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304

I 11 Choosing and Hiring Candidates

Outline Hiring Top Performers at MarineMax C hoosing Candidates

Combining Candidates' Scores Who Makes the Final Hinng Decision? Legal Issues Rel:ited to Hiring Candidates

J ob Offer Strategies Creating a Job Offer Compensation o~-cisions

The Employment Contract Legally Binding Contracts Common Contract Content Additional Agreements

Presrnting a Job Offer Negotiating

Negotiating with New Hires Rencgouating Contncts Derdop Your Skills: Job Offer Negotiation Tips

C losing the Deal Fairness Perceptions and Rejections

Rejecting Reneging

Hiring Top Performers at MarincMax Summary

LEARNING OBJECTIVES After slud)'ing this chapter, you should be a bit to:

Describe different ways of combining candidates' scores on different assessment methods to calculalC 111 overall score.

• Desmbc three different ways of making a final choice of whom 10 hire. Discuss the factors that innuence the content of a company's job offer. Describe the four different job offer strategics.

• ~:;:~ different types offaimess and explain why Cilndidatcs· pcrccplions of fairness arc impwtanl to

Ch.1ptcr 11 • Choosing and Hiring Candidate.~ 305

Hiring Top Performers at MarineMax

n-.1uon:il boating a~ y~ hling retailer M~ncMax is a publicly traded, billion•dollar boal broker Ii-\ (l ier 50 retail t~auons. Foc~SC<l ~n prcm1umb~, stJChas Sea Ray. Boston Whaler, Mcridilln, " 11 iirJS· GndY White, and the .Fcrr~lll C~p. i\~anncMax complements its industry•leading brands !U1 ·I 51\ C, valU('-addcd SCr\'ICC.S including dedicated de\J\·ery captains, clamoom and in•Wilter CUS· ;',~::·:~~ning_. p~fessionally organized Ge1a1U}'SI cruises., a no-haggle sales approach. nnd extensive

;(lrr·~~;p;;i::cs;Le _and continued grov,th, M~neMll.'I v,~kJ h~rd to retain 1he high level of servic_c ,r-ooal 1111cr.K"UOn found_ a1 smaller comparucs. TocOllunue 1lS growth. MarincMtix needs enthus1•

irJ r,: lcntcd. and highly mouvatcd employees with leadership potential for jobs acroiS the company.3 W~\~~plflY 1,; 11ows that th~ people are hsrd to find Md looks f?" wa)·s to ~resent a?peal ingjob offers Tl! tJleni. The company s managcrs know these people are 1111crestcd m int.ang1ble benefits. such 1~ 10~lenging work and 3dv~ment opportt.mities, In addition to top pay.~ MarineMax :isks you for ::1~ on how it cao bcSt persuade itS top finalists lO accept 11s Job offers without rarchcting up its labor ,~,ismorcth:in necessary.

this paint in the book. we have focused on planning the slllfling effort. attracting appli• Lip 1~ and C\"aluating job candidates. The next stage of the staffing process involves choosing ,a;bh,ring tlie best fi nalists. Because the cost of making n had hiring decision is often many 3r. _ chat person"s salary,5 not lo mention the effects of losing 3 high.potential candidate to a ::~uwr, choosi ng whom to hi re nnd persullding them to accept your offer arc ncritkal pan of the ~tr.ncgic staffing proc_css. _ _ . , _ .

Imagine the following evalwmon scores for two finalists for a financial analyst s pos1uon: \[:ma r,coll!d 80/100 on a job knowledge lest, 35/50 on a structured interview, and 45/50 on a · fSO!lJlity and values test designed 10 assess her fi t wi th lhe organization' s culture and values.

;cored 70/ l 00 on the job knowledge test. 45/50 on lhe struccurcd interview, and 35/50 on the pi:r;onali1y and values tesL Based on lhciescorcs, who should be hired? The answer depends ~n th~ 11 ay the firm weights :1nd combines the scores. ff the scores arc averaged, Maria would R:,ei\C ,he job offer. But if lhe structumi interview scores arc gil"cn greater weight than the other :Messments, Pete might be the preferred candidate. If candidates must score at least 75 on the iob ~now ledge test, however. lhen Pete would be out of the running.

nicrc arc different methods or combining candidates' assessment scores. All the meth• C\ls :ire relatively easy to understand, but you need to know how to use 1hem. You also need to ur,JmtJnd how to go about persuading the best candidates 10 join your firm. In this chilpter, we di"·u~s Ute methods used to choose candidates a.s well as the employment contract and job offer prro:c,s, negotiation, and closing the deal. After reading this chapter, you should have a good umkr,t:mding of how best to choose which final ist should receive a job offer, and how to r,:r;uJd..- that person to become an employee of your finn.

CHOOSING CANDIDATES

Some candidates lite so exemplary that to pre\"cnt them from accepting other companies' offers. finns 4u1ckly offer to hi re them prior 10 evaluating all their potential candidates. However, this i, unu,ual. It is more common to combine candidates' scores on a \'ariety of assessment methods onJ compare their overall scores to dctcnnine whom 10 hire. We discuss how this is done next.

Combining Candidates' Scores Th,·r..- arc two "llYS of combining candidates' assessment scores so thJl they can be compared 1<t1h one Jnothcr: 1hc multiple hurdles approach and Lhc compensatory approach.

THE MULTIPLE HURDLES APPROACH Requiring candidates to pcrfonn 31 a Sll tisfactory level on (inc :l\S('SSment before being allowed to continue in the selection process is called a multiple hurdlrs app roach. As we explained in Chapter 9, it is common for fire fi ghter candidates to be rcquin;tl to pa~s a str('ngth test (e.g., a Lest requiring them to lift 40 pounds) early in the assess• m,·nt proccs, . If candidates lack the physical capabilities they need. there is no point in wasting

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~:~1:;:~ ::~i:u~~1\::::i;~~: ~i;i;;~~::no::~; :::i;~;na:•;~;~::~_i uate thcrn if lhcy do Toe FB I uses :i mulup!~ hurdles uppi:o:ich w~en hiring special agenis. Because ttic·

mtiw :i l-,1l11 i,:s. problcm-solvmg, and deci~ron-makmg tr eog. ~Pl-'C13l agent applicants to pass n biodata inventory, Judgment t~·st before b.!1ng allowrd 10 pro<.'ccd in the dks approJ.ch is costly and takes m_orc lime, the :ippro:ich is generally used when the c • ;:::;.:i:::f:;:t ~;tmiancc is high-for example, when the pmon has lhe PQtcnti: ~frZ;

American Express uses a three-pan mu ltiple hurdle :bscssmcnt sys1em in which :c::;;~i;!:;s:1_1 ;;/1e current asscssme111 arc allowed to advance 10 the next sta~:I~~

ny 's Web si1c to rdocate. A summ:iry rcpon is thcn e-mailed 1 dccides which applicants become candidates and :~:~:~:~t~a~d:;::: :::!:r":~~~~d~~:s1!:\:a1: : ~:::~7n;:~:1~i~::}~:;dcn_tified by : an mtcrvicw.l THE COMPENSATORY APPROACH \~hen you think about 1he bcs1 professo~ you have do y~u thmk they arc rqu:i.lly t:i.lcntcd m every w:iy? Probably not. It' s more like] tha had, fovonic professors had different talents as wdl as different combinations of them F y t }OLJr you might have had a profcssor_with outstanding s1orytc-l ling skill ~, whereas anoth~r:i:x::nplc, ~rofr~ors who lacked those ski lls could have been equally cffoctJ\·e because of his or her }:u '.ty 10 inspire and challenge you. The compcnsotory opproach takes differences su h a JI. mto account. It allows high scores on some assessments to compensate for Jow sco~s ;: these assessments. For e,~ample, a company might allow a candidate's successful work experie;:cr compens.:ite for a lower grade point avcr.ige (GPA). lo

lbcre arc several ways to execute the compensatory approach. Job expcns ca - each_cand1~1e's scores on the different asscssmenls as well as any notes and other inf:r::: acquired d~nng the asscs~men1 process. The over.i ll_ judgment of the finn ·s job ex pens can then be used 10 integrate the different scores ~nd dcte~me each candidate's total score. Their jud • ment can also be used t_o_m.'.lke the final hmng decision or 10 dctcnnine if a candidate advances t the n~xc ~hasc of~e ~m~g process. However, because diffe~nt expcns arc likely to use differ- c~t cmena an_d wer~h II differently, the_ ris_k of legal troubles mcreases when this method is used, None~heless .. t~ the JOb e~pcru: ha\'e a s1gn1 fican1 amount of experience when it comes to making ~lcct1on decisions. and 1fthe1r acceptance of the selection process is imponant, relying on !heir Judgment can bi! appropriate.

_ J_ob ~xpcns· ra~in_gs may ~r may not produce the same scores as unit weighting. Unit "e1ghung mvolvcs gavmg multiple assessments equal weight when computing a candidaic·s ~vcr.111 score. If all the assessment methods a finn administers arc equally useful predictors or J~b success. then uni t weighting is approprialc. However. as we discussed in Chapter 8, when different assessmen ts arc made using different scales (e.g., if an interview is scored on a I to JO point scale but intelligence is scored on a I tO 60 point scale) si mply adding them togclher to producc an O\"erall score does not equally weight the scores. The importance of the inte lli• gence assessment would be overweighted and the imponance of the interview would be undtr- weighted. In th is case. the raw scores must be standardized before being combined.

Table 11 -1 shows an example of convening raw 10 standardized scores that are combined using a unit weighting approach. Ying·s score of 89/100 is J.5 standard devialions above a\er- :ige on lhe standardized interview, and Tony's score of 60/100 is .4 standard deviations above a\"era~e. Ying's know~edge tes1 score of 25/40 is J .I standard deviations above overage, and !:ii 5 ~ore of 35/40 1s l: 7 ~1andard deviations above average, Ying 's assessment center score

0 is .9 standard devia tion above average, and Tony's assessment center score of 7/10 is 1.1 stand~ deviations above a\"erage. Because the scoring of the assessment methods is so dif• fcrc nt, usmg scales ranging from I to 10 and Oto JOO and 1/icn adding candidates' raw scores "ould greatly overweight their Slructured interview scores and underweight their assessment center scores. Standardizing these scores makes it possible 10 compute an overall score for each candidate that can be compared \\ith 01hcrcandid:i1es' O\'eral l scores. In this case, al though Tony

Clu!ptcr 11 • Choosingand ll 1ringCandid.ites 307

the Unit Weig hted Approach 9

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Standa.rdizlng and Combining Raw Scores Usin

~---Ying Tony Raw Standardized

S'.l'l.l ctured lnterv1ew 89 15

(nowledgeTest 25 1.1

;asess rnent Center .9 Un, t Weighted Overa!I Score 3.5

Raw

60 35

Standardized

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formed Ying on the knowledge test and assessment center, Ying's strong performance ~:~c 10 Tony's in the struc1urcd in~erv!cw resuhed in her o~·ern\l score of J .5. That' s greater

Tony's overall score of 3.2, so Ymg 1s detemiined to be the stronger candidate. ihon Rutional weighting requi.res ex pens to assign a different subjec ti \·c weight 10 each assess-

cnt score. Job expcns. ine_ludmg hiring managers, _dctennme the weights based on the extent : .,.htch ihe job expcns behevc each assessment is 1mponant to a person 's on-the-job success. \ c,indidace·s score on e~ch ru:scss~ent method is then mult ipl ied by th:it assessment methoJ"s "ri£hl. and each assessment s weighted score is lhen added to produce an overall score.

ithou~h this approach has the advan~ge ofrecogmzing that each score contributes differently A the candidate's overall assessment, 11 requires U1e jobexpens to agree on the weights. In addi- :~on. there is no guarante~ that the_expcrts' weights will best p_redict success on the job. Clinical ~;.:ssments. which we discussed m Chapter 10, u1ilize the rauonal weighting approach.

Sltltistical weighting involves the use of a statistical technique. such as multiple regres· ,ion. 10 assign a different weight to each assessment score. A multiple regression analysis. a JalJ analysis tool initially discussed in Chapter 8, is a statistical technique that identifies the ideal wc1ghis to assign each assessment score based on each assessment method's correlation 111ihjob success and the degree to which the different assessment methods arc intercorrelated. A mul tiple regression :inalysis is the most scientific approach to determining how to weight each l,¼,,mcnt. Recall that the output of a multiple regression includes a fonn ula that looks like thi s:

Overall score = c + (b l X al) + (b2 X a2) + (b3 X a3).

Recall also that c is n constant, the b's are the statistical weights applied to each assess- ment method to maximize the validity of the group of assessment methods, and the a 's arc a cand1Ja1c 's scores on each of the assessment methods. Any number of assessment methods can t,e u)cd For example, if the regression equation for a salesperson looked like this:

Q1 erall ~corc = 24 + (.20 X Cognitive ability ) + (.25 X (ntcrvicw) + (. 15 X Personality )

Jnd the candidate's cognitive ability score was 70, h.is or her imerview score was 75, and pcrson- ~!1ty ~ore was 50, then the candidate's overall score would be 64.25:

Over.i ll score= 24 + (.2 X 70 ) + {.25 X 75} + (. 15 X 50 ) Overall score = 64.25

Tiw; O\ crall score is then used to evaluate whether the candidate should advance to the ne,t ph.l5c of the assessment process or whether a job offer should be extended. This method can produce bclter hiring outcomes than can either unit weighting or rational weightings by job C\f!Cn~. However, to be accurate, the multiple regression approach requires HR professionals to collm a sample of several hundred or more candidates. Jf you don't have a large sample size 111th II hich 10 work, unit weighting or rntional weighting may be a better choice .

COMBINING THE MULTIPLE HURDLES AND COMPENSATORY APPROACHES Often . some J~b requirements arc essential to pcrfonning a job, but others can compensate for each other, as 11c hJ.\"C indicated. For e:mmplc, a data entry candidate might need to type a cenain number of 11 mds per mmutc with a minimum number of errors. These skills might be used as hurdles in an

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mitrn.l a~.'>Cssmcnt. Hoiicn·r. his or hcr cognitive abll i11cs,!o_b knowkdgc, and [)Cr-.onalit Ix· C('lfOjX'ns.'.IIOl)' focwn:. In this l'a:;t·: after cleanng the m11~al hurdles , the J>Crson's c~ rn1g1i1 abilmcs, Job l:nowlNgc, and personality s.c~rcs would be w~1ghtrd and combined 10 a cof,ln1111t s:i.tory nrn.nnt.'r, :md the highest s.conng candi~atr offered the JOb. TI1c FBI srccial-agcnt SC rnpen procr.'sS we mcntioncJ earlier m the chapter 1s nn example of such an :ipproach_8 lccllori

fm. ,;:;u7°~~ ::~~;~l~•gfi~;l~;~.~~::;c~ ~s1:1~n1:cu~00:o:~~t::~~:lt~ rccc11c JOb ((. mcnts arc hin.'CI. Cut scores arc often dctL'nnmcd by JOb cxpcns based on the job' by a regn:ssron cqu:11100. The b,:l of a cut score is baslXI on what level of prcdi 1s m1n1mally acccprnblc. Jfa company's talent strategy 1s to hire only the bcsl, th

set at a high kH:-1. By contra.st , 1f a fim1 is focused on filling vacancies in the than hiring people for long-1em1 careers, then a lower cut score might be more company is pursuing a cost- lcadcn;hip strategy, a lov.cr cut score might be pleatthc1argc1,:dsal.:ll)'kvel One problem\\ . offoJ54.•ne1?anvcsb.."Causcthcyresultinthcr Be-cause this can increase the ad\·crse 1mpacl o ing its cut scores to reduce the number of folsc negatives among women and minonties lower.

Cut scores can be set in 1hrec ways. If job cxpc::rts can establish a minima1i able competency level, then the cut score can be sci at thi s level. If the company nc~ accq,i. quickly. the first candidate who exceeds the cut score IS sometimes hired. Hov.ever b IOh11t this approach, the fim1 nsks losing the opponunity to hire a more desirable candidate "w: llk.ing soon be rccruitcd. If a company's strategy is to hire top talent, 1t makes more sense 10 ° might

~ I of candidates before making a final hmng dccision unless an exccp1ional can:~:%'~ ---M s Ano1her option is lo compare c:mdida!es· assessment scores 10 each other and rank order '.hem from highest 10 lowc~I score. One of the primary issues wi!h .using rank ordering is that 11 docs nm gu.'.lr.lntec that any of the c.:1nd1da1es meet or cxcccd m1n1mum hiring standards. C scores do a better JOb of reducing the number of unqualified people being hired. Combining d; two methods and making Job offers 10 the highest-ranked candidates who e~ceed I cut score helps 10 kver.igc the strengths of both approaches.

Because rank onkring c.:1ndid.:l1cs can result in ad\ersc impact, ii is not always a desirable way to choose your emplO)'L'CS, despite the fact that it can be highly 11a!id and cost-dfcctive. Sometimes bandmg C.:ln be a beucr altemali\e, Recall that with banding, everyone who scores within I cc,. tain r.tnge of scores 1s cons1dercd 10 ha\·e pcrfom1cd equivalently and assigned the same grade. A student earning 98 percent in a course rcce11·cs the same A gr.tde as a student earning 93 pcttcm. Hiring v.ithin the band is then done randomly or based on other factors, such as the finn 's Equal Employment Opponuni t}' (EEO) or affinnati1·e action goals, the company's desire 10 promote an internal candidate who might otherwise leave the company, the experience and languages spo1.en by candid:ues. and so fonh . The widths of che bands arc generally calculated on the ba..is of the sundard errorofmcasurement9 described in Chapter 8. Table l 1-2 illustrates the use of banding

Although banding can help reduce advcnc impacl. it docs not alw.:1ys do so. 10 It also cannot remedy deficiencies in the sourcing and rccrui1ing of qualified minorities. However, it can help minimize the impacl of measurement errors. After all. is someone with an overall assessment

i(ljijifj Banding Candidates Candidate Overall Score Band

Amy 87 v, 85 Lee 79 Pedro 78 Am,la 78 81U 65 Tom 63 Lm 61

Chapccr 11 • Choosing ;ind Hinng C;indidates 309

• rf 87 3 \a~cly be.Iler t~an a candid~te whose score is 87.J-or even 86? Probably not. No ,s ri: p,:rkct- a c:m<hdatc s true score LS likely to be a little higher or lower than the score the \i;n .ictually rccc'.v~s .. lllt!s,. rank ordering candidates won't always produce the most accurate r.: 1 r t,cst turc. 1 his is p~icularly true when the differences betwee n candidates· scores arc ~~: 11

1 1 ~!y ,niall or when ,the standard_error ~f measuren:i~nl is larg~.

r- When using cut scores or ran_k ordcnng, an addmonal decision needs to be made about uhcthrr to hire from the top of 1.he hst down, or to create n pool of fin nlists from which 10 make

(inal choin:-. 1:111s pool offin~lLS\s can then be ran k ordered based on over:i.11 or spec ific asscss- J Alirmauvely, the candid.:l tcs can be randomly selected or banded. There is no one best mcnl~ ofchoo~ing the finali sts 10 whom 10 extend job offers. At the very lc:1s1 the finn· s cutoff If.Cl:, ~hould be set high e1~ough so the company's new hires at least meet or ~xcecd .:1 ny mini-: rn )iandwxts of success. Who Makes the Final Hiring Decision7

1 du~in:1l-organiz:1tional psychologists arc cxpens nt candidate assessment and test construe·

n Human resource professionals arc experts in the staffing process. Although these pcor,le 51affing policies and procedures, often make initial ~pplicant screening dccisions, and

her ha\C or know how to find legal and technical infonnat1on rcle1'ant to the assessment and cit , process, it is hiring managers who arc usually responsible for making actual hiring deci- :,1::s~ Bccausc the hiring .manager will be supervising the person hired, he or she is also one of I:~ prirn:11) stakeholders in the staffing process and should be in11olvcd in creating :m.d evaluat- 1 , staffinc policies and procedures. If the work is done in interdependent teams, it 1s also not ~nn~cornmo~ for the teams to be invol11ed in making the fin al choice.

Legal issues Related to Hiring Candidates Legal iNleS arc present throughout the hiring process. The candidate-choosing stage is no e\c,•pnon. In addit ion to the legal issues discussed in Chap1cr 3, people in 11olved in the staf~ng

rl'CC)S should be familiar with the Fair Labor Standards Act (FLSA), the Family and Medical rc .ii·c Act (FMLA), .:1nd the Uniform Guidelines on Employee Selection Procedures (UGESP).

TIIE FAIR LABOR STANDARDS AO The FLSA 12 covers working hours and the payment of owtnne or compensatory time off 10 nonexempt employees. If an applicant is not hired because th.: person has previously exercised his or her rights under the FLSA (by requesting earned ~wtime pay, for example). a coun may conclude that the :ipplicanCs rights hove been violated.

TIIE FAMILY AND MEDICAL LEAVE AO The A.ILA13 entitles qualilied applicants up to 12 11 ccks of unpaid leave for cenain reasons. If an applicant is not hired becausc he or she had pre1 iously r :-.:cn:iscd his or her rights under lhe ™LA, a coun may conclude that the appl!cant 's nehts ha\'<.' be<.'n violaced. For example, a federal appellate coun held that an employer vwlated 1; act by failing 10 hire an applicant bec.:1use, in the opinion of her employer, she had taken a lo tof lca\c.14

THE UNIFORM GUIOELI NES ON EMPLOYEE SELEOJON PROCEDURES The UGESP15 apply (o 1hc c:ind idat<.' -choosing process when adl'crse impact is occurring. The UGESP require finns toc11hcr clirninat,: adverse impact or justify ii through validation studies. The UGESP ge nerally ,1 :11..- ih :11 c11 1 scores should be set no higher than the level necessary for new hires to p,:rform rrofic"1..-ntly. Dl·tcrmining the minimum proficiency level is up to the comp.:lny. The UGESP also ,1a1r 1h:11 "1hc way in which nonnal expectations of proficiency within the workforce were deter- mined and th,: way in which the cutoff score was de1ennincd" should be described.

,h 1\..: ,::-.plumed in earlier chapters, to deal with ad1•ersc impa.c1 problems. the UG~S.P ~1,o di-..:u,, the use of":ihernati1·e procedures" that cause less ad11erse impact but that have s1m1- l~r 1:.ilid1t) to the problematic procedure. A cognitive abil ity tc~t may do a g~job ~rcdicting J1•h ,tic.c" but ii may result in adverse impact. In contrast, using a structured mtcf\"1cw and a 11orl. , :implc may have the same valid icy without generati ng adverse impact. The UGES~ do nut rd1~ \C :.i company of any of its affimtative ac tion ohligations, and ·:cn~ourage the adopuon and lmplcmcntation of\·oluntary affi rm:i ti vc action progr:ims" fo r organ1zat1ons that do not currently h.n c an} ,