initiatives by the firm hada clear impact. The analysis revealed th at thepractice of adding a deferred compensationprogram would have had a much smallerimpact on retention w hen compared with thepractice of addressing work-life balance andconcerns about career development and pro-gression. The actions the firm took includedstrengthening relationships between partnersand staff focusing on coaching and develop-ment, and providing new tools for leadersand HR to manage workload balance issues.The end result was a marked decrease in vol-un tary tu rnov er tha t met the f i rm soperational and strategic goals.

    (c) take orders and negotiate for additionaldisplay shelf space, which drives incre-mental sales volume (sales). hecompanywas experiencing low productivity andhigh turnover in the role. They knew thatcompe nsation was a potential issuebecause some regions had fallen behindthe company s local benchm arks for tar-get compensation.An initial regression analysis revealed thatregions with larger compensation gaps tend-ed to have higher turnover. Yet the statisticalrelationship was stronger for new hire turn-over and weaker for longer-tenured RSRswho have greater productivity and sales. Soclosing the compensation gap would havepositively impacted the talent supply of RSRsbut not necessarily productivity. To betterunderstand the situation, Frito-Lay launcheda study that included surveys of both theRSRs and their supervisors.The employee survey collected informationon years of experience before joining the com-pany in jobs that required the three differentskills related to th e job s components (driving/delivery, merchandising, sales), along withmany attitudinal measures. The supervisor

    The actions the firm took included strengtheningrelationships between partners and staff, focusing oncoaching and developm ent, and providing new too lsfor leaders and HR to manage workload balanceissues. The end result was a marked decrease involuntary turnover...

    Frito-Lay case studyThe second case also involves a talent man-agement and retention challenge, faced byFrito-Lay, a division of PepsiCo, anddetailed in Levenson and Faber (2009). Thekey talent pool in this case was the RouteSales Representatives (RSRs), who performthree tasks:(a) take the compan y s produc ts from thedistribution centers to the stores (driv-ing / delivery);(b) manage the in-store inventory and prod-uct placement on the display shelves

    (merchandising); and

    survey collected ratings of each RSR s abilityto execute the three different job dimensions,and a measure of how much time the supervi-sors spent covering RSR routes and theestimated lost sales as a consequence: Cover-ing routes takes away from supervisorsworking directly with the accounts, whichlimitstheir ability to increase sales above w hatthe RSRs can do on their own. This repre-sented one of the most difficult parts of thework because an extremely high response ratefrom the supervisors was needed to ensurethat a large and sufficiently representa tivesample of the RSRs would be rated. The highresponse rate was achievedbyclose coordina-tion between the analysis leaders and the

    senior and mid-level executives who comnicated throughout the supervisor ranksimportance of contributing to the study.Simple statistical analysis (means) ofsupervisor data on time spent covering droutes and the estimated lost sales provthe economic justification for investinclosing the com pensation gaps to reduce hire turnover. Adding to this evidence wlogic argument based on deep understanof the external labor market and changethe job over time. The job candidates trtionally came predominantly from the of high school graduates (i.e. non-colgraduates). Over 30 years, this pool shrunk from being three quarters of the population tohalf making it harder to atjob applicants. At the same time, thedemands had increased with a growing nber of products and greater channel retailer competition. These two trends vided additional logic behind keeping pathe intended targets. Acting on the anaand logic, the company made a signifiinvestment over three years to bring Rcompensation to the market targets.Mu ltivariate regression analysis of the suvisor ratings matched with emploperformance revealed a second pathimprovement. The analysis enabled a cparison of the importance of task execuacross the three job dimensions, both relto each other, and in the context of differoute types. The results demonstrated sales skills were the bottleneck on smvolume routes, and that driving/delivskills were the bottleneck on higher volrout es. The former further highlightedimportance of sales task execution in driincremental sales volume, an issue wknown within the organization. The lawas less expected, but was consistent wconcerns people in the organization about restrictive delivery windows at lformat retailers and the RSR s ability to etively serve those accounts. The study rehelped crystallize the decision to move ward with a job design change for throutes that added a dedicated hourly mchandiser while increasing the numbestores, route volume and capital utilizatFinally, a separate regression analysis ofrole of different types of work experibefore joining Frito-Lay revealed that psales experience contributed to sales volon both types of route. hisled to a modition of the hiring profile to put a greemphasis on prior sales experience.

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    thetool kit needed inagiven situation,

    EXHIBIT 1: CAPABILITY-OPPORTUNITY-MOTIVATION MODEL

    It is not the sophistication of statis-tical techniques that typically poses abarrier to meaningful analyticalinsights.

    has been col-

    itment in terms of resources and

    ed to get stakeholder alignment and sup-

    t s , t h e l a r g e r - s c a l e i n i t i a t i v e s

    This raises the questio n, Canbeapplied in thesecasesto improve

    Competencies:knowledge, skills,ability, behaviors Learning / training Time to productivity

    / - ^ ^ Organizationdesign Job design /responsibilities Cross-funct ionalroles processes

    Businessperformance Individual

    Location/mark et/ region

    Organization fitjob fit Satisfaction: job ,pay, career Total rewards Goal setting Work-life balance Leadership

    decision making and, if so, how ? A com-prehensive answer to this question cannotbe provided in one article. The foundationof knowledge needed to do so can be out-lined as follows in the remainder of thisarticle.

    The Capabi l i ty-Opportuni ty-Motivation modelExhibit contains a version of the Capabili-ty-Opportunity-Motivation (COM) modelthat has strong roots in both the research andpractice traditions (Blumberg and Pringle,1982; Boudreau, Hopp, McClain and Thom-as,2003). It was a core part of the approachused to conduct the PwC and Frito-Lay casestudies. The main point of the model is thateach of the following is a potential causalfactor behind individual motivation and per-formance. Usually, more than one, if not allthree, factors are involved: Capabili ty: knowledge/skil ls/abil i t ies(KSAs); how they are built through on-the-job learning, training and development; thetime it takes for someone to get to full(average) productivity in the role. Mo tivation: all the factors that influencemotivation in the role including relationshipwith supervisor, fit, satisfaction, rewards,and work-life balance. Opp ortunity: the structure of the role and

    organization that enables and/or impedes

    performance in the role, including botformal and informal processes.As a diagnostic too l, the COM model is standard for identifying the complete rangof factors that impact individual performance, and the collective performance of thentire group of people in arole.Asin the Pwand Frito-Lay cases, it can be used to definthe domains of data to be collected for ain-depth analytics project.In cases where there is not sufficient time foin-depth analytics, the COM model caserve as a map for checking whether thappropriate questions are being asked abouwhat is driving behavior and for testinalternative scenarios beyond what is initially identified. Many functionally orienteHR people consider only their own area oexpertise and infiuence when determining course of action to tak e.Thistypically m eanonly the cap abil ity angle or only th mo tiva tion angle; it rarely means th opp ortu nity angle, as that key aspect often ignored when HR assesses possiblsolutions to productivity challenges. ThCOM model can help HR professionals tbreak the cycle of only inwa rd-looking diagnosis and consider other factors. Foexample, a compensation and benefits person presented with the Frito-Lay RSRchallenge might easily have chosen to focuon closing the regional gaps in compensation while ignoring other possible causa

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    EXHIBIT : WORK DESIGN EMBEDDED IN HUMAN CAPITAL BUSINESSMODEL APPROACHES

    HC a ndWork DesignJob alternativesCareer alternativesJob dynamics over time

    Business Modeland Work Design P L impacts* Buy vs. build

    Work design thatmaximizes engagementando rgperformance: In dynamic labormarkets2. W ith P L impacts3. That stand the testof time

    factors. Using the COM model, diagnosticquestions that could be asked include: Could a lack ofskills,including sales skills,contribute to low productivity? Ifweclosetbe compensation gap but do not address

    recruiting profiles, would that ensure weget the right mix of sales skills in the role? What happens to the supervisors whenturnover is so high? Do they have tocompensate for RSR absences in ways tha thurt overall sales performance?Asking these questions without extensivesurvey data collection and analysis does notguarantee the exact same insights found bythe study. But asking these questions andengaging with the other experts in HR andthe line organization would greatly increasethe chances of identifying viable options forimproving the sales recruiting profile andaddressing lost sales opportunities fromsupervisors who are being overloaded.

    Labor iVlarkets ModelExhibit 2 embeds work design and organiza-tion design inside a framew ork tha taddresses external labor market and careerissues as well as business model issues. External labor market and career issues,including:

    o external job opportunities;

    o alternative career paths available to eachperson and differences across people intheir chosen career paths; ando job dynamics as people transit ionthrough different career stages, includingtrading off compensation today for

    development that can lead to greatercompensation tomorrow. Business model issues:

    o W hat are the P L assumptions behindthe work design, including options forpaying more (or less) to attract andretain higher (or lower) productivityworkers?o How to evaluate the buy-versus-builddecision for a given skill set, includingoptions for outsourcing individual jobs/

    rol sor entire segments of the pro ductionprocess?Using both the COM and labor marketsmodels is important for evaluating the full setof options available to an organization relat-ed to increasing profitability. The COMmodel alone addresses only productivity, notthe bottom-line impact.For exam ple, in the Frito-Lay case, the labormarkets model was critical for identifying theoption of adding a dedicated merchandiser tothe higher volume routes as a cost-effectivesolution for dealing with the challenges of

    low productivity on those routes. The lmarkets model in both the PwC and FLay cases was important for evaluatingrole of alternative jobs as drivers of motion and productivity. One major benefthe labor markets model is that it primarelies on logic, which is very helpful wthere is not sufficient time for extensive collection and analysis. For example, the clusion reached about Frito-Lay Rcompensation gaps based on the histotrends in college attendance and changethe job design used elements of both the lmarkets and COM models without tintensive data analysis.

    Organization Design ModelExhibit 3 presents the classic organizadesign model pioneered by Galbraitb (19Since then, there have been many other onization design models, yet the essence omodels is the same in the fundamental that is relevant for our purposes: There mbe alignment among all the key organizadesign elements, and both formal and inmal processes are needed to ensure succeoperations across the entire enterprise.Organization design is listed as a designment in the COM model in Exhibit 1, may seem repetitive to address organizadesign as a standalon e m odel in Exhibitdone to highlight the different levels of aggation that mustbeaddressed when anaorganization bebavior and performance.COM model focuses exclusively on an vidual or on groups of people who occupsame role. It can be applied to diffegroups of people in different roles, buaccuracy diminishes greatly as the rbecome more and m ore dissimilar and/opeople in the roles become more and mdissimilar.The organization design model, in conaddresses the aggregate organization beiorissu stha t exist enterprise-wide, inclmost imp ortantly, where processes work versus how they break down across dsional and functional lines. Neither the Cmodel nor the labor markets model adde s t h o s e c r i t i c a l d e t e r m i n a n t sorganizational performance and succMoreover, from an analytics perspectiveorganization design model is data-lmeaning that organizational diagnoses ocan be made through qualitative assessmof decision rights and the formal and inmal processes by which work gets done inorganization. Advanced statistical techni

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    geted analytics that often does not

    cs in the HR function to this point has

    lists w ho do the bulkin HR. In this article,have shown a numb er ofwaysthat an alytics

    here,Ihave shown that

    part of the w ork typically consists of

    the data needed for the statistical analysis.Those tasks and competencies must be mas-tered by HR generalists, not analyticspecialists, to increase the overall level ofinsightful,analytically based decision makingin HR. Mastering thetn is doable for mostpeople, so this is not an impossible challengefor today s typical HR generalist.Another key issueisthe time needed to do deepanalysis. The models discussed hereCOM,labor markets, organization designall haveelements that can be applied as diagnosticsusing logic exercises when time is insufficientfor intensive analytics. If HR professionalscan master these models and others like them,that will raise the level of analytic competencethroughout the function. It will not diminishthe role of statistical analysis in HR decisionmaking and, to the contrary, likely willenhance it because many more opportunitiesfor applying advanced statistics to diagnoseHR issues will emerge from applying goodanalytics more broadly. [^S

    ReferencesBiumberg, M. and Pringle, CD . 1982.TheMissing Oppor-tunity in Organizational Research: Some Implications fora Theory ofWorkPerformance. cademyof ManagementReview,7(4): 560-569.Boudreau,J.W. Hopp,W. McClain, J.O. and Thomas, L.J.2003.On the Interface Between Operations and Human

    EXHIBIT : G ALBRAITH S CLASSIC ORGANIZATION DESIGN MODEL

    ManagementProcessesSource;Galbraith 1977)

    Resources M anagement.Manufacturing ServiceOptionsManagement 5(3): 179-202.Boudreau, J.W. and Ramstad, P.M. 2006 . Talentship anHuman Resource M easurement and Analysis: From ROto Strategic Organizational Change.Human ResourPlanningJournal 29 .Galbraith,J.1977.OrganizationDesign,A ddison WesPublishing Company.Lawler, E., Levenson, A., and Boudreau, J.2004. HR Merics and Analytics: Use and Impact,Human ResourPlanningJournal 27(4): 27-35.Levenson, A. 2005. Harnessing the Power of HR Analyics,StrategicHR Review,4(3): 28-31.Levenson, A. and Faber, T. 2009. Cou nt on ProductivitGains.HR Magazine, }une,68-74.Levenson, A., Fenlon,M.J.and Benson, G.2010. Rethining Retention Strategies: Work-Life Versus DeferreCompensationin a TotalRewards Strategy.World tWJournal Fourth Quarter, 41-52.Levenson, A., Lawler, E., and Boudreau, J. 20 05. Surveon HR Analytics and HR Transformation: FeedbacReport, Center for Effective Organizations, University oSouthern California.Rothwell,W.J.and Srcdl, H.J. 1992.TheASTDReferGuide to Professional Human Resource DevelopmeRoles Compe tencies, Second Edition, Volume Amherst, MA: HRD Press, Inc.

    Alec Levenson is senior research scien-tist at the Center for EffectiveOrganizations, Marshall School ofBusiness, University of Southern Cali-fornia. His action research andconsulting work with companies opti-mizes job, HR and organiza t ionperformance through the applicationof organization design, job design,human capital analytics and strategictalent management.Dr. Levenson s w ork with companiescomhines the best elements of scientificresearch and actionable knowledgethat companies can use to improveperformance. Heuseseconom ics, strat-egy, org aniza t ion b ehav ior andindustrial-organizational psychologyto tackle complex challenges that defyeasy solutions and to derive lastingimprovements in critical areas.Dr. Levenson has trained numeroushuman resource professionals in theapplication of human capital analytics,representing a broad range of Fortune500 and Global 500 companies.

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