The use of person–organization fit and person–job fit information in making selection decisions

Organizational Behavior and Human Decision Processes 116 (2011) 203–216

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Organizational Behavior and Human Decision Processes

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

The use of person–organization fit and person–job fit information in makingselection decisions

Tomoki Sekiguchi a,⇑, Vandra L. Huber b

a Graduate School of Economics, Osaka University, Japanb Foster School of Business, University of Washington, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 1 September 2009Accepted 29 April 2011

Accepted by Paul Levy

Keywords:Selection decision makingPerson–organization fitPerson–job fitPosition characteristicsContract durationTask elements

0749-5978/$ - see front matter � 2011 Elsevier Inc. Adoi:10.1016/j.obhdp.2011.04.001

⇑ Corresponding author. Address: Graduate Schoolsity, 1-7 Machikaneyama, Toyonaka, Osaka 560-0043

E-mail address: [email protected] (T. Sek

Two policy-capturing studies were conducted to investigate how person–organization (PO) fit and per-son–job (PJ) fit information are weighted and combined when hiring decision makers evaluate job can-didates, and how the process is influenced by the position’s characteristics. Regarding the combiningprocess, we detected a nonlinear, conjunctive rule in which a low level of PJ fit was paid more attention,the levels of PO fit and PJ fit interacted, and candidates with moderate levels of PO fit and PJ fit were pre-ferred over those with high and low levels. Regarding the weighting process, we found that PO fit wasweighted more heavily for a permanent position, and PJ fit for a fixed-term and/or a knowledge-intensiveposition. In addition, the position’s contract duration (permanent vs. fixed-term) and task elements(managerial vs. knowledge-intensive) interacted in influencing the weighting of PO fit and PJ fit.

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Introduction

Research on employee selection has traditionally focused on theassessment of the match between job requirements and qualifica-tions of job candidates in terms of their knowledge, skills, and abil-ities (KSAs). In recent decades, however, researchers have alsobeen interested in the potential benefits of selecting employeesbased on their fit with the culture and goals of an organization(e.g., Bowen, Ledford, & Nathan, 1991). These different aspects ofjob candidate fit refer to person–job (PJ) fit and person–organiza-tion (PO) fit, respectively. PO fit refers to the compatibility betweena person and an organization (e.g., its values and culture) (Adkins,Russell, & Werbel, 1994; Kristof, 1996). PJ fit refers to the matchbetween job requirements (i.e., KSAs) and applicant qualifications,or the match between the needs of the applicant and the suppliesfrom the job (Edwards, 1991). The latter may be the most impor-tant type of fit from the employee’s or job candidate’s point ofview, and is not the focus of the present investigation. Researchhas shown that both PO fit and PJ fit are related to a number of po-sitive employee attitudes and behaviors including satisfaction,commitment, retention, citizenship behaviors and performance(for reviews, see Arthur, Bell, Villado, & Doverspike, 2006; Edwards,

ll rights reserved.

of Economics, Osaka Univer-, Japan.iguchi).

1991; Hoffman & Woehr, 2006; Kristof-Brown, Zimmerman, &Johnson, 2005; Verquer, Beehr, & Wagner, 2003).

Past research has shown that both PO fit and PJ fit play animportant role in the hiring context. In earlier studies, Rynes andGerhart (1990) found that recruiters judged job candidates’ PO fitas a distinct construct from general employability. Their resultsindicated that recruiters used subjective judgments of interper-sonal skills, future goal orientation, and personal appearance to as-sess PO fit. Similarly, Adkins et al. (1994) showed that recruitersdistinguished between PO fit and employability, but suggested thattheir perceptions of PO fit were largely influenced by the similar-ity-to-me bias or the similar-to-ideal bias rather than the job can-didate-organization value congruence. On the other hand, Cableand Judge (1997) demonstrated that interviewers assessed thejob candidate-organization value congruence with significant lev-els of accuracy and used it to assess job candidates’ PO fit. Theyfound that the subjective assessment of PO fit had large effectson hiring recommendations and subsequent job offers. Kinicki,Lockwood, Hom, and Griffeth’s (1990) study also found that sub-jective evaluations of PJ fit were more strongly related to hiringrecommendations than objective qualifications were.

Kristof-Brown (2000) examined recruiters’ perceptions of PO fitand PJ fit simultaneously and showed that job candidates’ KSAs arefrequently used to assess PJ fit, whereas job candidates’ values andpersonality traits are frequently used to assess PO fit. She alsofound that PO fit and PJ fit each explained unique variance inrecruiters’ hiring recommendations, with PJ fit explaining more

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204 T. Sekiguchi, V.L. Huber / Organizational Behavior and Human Decision Processes 116 (2011) 203–216

variance than PO fit. Other studies focused on the role of job can-didates’ impression management and influence tactics on hiringdecision makers’ perceptions of PO fit and PJ fit. For example, Kris-tof-Brown, Barrick and Franke (2002) found that job candidates’self-promotion significantly affected the interviewers’ perceptionsof the candidates’ PJ fit. Higgins and Judge (2004) found that jobcandidates’ ingratiation tactics significantly affected interviewers’overall fit perceptions, and these perceptions in turn were relatedto hiring recommendations and job offers.

In summary, past research suggests that hiring decision makersassess both PO fit and PJ fit in evaluating job candidates. Their per-ceptions of PO fit and PJ fit are influenced not only by the job can-didate-organizational value-congruence and KSAs, but also byfactors such as similar-to-me bias and impression management.However, once developed, hiring decision makers’ subjective per-ceptions of PO fit and PJ fit have a strong impact on selectiondecisions.

Nonetheless, there still are research gaps, and several issues re-main unknown. Two issues are particularly important and are thefocus of our research. First, past research has not focused on howhiring decision makers actually combine PO fit and PJ fit informa-tion once they obtain it. Some studies examined either PO fit or PJfit only, and other studies that included both types of fit examinedthe simple linear combination model (e.g., Kristof-Brown, 2000).However, decision making literature in general, and personneldecision making literature in particular, consistently demonstratedthat human judgment and decision making involve a nonlinearcombination of information (Brannick & Brannick, 1989; Hitt &Barr, 1989; Ogilvie & Schmitt, 1979). If this also applies to the com-bining process of PO fit and PJ fit information, then predictionsfrom levels of PO fit and PJ fit about who will be evaluated morefavorably and eventually offered a job may be different from pre-dictions using the simple linear model. This research gap comesfrom the lack of integration of decision making literature and fit lit-erature, and from the lack of laboratory studies that focus on deci-sion making process by manipulating PO fit and PJ fit informationin carefully controlled settings.

Second, most research on fit in selection decision making havefailed to consider the characteristics of opening positions as beingimportant boundary conditions, although they may influence theweighting of PO fit and PJ fit in making selection decisions. Positioncharacteristics will be important when hiring decision makersintegrate PO fit and PJ fit information because the impact of POfit and PJ fit on post-hire outcomes may differ across job positions,and hiring decision makers take this into account when evaluatingjob candidates. Therefore, decision makers may not give equalweights to PO fit and PJ fit in hiring for any kind of job position.Rather, they may adjust the weight of PO fit and PJ fit accordingto position characteristics.

To fill these research gaps, we develop theory regarding how POfit and PJ fit information is used when hiring decision makers eval-uate job candidates, and how the process is influenced by positioncharacteristics. We test our theory through two experimental pol-icy-capturing studies in which the levels of PO fit and PJ fit aremanipulated. Decision making literature suggests that the integra-tion of multiple attributes to form an overall evaluation involvesthe combining and the weighting processes (Ganzach, 1997; Hitt& Barr, 1989; Stumpf & London, 1981). We posit that the combina-tion of PO fit and PJ fit information will be influenced largely by acognitive rule that hiring decision makers commonly have, namely,the non-linear information processing. On the other hand, we positthat the weighting of PO fit and PJ fit will be largely influenced byposition characteristics because the importance of PO fit and PJ fitin producing employee outcomes varies across positions. As thecharacteristics of an opening position, we focus on the durationof the employment contract (whether the contract is permanent

or fixed-term) and task elements (whether the position largely in-volves managerial or knowledge-intensive tasks). We choose thesetwo dimensions because, as discussed later, the degree to which POfit and/or PJ fit are critical in post-hire outcomes will be related to(1) how long the new hire is expected to stay in the organization(i.e., contract duration), and (2) the degree to which the tasks aredeeply embedded in the organizational context (i.e., managerialtasks) and/or the tasks require advanced knowledge and expertiseto be successfully performed (i.e., knowledge-intensive tasks).

In what follows, we begin with the theorizing of how hiringdecision makers combine PO fit and PJ fit information by focusingon the nonlinear information processing. Then, we proceed to theweighting process of PO fit and PJ fit information by focusing onthe effect of the two dimensions of position characteristics,namely, contract duration and task elements.

Nonlinear combination of PO fit and PJ fit

In the context of personnel decision making, such as selection orpromotion decisions and performance evaluation, decision makersoften use nonlinear, noncompensatory rules, specifically conjunc-tive rules (Brannick & Brannick, 1989; Ganzach, 1995; Hitt & Barr,1989). In forming an overall evaluation under noncompensatoryrules, low (high) values of some attributes cannot be offset or com-pensated by high (low) values of other attributes. This is in contrastwith compensatory rules, where information is integrated in a linermanner. Conjunctive rules are a particular form of noncompensa-tory rules that entail rejection of any object that fails to meet aminimum criterion on an attribute. This also means that evaluationis based on negative attributes, often associated with negativitybias (Skowronski & Carlston, 1987). Past research has found thatnegative information was weighted more heavily in impressionformation, performance appraisal and interview contexts (Fiske,1980; Hollmann, 1972; Hamilton & Huffman, 1971; London &Hakel, 1974; London & Poplawski, 1976; Motowidlo, 1986), indi-cating the existence of conjunctive rules. Conjunctive rules are saidto be cognitively simpler (i.e., less demanding) for decision makersto implement than linear compensatory rules (Elrod, Johnson, &White, 2004). These rules are also said to be superior to linear ruleswhen the cost of false positive decisions are assumed to be high(Einhorn, 1971; Ogilvie & Schmitt, 1979).

Consistent with past literature on personnel decision making,especially on the evaluation of job candidates (e.g., Ganzach,1995), we propose that the way hiring decision makers combinePO fit and PJ fit information in the evaluation of job candidates in-volves conjunctive rules. Decision making literature suggests thatthe conjunctive information processing involves curvilinearityand interaction. Curvilinearity in this context means that negativeinformation (i.e., a low level of fit) will be paid more attention andweighted more than positive information (i.e., a high level of fit).Interaction means that when evaluating, the effect of one attribute(i.e., PO fit or PJ fit) depends on the value of another attribute(Billings & Marcus, 1983).

Curvilinearity

Because high levels of PO fit and PJ fit are related to desirableemployee outcomes, there will be a positive association betweeneach type of fit and the evaluation of job candidates. However,when the nonlinear conjunctive rule is included, the relationshipcould become curvilinear where a low level of fit is evaluated morenegatively than the liner relationship predicts. This curvilinearityalso implies the existence of thresholds or cutoffs that should beexceeded to receive positive evaluation – an inherent part of con-junctive rules (Billings & Marcus, 1983). We apply the idea of

T. Sekiguchi, V.L. Huber / Organizational Behavior and Human Decision Processes 116 (2011) 203–216 205

pervasive influence of conjunctive cutoffs to this research context(Elrod et al., 2004), meaning that objects that nearly fail the con-junctive criterion (i.e., a low level of fit) are valued more negativelythan objects that easily satisfy it. Thus, we predict that therelationship between each type of fit and the evaluation of job can-didates reflects the combination of the positive linear associationand the pervasive influence of cutoff, which can be approximatedby a curvilinear, concave relationship (Elrod et al., 2004).

Hypothesis 1a. The relationship between the level of PO fit andthe evaluation of job candidates will be positive curvilinear:Candidates with a low level of PO fit are evaluated more negativelythan the liner relationship predicts.

Hypothesis 1b. The relationship between the level of PJ fit and theevaluation of job candidates will be positive curvilinear: Candi-dates with a low level of PJ fit are evaluated more negatively thanthe liner relationship predicts.

Interaction

The use of conjunctive rules by hiring decision makers also indi-cates the interactive combination of PO fit and PJ fit in the evalua-tion of job candidates. That is, when a candidate’s level of PO fit (PJfit) is low, the relationship between PJ fit (PO fit) and the overallevaluation of the job candidate will become weaker, because hiringdecision makers tend to pay more attention to the negative infor-mation (i.e., a low level of one type of fit) and pay less attention tothe other type of fit. The interactive use of PO fit and PJ fit informa-tion also implies that this kind of interaction predicts post-hireoutcomes because the organizational context and jobs embeddedin the organization are usually interdependent and interrelated.For example, a high level of PJ fit will result in more desirable workoutcomes when the level of PO fit is also high, because such a per-son can use his or her knowledge and skills in line with organiza-tional values and goals and can effectively contribute to theorganizational goals.

Hypothesis 2. The evaluation of job candidates will reflect aninteractive combination of PO fit and PJ fit: The higher (lower) onetype of fit is, the stronger (weaker) the relationship is between theother type of fit and the evaluation of the candidate.

High vs. low scatter candidates

Another way to show the existence of nonlinear conjunctive rulesis to compare hiring decision makers’ evaluations between low scat-ter and high scatter candidates (Ganzach, 1993, 1995). The scatter inthis context refers to the standard deviation of the attribute valuesaround the profile means. Suppose that there are two job candidateswho have the same mean value on the level of PO fit and PJ fit, butone has two moderate levels of PO fit and PJ fit (a low scatter candi-date) and the other has a combination of high and low levels of PO fitand PJ fit (a high scatter candidate). Under the linear compensatoryrule where PO fit and PJ fit are weighted equally, the two candidateswill receive the same evaluation. However, if the evaluation followsthe conjunctive rule, the low scatter candidate will receive a higherevaluation than the high scatter candidate. This is so because, underthe conjunctive rules, decision makers will pay more attention to,and place more weights on the low level of one type of fit, and thiscannot be compensated by the high level of the other type of fit.Thus, assuming that PO fit and PJ fit both play an important role inmaking selection decisions, albeit not given exactly the sameweight, we predict the following.

Hypothesis 3. Job candidates who have moderate levels of both POfit and PJ fit will be evaluated more positively than those who havea combination of high and low levels of PO fit and PJ fit.

The effect of position characteristics on the weighting of PO fitand PJ fit

As discussed, the weights given to PO fit and PJ fit when evalu-ating job candidates will be influenced by position characteristicsbecause the impacts of PO fit and PJ fit on post-hire outcomesmay be different across job positions and hiring decision makerswill take this into account in the evaluation of job candidates.

The effect of contract duration

Contract duration of the position (permanent contract vs. fixed-term contract) specifies how long a new hire is expected to staywith the organization. Whereas employees in the permanent con-tract should continue to perform well for a long time, employees inthe fixed-term contract should perform well only for a short time(during their contract duration). Permanent employees are also ex-pected to show growth in their performance during their long-term stay in the organization. We propose that hiring decisionmakers will consider this type of time horizon when evaluatingjob candidates.

It should be noted that the levels of both PO fit and PJ fit couldchange over time (Dawis & Lofquist, 1984). The longer an employ-ee stays with the same organization, the more likely that both theperson and the environment change. However, fit literature sug-gests that PO fit is relatively unchanged while PJ fit is more likelyto change over time (Ostroff, Shin, & Feinberg, 2002). It is becauseindividual and organizational values are stable characteristics,whereas individuals can upgrade their knowledge and skills, andalso business environment and the nature of jobs change overtime.

First, we predict that PO fit will be weighted more heavily for apermanent position than for a fixed-term position. Because PO fit isrelatively stable over time, the positive effect of the high level of POfit such as desirable work attitudes (Verquer et al., 2003), low vol-untary turnover (O’Reilly, Chatman, & Caldwell, 1991) and citizen-ship behaviors (O’Reilly & Chatman, 1986) will continue for a longtime. In addition, because of the high value congruence, a personwith a high level of PO fit will be highly motivated to align withorganizational values and goals. Thus, a high level of PO fit is alsorelated to long-term organizational commitment. The negative ef-fect of a low level of PO fit will also continue over time, and there-fore should be avoided, especially for permanent positions.Although organizational socialization may improve the level ofPO fit to some degree (Cable & Parsons, 2001; Chatman, 1991), alow level of PO fit at the time of organizational entry cannot be re-solved easily. Thus, a high level of PO fit is desirable especially forpermanent positions.

For a fixed-term position, the advantage of a high level of PO fitwill be less salient. For example, although a high-level of PO fitwould increase organizational commitment, organizations maynot even expect their fixed-term contract employees to makelong-term commitments to them (Hulin & Glomb, 1999). Otheradvantages of high level of PO fit, such as employee retention,are not very important for those who will leave the organizationafter their short-term fixed contracts end. Thus, although a high le-vel of PO fit is desirable for fixed-term positions as well, it is rela-tively less important than for permanent positions.

Next, we predict that PJ fit will be weighted more heavily for afixed-term position than for a permanent position. Fixed-termcontract employees should show immediate high performance.


Because job candidates with a high level of PJ fit have the appropri-ate knowledge and skills for the opening position, their posting topositions will most likely result in higher job performance, at leastin the short run. Although the level of PJ fit can likely be improved,a low level of PJ fit at the time of hiring cannot be resolved quickly,and should be avoided especially for fixed-term positions. On theother hand, for permanent contract employees, PJ fit at the timeof hiring does not guarantee long-term success, because the levelof PJ fit may change when the business environment changes. Inaddition, the level of PJ fit could be improved if time permits. Forexample, an organization can provide the new hire with trainingopportunities by which he or she can acquire the necessary knowl-edge and skills, especially if they are firm-specific (Becker, 1964).An employee’s level of PJ fit can also be improved by changingjob assignments within the organization, assuming that the personwill stay with the organization for a long time. Therefore, althougha high level of PJ fit is desirable for permanent positions as well, itis relatively less important than for fixed-term positions.

In summary, a high level of PO fit benefits most the post-hireoutcome of a person who stays with the organization for a longtime. A high level of PJ fit at the time of hiring benefits most imme-diate post-hire outcomes without guaranteeing the long-term suc-cess of the candidate. Hiring decision makers will take this intoaccount when evaluating job candidates. Thus, we predict thefollowing.

Hypothesis 4. PO fit will be weighted more heavily for a perma-nent position than for a fixed-term position. Alternatively, PJ fitwill be weighted more heavily for a fixed-term position than for apermanent position.

The effect of task elements

As discussed in the introduction, we focus on the differences be-tween managerial tasks and knowledge-intensive tasks. Manage-rial tasks include planning, organizing, commanding,coordinating, and controlling in order to attain organizational goals(Fayol, 1916). The person who performs managerial tasks is ex-pected to play interpersonal, informational, and decisional roleswithin the organization (Mintzberg, 1973), and spend most of theirtime interacting with others (Kotter, 1982). Simply put, managerialtasks are about getting things done through other organizationalmembers (DeBrin, 1994). Knowledge-intensive tasks are con-ducted by professional or technical workers such as lawyers, scien-tists, and engineers who use advanced knowledge and expertiseacquired through formal education (Davenport & Prusak, 1998;Drucker, 1994). Knowledge-intensive tasks are a means by whichorganizations attain technical rationality and produce desired out-comes efficiently (Thompson, 1967).

First, we predict that PO fit will be weighted more heavily for amanagerial position than for a knowledge-intensive position. Ingeneral, managerial tasks are embedded deeply in the organiza-tional context (e.g., Zajac, Golden, & Shortell, 1991). To succeed,individuals who perform managerial tasks need to understandtheir organization’s values, goals, and strategies, and execute theirmanagerial tasks accordingly. In addition, effectively performedmanagerial tasks require knowledge and skills about the firm’s un-ique routines, processes and documentation embedded in theorganizational context. Individuals with a high level of PO fit wouldbe comfortable with the organization’s values and goals, and wouldexhibit attitudes and behaviors consistent with the organization’svalues and course of action. These individuals are also likely to ob-tain contextual knowledge of the organization quickly. Moreover, aperson with a high level of PO fit will likely share similar valuesand personality characteristics with other members of the organi-

zation, which develops trust with the organization’s members andincreases the ease at which these members are managed (e.g., By-rne, 1971). On the other hand, knowledge-intensive tasks deal withtechnical issues that are generally less influenced by organizationalculture and norms. Related to this point, knowledge workers areoften given control and autonomy in order to focus on technical is-sues without being heavily constrained by the organizational con-text (Derber & Schwartz, 1991; Wallace, 1995). Thus, a high level ofPO fit is desirable but less influential on post-hire outcomes forknowledge-intensive tasks.

Next, we predict that PJ fit will be weighted more heavily for aknowledge-intensive position than for a managerial position. Asthe position largely involves knowledge-intensive tasks, a high le-vel of PJ fit increases instrumentality and efficiency (e.g., Thomp-son, 1967). That is, advanced knowledge and skills in addition toexperience possessed by the person would be the major determi-nants of knowledge-based task performance. On the other hand,for a job position largely made up of managerial tasks, a high levelof PJ fit alone may not be sufficient to influence the managerialoutcomes, such as group or unit performance, that often dependon organizational context (e.g., compatibility with group mem-bers). Usually, job candidates cannot have knowledge of an organi-zational context before they have worked in the organization.Therefore, for managerial tasks a high level of PJ fit is necessarybut is relatively less influential on post-hire outcomes than forknowledge-intensive tasks.

In summary, a high level of PO fit has a stronger impact on post-hire outcomes when organizational context is critical in perform-ing the job, whereas a high level of PJ fit has a stronger impacton post-hire outcomes when advanced knowledge and expertiseare critical for performing the job. Hiring decision makers will takethis into account when evaluating job candidates. Thus, we predictthe following.

Hypothesis 5. PO fit will be weighted more heavily when theposition largely involves managerial tasks rather than knowledge-intensive tasks. Alternatively, PJ fit will be weighted more heavilywhen the position largely involves knowledge-intensive tasksrather than managerial tasks.

Interaction of contract duration and task elements

Because job information contains both contract duration andtask elements, hiring decision makers consider these two dimen-sions simultaneously when evaluating job candidates. Thesedimensions could interact with each other in influencing theweights of PO fit and PJ fit placed by hiring decision makers. Spe-cifically, we predict that the weights of PO fit and PJ fit will beinfluenced more by the contract duration when the position largelyinvolves managerial tasks rather than knowledge-intensive tasks.

As already discussed, the contract duration is related to the timehorizon of post-hire outcomes, and task elements are related towhether organizational context or advanced knowledge and exper-tise are critical for post-hire outcomes. For managerial tasks, theadvantage of a high level of PO fit is critical particularly from thelong-term perspective, because it may take a longer time for individ-uals to obtain knowledge on the organizational context and developtrust with other organizational members for managerial effective-ness. On the other hand, a high level of PO fit may not contributeto the immediate or short-term managerial outcomes even for man-agerial tasks. Instead, an excellent level of managerial skills (i.e., ahigh level of PJ fit) would compensate for a candidate’s lack of orga-nizational knowledge in producing immediate managerial success.Thus, a high level of PJ fit would have the strongest impact for imme-diate managerial performance. The importance of PJ fit at the time of


hiring may be less salient from the long-term perspective becausethe level of PJ fit could be improved after individuals are hired byupgrading and developing organization-specific managerial skills(e.g., managing particular types of people) through experience andtraining within the organization. These predictions are consistentwith the basic arguments in Hypothesis 4, that is PO fit most benefitsthe post-hire outcome of a person who stays in the organization for along time, and PJ fit most benefits immediate post-hire outcomes.

For knowledge-intensive tasks, on the other hand, a high levelof PJ fit will be the most essential determinant of post-hire out-comes regardless of the contract duration. This is because forknowledge-intensive positions the level of PJ fit may relatively re-main unchanged over time as professionals and knowledge work-ers usually stay in the same area of expertise (e.g., intellectualproperty law; mechanical engineering). Also, their advancedknowledge and expertise, the base of their PJ fit, are usually ob-tained through relatively long formal education (e.g., JD, MD, orPh.D. programs) before they join any organization. Although a highlevel of PO fit in general would be beneficial from the long-termperspective, it is less salient for knowledge intensive tasks, whichare relatively less influenced by the organizational culture andnorms. These arguments suggest that the weights of PO fit and PJfit will be less influenced by the difference in the contract durationwhen the position largely involves knowledge-intensive tasks.

The above arguments also suggest that the effect of the differ-ence between managerial and knowledge-intensive task elementson the weights of PO fit and PJ fit, as predicted in Hypothesis 5, willbe stronger for permanent-contract positions than for fixed-termpositions. For fixed-term positions, PJ fit will be the most essentialfor immediate post-hire outcomes for both managerial and knowl-edge-intensive tasks. Thus, we predict the following.

Hypothesis 6a. The degree to which PO fit is weighted moreheavily for a permanent rather than fixed-term position and PJ fit isweighted more heavily for a fixed-term rather than permanentposition will be stronger when the position largely involvesmanagerial tasks rather than knowledge-intensive tasks.

Hypothesis 6b. The degree to which PO fit is weighted more heav-ily for a managerial rather than a knowledge-intensive positionand PJ fit is weighted more heavily for a knowledge-intensiverather than a managerial position will be stronger when the posi-tion is for a permanent rather than for a fixed-term contract.

Study 1

Method

ParticipantsParticipants of this study included 120 mid- to senior-level

executives who were enrolled in executive education programsin a large public university in the northwest region of the UnitedStates. During the study period, approximately 210 potential par-ticipants were asked to participate in this study and were givenresearch material. About 57% of them voluntarily participatedin the study. Data from five participants were excluded fromthe sample because they failed to answer manipulation checkquestions correctly, or because they did not complete the entirestudy. Given these constraints, the final sample consisted of115 individuals.

Males accounted for 58% of participants. Participants’ medianage was 31–40, with 84% currently holding full-time positions.Median full-time work experience was 11–15 years. Of thosewho held full-time positions, 66% held managerial jobs. A total of80% of the participants reported direct experience in corporate

hiring activities, with 80% of those individuals involved in the hir-ing process on a yearly or quarterly basis and 75% had been in-volved in hiring decisions within one year preceding the study.

The industrial background of the participants varied: 19% were incommunication/computers, 13% were each in construction/manu-facturing and government/education/nonprofit, 10% were in fi-nance/banking/insurance, 7% were in transportation/distribution,6% were in professional services, 5% were each in biotechnology/pharmaceuticals and healthcare, and 3% were each in retail and en-ergy/utility/natural resources. Their functional areas were as fol-lows: 21% each in operations and general management, 12% inresearch and development, 9% in marketing, 6% in sales, 5% each instrategy and finance, 4% in accounting, and 2% in human resources.

DesignWe used an experimental policy-capturing design that included

both within-subject and between-subject manipulations. As with-in-subject manipulations, the levels of job candidate PO fit and PJfit were manipulated at three levels (low, medium, and high). Par-ticipants were given all possible combinations of PO fit and PJ fitlevels (i.e., 3 � 3 = 9). One replication was added to assess judg-ment reliability. We also employed an orthogonal 2 � 2 between-subject factorial design (contract duration and task elements).The contract duration was manipulated as a permanent contractor a six-month contract. Task elements were manipulated as towhether the position was for a manager (a job that largely involvesmanagerial tasks) or an in-house attorney (a job that largely in-volves knowledge-intensive tasks). Participants were randomlyassigned to one of the four between-subject conditions.

ProceduresParticipants were told that this study was being conducted to

determine how executives make human resources decisions.Through experimental material, participants were informed thatthey would play the role of a hiring decision maker in a softwarecompany and would evaluate the credentials of ten job candidates.The scenario in the material indicated that the previous manager(or in-house attorney) had resigned unexpectedly, and it was neces-sary to hire another manager (or in-house attorney) to fill a perma-nent position (or interim or fixed-term position). A job descriptionfor the position of interest followed. Position descriptions weredrawn from the Department of Labor’s O⁄Net occupational data-base. The position descriptions (manager vs. in-house attorney) dif-fered in terms of the job titles, job requirements, key functions, andcontract type (permanent contract or six-month contract).

In terms of job requirements, the candidate for the managementposition was required to have an MBA degree, management expe-rience at the division level, direct experience in the software indus-try, and knowledge of financial, marketing, and human resourcesmanagement. The manager’s role description stated the following:

Under the supervision of the VP of strategic business develop-ment, formulates business strategies and provides overall direc-tion of the manufacturing software division. Plans, directs, andcoordinates the division’s daily operational activities. Managesand supervises employees in the division.

The attorney position required a JD degree, experience in prac-ticing general corporate law, and experience in commercial trans-actions and intellectual property law. The attorney’s roledescription stated the following:

Under the direction of the VP of strategic business development,advises the company concerning legal issues related to its busi-ness activities. These issues include patents, government regu-lations contracts with other companies, and propertyinterests. No supervisory responsibility is required.


Participants were also informed that the human resourcesdepartment of the company had selected ten job finalists. Theirjob as the hiring decision maker was to assess the qualificationsof the ten finalists to determine who would be offered a job. Thisinformation was followed by the profiles of the candidates, whichsummarized each individual’s PO fit and PJ fit information. To limitthe result of the ordering effect, the order in which participantsevaluated the ten profiles was random. In addition, the order ofthe fit information within the profiles was varied across partici-pants such that profiles for half of the participants presented POfit followed by PJ fit, and the other half of the participants receivedprofiles that contained this information in the reverse order.

To ensure their understanding of the scenario and of their rolein the hiring simulation, and to ensure the manipulation of the po-sition’s contract duration and task elements, participants answeredseveral manipulation check questions (e.g., ‘‘The position you arefilling is ____. [Options provided were (a) managerial, (b) profes-sional, and (c) low skilled],’’ and ‘‘The contract type of this positionis ____. [Options provided were (a) permanent, (b) temporary, and(c) not specified]’’). After answering these questions, participantswere asked to evaluate the qualifications of the ten job finalistsby answering four questions. After completing the evaluation ofall the hypothetical finalists, participants were asked to respondto several personal demographic questions.

Manipulation of fit informationTo determine the differential impact of candidate PO fit and PJ

fit, the level of each type of fit was manipulated in job candidateprofiles. PO fit and PJ fit cues were developed through a reviewof the literature on PO fit and PJ fit, in addition to discussions withhuman resource experts working in various firms as well as man-agement researchers. The literature on PO fit and PJ fit suggeststhat decision makers often use candidates’ KSAs to assess PJ fit,and candidates’ values, goals, and personality traits to assess POfit (e.g., Kristof-Brown, 2000). In light of this, six to eight itemsfor PO fit and PJ fit cues were selected from the domain of possibleKSAs and values, goals, and personality traits. Based on the discus-sions with experts and researchers, the pool of items was narroweddown to three items for each type of the fit. The items for the PJ fitcue included the following: (1) Match between the candidate’s aca-demic degree and the job requirements; (2) Match between thecandidate’s work experience and the job requirements; and (3)Match between the candidate’s knowledge and skills and the jobrequirements. The items for the PO fit cue included the following:(1) Similarity between the candidate’s personal values and the cor-porate culture; (2) Similarities between the candidate’s personalgoals and the corporate goals; and (3) Similarities between thecandidate’s personality and those of the corporation’s typicalemployees. For presenting PO fit and PJ fit information, verbalstatements were combined with a simple graphical representationof fit to increase the effectiveness of the PO fit and PJ fit manipula-tions. A sample of the verbal and graphic representations used inthis study is presented in Appendix 1.

To create an orthogonal cue design, the three levels of PO fit andPJ fit cues (1 = low, 2 = medium, 3 = high) were completely crossed,resulting in nine (i.e., 3 � 3) different candidate profiles for eachbetween-subject condition. An orthogonal cue design was appro-priate to assess the independent effects of each cue (e.g., Aiman-Smith, Scullen, & Barr, 2002). One replicated profile was includedto assess judgment reliability, bringing the total number of profilesto ten.

Two pilot studies were conducted to check whether the manip-ulated fit cue levels generated desirable perceptions of low, med-ium, and high levels of PO fit and PJ fit, and whether the numberof profiles (i.e., nine profiles) used in this study was sufficient tocapture consistent individual judgment policies. In the first pilot

study, ten undergraduate students from a northwest businessschool rated their perceptions of candidates’ PO fit and PJ fit basedon the candidate profiles. Each student rated all ten profiles, result-ing in a total of 100 ratings. T-tests of the mean level of perceivedfit demonstrated significant differences among the low, medium,and high levels of each fit cue. In addition, there were no statisti-cally significant differences between the two low cues, the twomedium cues, or the two high cues of PO fit and PJ fit. This demon-strated that PO fit and PJ fit were manipulated with equivalentstrength. In the second pilot study, 40 undergraduate studentsrated the qualifications of the ten candidate profiles, and a multiplecorrelation between the evaluation of the candidates and PO fit andPJ fit was calculated for each individual. Results revealed that theaverage of multiple correlations without replication profiles was.91, which indicated that the nine profiles could provide stableand consistent regression results and thus were adequate to cap-ture judgment policies (Cooksey, 1996).

Evaluation of job candidatesEvaluation of job candidates was measured with four 7-point Lik-

ert-type scales. Participants were asked to indicate the following: (1)‘‘My overall evaluation of this applicant is:’’ (1 = very negative;7 = very positive); (2) ‘‘I want to hire this candidate.’’ (1 = not atall; 7 = absolutely); (3) ‘‘I will offer a job to this candidate.’’ (1 = verylow; 7 = very high); and (4) ‘‘I think this candidate will succeed in thejob.’’ (1 = very unlikely; 7 = very likely). Factor analysis with a prin-cipal component solution for these four items identified a single fac-tor that explained 92.9% of the variance. The internal consistency interms of alpha reliability estimate was .97. Therefore, a compositemeasure of the evaluation of the job candidates was created by aver-aging the four items (M = 3.61, SD = 1.60). This composite measurewas used in subsequent analyses.

Results

A summary of means, standard deviations, and a correlationmatrix of variables used in this study is presented in Table 1. Noindividual difference variables were significantly correlated withthe evaluation of the job candidates. The overall reliability of judg-ment across participants was assessed using replication profiles.The correlation between the original candidate profiles and thereplicated ones was .85. Considering that other published policy-capturing studies have reported judgment reliabilities of around.80 (e.g., Karren & Barringer, 2002), the reliability obtained in thisstudy was considered adequate. Data for the replication profileswere eliminated from original data for subsequent hypothesestesting.

To test our hypotheses, we used hierarchical linear modeling(HLM), which allows for simultaneous analysis of both within-and between-subject variances (Bryk & Raudenbush, 1992). Weemployed HLM version 6.0 with restricted maximum likelihoodestimation. Level 1 analysis of HLM deals with the within-subjectvariance and was used to test Hypotheses 1, 2 and 3. Level 2 anal-ysis of HLM deals with between-subject variance of the slope coef-ficients for PO fit and PJ fit obtained in Level 1 analysis, and wasused to test Hypotheses 4, 5 and 6. Centering of predictors in Level1 analysis was not done because the levels of PO fit and PJ fit werecontrolled and the same across participants (e.g., Kristof-Brown,Jansen, & Colbert, 2002).

Level 1 analysisIn Model 1 of Level 1 (within-subject) analysis, the evaluation of

job candidates was regressed onto PO fit and PJ fit for each partic-ipant. The slope coefficients for PO fit and PJ fit represent theweights given to each type of fit by participants in evaluating jobcandidates. In Model 2 of Level 1 analysis, squared terms of PO

Table 1Means, standard deviations and correlations among variables in study 1.

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11

1 Evaluation of job candidates 3.61 1.60 –2 PO fit 2.00 .82 .61** –3 PJ fit 2.00 .82 .57** .00 –4 Contract duration .50 .50 .01 .00 .00 –5 Task elements .49 .50 .03 .00 .00 �.01 �6 Age 3.50 1.63 �.05 .00 .00 .11 .11 –7 Gender .58 .50 .03 .00 .00 .06 �.10 .10 –8 Work experience a 4.03 1.64 �.06 .00 .00 .10 .08 .88** .09 –9 Managerial experience a 2.56 1.45 �.01 .00 .00 �.02 �.09 .69** .10 .79** –10 Frequency of hiring a 2.91 1.55 �.04 .00 .00 �.07 �.00 .30** .16 .32** .28** –11 Recency of hiring a 3.29 1.76 .09 .00 .00 �.05 �.10 �.34** �.08 �.42** �.44** �.53** –

Notes: N = 1035 for correlations including variable 1–3 (within-subject variables). N = 115 for correlations among variable 4–11 (between-subject variables).a Age, work experience, managerial experience, frequency of hiring, and recency of hiring are quasi-interval scales. A higher score in frequency of hiring means participants

were involved in hiring activities more frequently. A lower score in recency of hiring means they participated in hiring decisions more recently.** p < .01.

Table 2Results of hierarchical linear modeling of Level 1 (within-subject) analysis in study 1.

Variable Model 1 Model 2 Model 3

Coefficient SE t Coefficient SE t Coefficient SE t

Intercept, b0 �1.01** .11 �9.59 �1.71** .23 �7.27 �.65** .22 �2.97PO fit, b1 1.19** .04 29.25 1.31** .16 8.27 .78** .16 4.79PJ fit, b2 1.12** .04 25.50 1.84** .17 10.68 1.31** .17 7.66PO fit squared, b3 �.03 .04 �.70 �.03 .04 �.68PJ fit squared, b4 �.18** .04 �4.11 �.18** .04 �4.08PO fit � PJ fit, b5 .26** .04 6.84Effect size (%)a 80.92 81.68 86.72

Note: N = 115.a Percentage of explainable Level 1 variance in the dependent variable accounted for by fit cues.

** p < .01.

1

2

3

4

5

6

7

Low PJ fit Mid PJ fit High PJ fit

Eva

luat

ion

of c

andi

date

High PO fit

Mid PO fit

Low PO fit

Fig. 1. Levels of PO fit and PJ fit and evaluation of job candidates in study 1.


fit and PJ fit were added to the equation to test the curvilinear rela-tionships predicted in Hypotheses 1a and 1b. In Model 3 of Level 1analysis, the interaction term of PO fit and PJ fit was further addedto test Hypothesis 2. For each model, the effect size value for theLevel 1 analysis, which is equivalent to R2 in a multiple regression,was calculated as the proportion of the variance explained by theLevel 1 predictors (Kristof-Brown, Jansen et al., 2002). Table 2 en-lists the average intercepts and slopes across participants for Level1 analysis. In Model 1, PO fit and PJ fit averaged across participantswere both significant and accounted for 80.92% of the explainablevariance in the evaluation of the job candidates. Next, as shown inModel 2 in Table 2, the square term of PJ fit was significant(t = �4.11, p < .01) in Model 2, while that of PO fit was not, suggest-ing that there was a curvilinear relationship only for the levels of PJfit and the evaluation of the job candidates. The effect size in-creased .92% from Model 1, indicating the small incremental valid-ity of the curvilinear component. Next, as shown in Table 2, theinteraction term of PO fit and PJ fit was significant (t = 6.84,p < .01) in Model 3 and the effect size increased 6.18% from Model2, indicating the small to moderate incremental validity of theinteraction component. Fig. 1 illustrates the interaction effect ofPO fit and PJ fit in addition to the curvilinear relationship betweenPJ fit and the evaluation of the job candidates. As shown, the graphis fan-shaped, suggesting that the relationship between PJ fit andthe evaluation of the job candidates is stronger (weaker) whenthe level of PO fit is high (low), and the curved line suggests thatthe effect of PJ fit on the evaluation of the job candidates is strongerfor low to moderate levels of PJ fit than for moderate to strong lev-els of PJ fit. Taken together, our data provides support for Hypoth-eses 1b and 2, while Hypothesis 1a was not supported. In addition,Fig. 1 and paired sample t-tests suggest that, as predicted in

Hypothesis 3, the candidate who had both moderate levels of POfit and PJ fit was evaluated more positively than candidates whohad a high level of PO fit and a low level of PJ fit (t = 4.91,p < .01) and a high level of PJ fit and a low level of PO fit(t = 5.23, p < .01). Cohen’s d was calculated as the effect size foreach of the mean differences. The effect sizes were .59 for the for-mer case and .64 for the latter case, indicating the effect sizes wereboth medium (Cohen, 1988). Thus, Hypothesis 3 was supported.

Next, we assessed whether there was a systematic varianceacross the PO fit and PJ fit slopes of Model 1 of Level 1 analysis.The variation in the PO fit and PJ fit slopes in Level 1 analysis

Table 3Results of hierarchical linear modeling of Level 2 (between-subject) analysis in study 1.

Variable Model 1 Model 2

Coefficient SE t Coefficient SE t

PO fit, b1

Intercept, c10 1.06** .06 17.38 1.14** .07 16.97Contract duration, c11 .19** .07 2.72 .05 .10 .54Task elements, c12 .06 .07 .83 �.08 .10 �.83Contract duration � task elements, c13 .28* .14 2.05Effect size (%)a 3.14 7.40

PJ fit, b2

Intercept, c20 1.23** .06 19.61 1.16** .07 16.64Contract duration, c21 �.19** .07 �2.71 �.06 .10 �.58Task elements, c22 �.03 .07 �.47 .11 .10 1.07Contract duration � task elements, c23 �.28* .14 �2.00Effect size (%)a 7.51 9.34

Note: N = 115.a Percentage of explainable Level 2 variance in the dependent variable accounted for by Level-2 variables.

* p < .05.** p < .01.


was significant (v2 = 268.42, p < .01 and v2 = 310.76, p < .01,respectively), justifying a follow-up Level 2 analysis to test remain-ing hypotheses.

Level 2 analysisLevel 2 analysis was performed to test the main effects of the

contract duration and task elements on the weights that hiringdecision makers gave to PO fit and PJ fit, as delineated in Hypoth-eses 4 and 5, and to test the interaction effect between the contractduration and task elements that was represented in Hypothesis 6.In Model 1 of Level 2 analysis, slope coefficients of PO fit and PJ fitin Model 1 of the Level 1 equation were regressed onto the contractduration and task elements. Next, in Model 2, the interaction be-tween the contract duration and task elements was added. Wepresent the results from these analyses with effect size measuresin Table 3.

We predicted that PO fit would be weighted more heavily for apermanent position than for a fixed-term position, and PJ fit wouldbe weighted more heavily for a fixed-term position than for a per-manent position (Hypothesis 4). As shown in Table 3, the contractduration significantly explained the variance of the slopes for bothPO fit and PJ fit (t = 2.72, p < .01; t = �2.71, p < .01, respectively).Since the contract duration was coded as (1 = permanent;0 = fixed-term), the positive sign of the regression coefficient onthe contract duration for PO fit slope means that the weight ofPO fit was larger when the position was based on a permanent con-tract. Similarly, the negative sign of the regression coefficient onthe contract duration for PJ fit slope means that the weight of PJfit was smaller when the position was based on a permanent con-tract. Therefore, these results support Hypothesis 4.

Next, we predicted that PO fit would be weighted more heavilywhen the position largely involves managerial tasks rather thanknowledge-intensive tasks, and PJ fit would be weighted moreheavily when the position largely involves knowledge-intensivetasks rather than managerial tasks (Hypothesis 5). As shown in Ta-ble 3, task elements did not explain the variance of the slopes forboth PO fit and PJ fit. Therefore, Hypothesis 5 was not supported.The effect sizes for Model 1 were 3.14% for PO fit slope and7.51% for PJ fit slope, indicating that position characteristics ac-counted for small variances of PO fit and PJ fit slopes.

Finally, we predicted that the contract duration and task ele-ments would interact to influence the weights of PO fit and PJ fit(Hypothesis 6a and 6b). As shown in Table 3, the interactions be-tween the contract duration and task elements were significantand explained the variance of the slopes for both PO fit and PJ fit

(t = 2.05, p < .05; t = �2.00, p < .05, respectively). The effect sizemeasures for Model 2 of Level 2 analysis increased to 7.40% forPO fit slope and 9.34% for PJ fit slope, increases of 4.26% and1.83%, respectively, from Model 1 of Level 2 analysis (i.e., withoutinteraction terms), indicating the small incremental validities ofinteraction terms.

Fig. 2 illustrates the interaction effect of the contract durationand task elements on the weight given to PO fit and PJ fit. To fur-ther examine the nature of the interaction, we tested the differ-ences in average slope coefficients for PO fit and PJ fit betweenthe permanent and fixed-term contract conditions for both taskelement conditions. We found that, when the position largely in-volved managerial tasks, average slopes for both PO fit and PJ fitwere significantly different between permanent and fixed-termcontract conditions (t = 2.62, p < .05 for PO fit, and t = 2.69, p < .01for PJ fit). Effect sizes measured as Cohen’s d were .71 for PO fitand .73 for PJ fit, which were considered as medium to largeaccording to Cohen (1988). However, average slopes for both POfit and PJ fit were not significant when the position containedmostly knowledge-intensive tasks. This result shows that the con-tract duration was more likely to influence the weights of PO fitand PJ fit when positions were managerial but the weights of POfit and PJ fit were insensitive to the contract duration when posi-tions were knowledge-intensive. Thus, Hypothesis 6a wassupported.

We also tested the differences between the average slope coef-ficients for PO fit and PJ fit between the managerial task conditionand knowledge-intensive task condition for both of the contractduration. The results of the t-tests show that the average slopefor PJ fit on the knowledge-intensive task condition was signifi-cantly higher than that on the managerial task condition(t = 1.81, p < .05, one-tailed) for a permanent-contract position.The effect size measured as Cohen’s d was .48, which was consid-ered as medium. No significant differences were found for PJ fitslopes for the fixed-term contract condition and for PO fit slopesfor both permanent and fixed-term contract conditions. These re-sults indicate that when the position is permanent, the weight ofPJ fit was significantly higher for knowledge-intensive rather thanmanagerial tasks. Thus, Hypothesis 6b was partially supported.

Finally, to examine whether any individual difference variableinfluenced the weights decision makers gave to PO fit and PJ fit,individual difference variables shown in the descriptive statisticsin Table 1 were entered as predictor variables in the Level 2 equa-tion. None of these variables significantly explained the variancesof the slopes for PO fit and PJ fit.

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

Permanent contract Fixed-term contract

PO f

it sl

ope

Managerial tasks Knowledge-intensitve tasks

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

Permanent contract Fixed-term contract

PJ f

it sl

ope

Managerial tasks Knowledge-intensitve tasks

Fig. 2. Plots of interaction between contract duration and task elements on theweights of PO fit and PJ fit in study 1.


Discussion

With regards to the combining process of PO fit and PJ fit infor-mation (Hypotheses 1 to 3), results of this study generally supportthe existence of the nonlinear combination of PO fit and PJ fit whenhiring decision makers evaluate job candidates. However, Hypoth-esis 1 was only supported for PJ fit, suggesting that the curvilinearrelationship exists for PJ fit but not for PO fit. These results indicatethat, although the combination of PO fit and PJ fit information in-cludes the conjunctive rule that is noncompensatory, hiring deci-sion makers pay more attention to a low level of PJ fit ratherthan that of PO fit to form lower/negative evaluation of a candi-date. In other words, the threshold or pervasive cutoff might existfor PJ fit but not for PO fit.

With regards to the weighting process of PO fit and PJ fit infor-mation (Hypotheses 4 to 6), results support our prediction that theduration of contract influences the weight of PO fit and PJ fit. Inaddition, significant interaction between contract duration andtask elements supports our prediction that contract duration hasa substantial effect when the position involves managerial ratherthan knowledge-intensive tasks. However, whereas the weight ofPJ fit was affected by the differences in task elements for a perma-nent position, the weight of PO fit was not. This result suggeststhat, contrary to our prediction, the weight of PO fit is less sensitiveto the differences in task elements.

A potential limitation of the present study is that only an in-house attorney position was examined as a knowledge-intensive

position. In addition, manipulation of task elements in this study(i.e., managerial vs. in-house attorney positions) implied examiningtwo, rather than one, boundary conditions at the same time. That is,the comparisons between managerial vs. non-managerial positionsand between high vs. low knowledge-intensive positions. Therefore,although we found that the weight of PJ fit was significantly higherfor the permanent in-house attorney position than for the perma-nent managerial position, it is not clear whether this result reflectsthe difference in knowledge-intensiveness of the tasks or the differ-ence between managerial and non-managerial tasks. Furthermore,managerial positions and legal positions belong to different occupa-tional families and usually require different academic background(e.g., MBA vs. law degrees) and study participants in the executiveeducation programs might have been more familiar with and hadmore experience in managerial tasks than legal tasks. Therefore, par-ticipants’ views toward the two positions could be somewhatbiased, which might have affected the results of the study.

Study 2

Study 2 was designed to address the limitations of Study 1regarding the manipulation of task elements, and to replicate thecombining process of PO fit and PJ fit, namely Hypotheses 1 to 3.With the same research design as Study 1, the present study exam-ined a different type of occupation: nurse jobs in the health careindustry. In order to focus on the effect of knowledge-intensive-ness in weighing fit information, we compared a registered nurseposition that requires advanced knowledge and expertise (i.e., ahigh degree of knowledge-intensiveness) to a certified nurse assis-tant position that requires less knowledge and expertise (i.e., a lowdegree of knowledge-intensiveness). This study improved Study1’smanipulation in two ways. First, the present manipulation elimi-nated the comparison between managerial vs. non-managerialpositions. The primary responsibility of the registered nurse de-scribed in the research material was not to manage the entire unitor division but to provide high quality services to patients. Thus,the degree of knowledge-intensiveness within the same field wasconsidered the major determinant that differentiates between aregistered nurse and a certified nurse assistant. Second, by usingtwo positions from the same occupational family as opposed tousing two different occupations as in Study 1, the present manip-ulation also reduced the possibility that biases in viewing differentoccupations confound the degree of knowledge-intensiveness thatinfluences the weights of PO fit and PJ fit.

On the basis of the results for Hypotheses 1 to 3 and discussionfrom Study 1, we examine whether the same results will occur inthis study. Results from Study 1 suggest that there might be a cur-vilinear relationship with the evaluation of job candidates only forPJ fit (Hypothesis 1b), not for PO fit. That is, decision makers mightpay more attention to a low level of PJ fit than that of PO fit to formlower evaluation of job candidates.

Next, consistent with the previous argument that a high level ofPJ fit would be beneficial for post-hire outcomes, especially whenthe position’s task elements require advanced knowledge andexpertise, we predict that PJ fit will be weighted more heavilywhen the position is for a registered nurse rather than for a certi-fied nurse assistant. On the other hand, we predict that the weightof PO fit will not be affected by the difference in task elements,namely, the degree of knowledge-intensiveness.

Method

ParticipantsParticipants were 92 mid- to senior-level executives who were

enrolled in executive education programs in the same university in


Study 1. Approximately 106 potential participants were given re-search material and about 88% of them voluntarily participatedin the study. Data from one participant was excluded because ofincomplete responses. Males accounted for 60% of participants.Participants’ median age was 26–30, with 71% currently holdingfull-time positions. Median full-time work experience was 6–10 years. Of those who held full-time positions, 83% held manage-rial jobs. A total of 75% of the participants reported direct experi-ence in corporate hiring activities, with 63% of those individualsinvolved in the hiring process on a yearly or quarterly basis and88% had been involved in hiring decisions within three years pre-ceding the study.

Participants had diverse industrial background: 24% were incommunication/computers, 13% were in construction/manufactur-ing, 8% were each in finance/banking/insurance and in professionalservices, 6% were biotechnology/pharmaceuticals, 5% were in re-tail, 4% were in transportation/distribution, and 3% were each inhealth care and energy. Their functional areas were as follows:15% each in marketing and research and development, 11% in oper-ations, 10% in general management, 7% in human resources, 5% insales, 4% in finance, 3% in strategy, and 2% in accounting.

Design and procedureWe used almost the same procedure and scenarios as Study 1

except that we examined two nursing jobs (i.e., registered nurseand certified nurse assistant) in the between-subject manipulation.Participants were informed that they would play the role of a hir-ing decision maker in a children’s hospital and would evaluate thecredentials of ten job candidates. The scenario in the material indi-cated that in the hospital would hire a registered nurse (or certifiednurse assistant). Job requirements for the registered nurse positionincluded registered nurse license, at least two years of professionalnursing experience and excellent interpersonal and teamworkskills. The registered nurse’s role description included the follow-ing statement:

Provides professional nursing care for ill, injured, convalescentand handicapped children in hospital. Provides leadership forspecific program areas and unit functions such as developingeducation programs, acting as clinical resource for staff, andcoordinating daily operations. Assists the Director of Nursingto develop, maintain, and evaluate the ongoing operations ofclinical area.

Job requirements for nurse assistant position included certifica-tion as a nurse assistant, at least 6 months experience, ability to readand write, and ability to lift and move patients. The certified nurseassistant’s role description included the following statement:

Table 4Means, standard deviations and correlations among variables in Study 2.

Variable Mean SD 1 2

1 Evaluation of job candidates 3.71 1.60 –2 PO fit 1.99 .82 .58** –3 PJ fit 2.00 .82 .58** �.024 Knowledge-intensiveness .41 .49 �.02 �.015 Age 2.75 1.51 �.01 .006 Gender .60 .49 .04 .007 Work experience a 3.42 1.49 �.03 .008 Managerial experience a 1.97 1.02 .00 .019 Frequency of hiring a .74 .86 .03 .0310 Recency of hiring a 3.73 1.88 .02 �.02

Notes: N = 975 for correlations including variable 1–3 (within-subject variables).N = 92 for correlations among variable 4–11 (between-subject variables).⁄ p < .01.

a Age, work experience, managerial experience, frequency of hiring, and recency of hiriwere involved in hiring activities more frequently. A lower score in recency of hiring m* p < .05.** p < .01.

Provides varied medical support services. Bathes patients asneeded. Cleans and shaves patients. Measures and recordsintake and output of liquids. Takes and records temperatures,pulse and respiration rate. Carries meal trays to patients. Fre-quently lifts patients onto and from bed and transports patientsonto and from bed. Transports patients to hospital areas such asX-ray or physical therapy.

The ways fit information was manipulated and evaluation of jobcandidates was measured were the same as Study 1.

Results

A summary of means, standard deviations, and a correlationmatrix of variables used is presented in Table 4. The overall reli-ability of judgment across participants was .83. Table 5 showsthe results of Level 1 (within-subject) analysis of HLM. Same stepswere taken as Study 1 in Model 1, 2, and 3. In Model 1, PO fit and PJfit averaged across participants were both significant and ac-counted for 81.30% of the explainable variance in the evaluationof the job candidates. In Model 2, the square term of PJ fit was sig-nificant (t = �3.64, p < .01), while that of PO fit was not, suggestingthat there was a curvilinear relationship only between the levels ofPJ fit and the evaluation of the job candidates. The effect size in-creased 1.6% from Model 1, indicating the small incremental valid-ity of the curvilinear component. In Model 3, the interaction termof PO fit and PJ fit was significant (t = 8.41, p < .01) and the effectsize increased 3.82% from Model 2, indicating the small incremen-tal validity of the interaction component. In addition, paired sam-ples t-tests found the candidate who had both moderate levels ofPO fit and PJ fit was evaluated more positively than candidateswho had a high level of PO fit and a low level of PJ fit (t = 4.95,p < .01) and a high level of PJ fit and a low level of PO fit(t = 4.89, p < .01). The effect sizes measured as Cohen’s d were .65for both cases, which were considered as medium. Thus, in Level1 analysis, we obtained exactly the same pattern of results as Study1, replicating our findings for Hypotheses 1, 2 and 3.

The variation in the PO fit and PJ fit slopes in Level 1 analysiswas significant (v2 = 235.12, p < .01 and v2 = 319.23, p < .01,respectively), justifying a Level 2 analysis. Table 6 shows the re-sults of Level 2 (between-subject) analysis of HLM. The slope coef-ficients of PO fit and PJ fit in Model 1 of the Level 1 equation wereregressed onto knowledge-intensiveness of the tasks. As shown inTable 6, the knowledge-intensiveness explained the variance of theslopes for PJ fit (t = 2.21, p < .05), but not for PO fit. Since the knowl-edge-intensiveness was coded as (1 = registered nurse; 0 = certifiednurse assistant), the positive sign of the regression coefficient for PJ

3 4 5 6 7 8 9 10

–.01 –.00 �.01 –.00 .10 .01 –�.01 .00 .76** �.03 –�.01 �.04 .47** .04 .46** –

.01 .16 .22* �.13 .22* .48** –

.00 �.07 �.20 .04 �.26** �.38** �.70** –

ng are quasi-interval scales. A higher score in frequency of hiring means participantseans they participated in hiring decisions more recently.

Table 5Results of hierarchical linear modeling of Level 1 (within-subject) analysis in study 2.

Variable Model 1 Model 2 Model 3

Coefficient SE t Coefficient SE t Coefficient SE t

Intercept, b0 �.96** .11 �8.18 �1.61** .29 �5.59 �.46 .25 �1.81PO fit, b1 1.17** .05 23.80 1.23** .19 6.61 .66** .18 3.60PJ fit, b2 1.17** .04 27.49 1.89** .20 9.25 1.32** .19 6.96PO fit squared, b3 �.02 .05 �.36 �.01 .05 �.32PJ fit squared, b4 �.18** .05 �3.64 �.18** .05 �3.66PO fit � PJ fit, b5 .26** .03 8.41Effect size (%)a 81.30 82.90 86.72

Note: N = 92.a Percentage of explainable Level 1 variance in the dependent variable accounted for by fit cues.

** p < .01.

Table 6Results of hierarchical linear modeling of Level 2 (between-subject) analysis in study 2.

Variable Model 1

Coefficient SE t

PO fit, b1

Intercept, c10 1.18** .06 19.34Knowledge-intensiveness, c11 �.04 .08 �.53Effect size (%)a .00

PJ fit, b2

Intercept, c20 1.08** .06 15.17Knowledge-intensiveness, c21 .20* .09 2.21Effect size (%)a 4.58

Note: N = 92.a Percentage of explainable Level 2 variance in the dependent variable accounted

for by Level-2 variables.* p < .05.** p < .01.


fit slope means that the weight of PJ fit was larger when the posi-tion was for a registered nurse. This result supports the argumentthat PJ fit becomes critical when the knowledge-intensiveness ofthe tasks included in the position is high while the weight of POfit is not affected by the degree of knowledge-intensiveness. Theeffect size was 4.58% for PJ fit slope, indicating that position char-acteristics had small incremental validities to explain the variancesof PJ fit slope.

Finally, to examine whether any individual difference variableinfluenced the weights decision makers gave to PO fit and PJ fit,individual difference variables shown in the descriptive statisticsin Table 1 were entered as predictor variables in the Level 2 equa-tion. None of these variables significantly explained the variancesof the slopes for PO fit and PJ fit.

Discussion

With regard to the combining process of PO fit and PJ fit(Hypotheses 1 to 3), we obtained the same results as in Study 1.Consistent with the results from Study 1, the present study alsofound that curvilinear relationship was significant only for PJ fit.These results enhance the possibility that within the conjunctiverules, hiring decision makers pay more attention to a low level ofPJ fit than that of PO fit to form a lower evaluation of candidates.Potential reasons for this consistent finding will be discussed inthe General Discussion section.

With regard to the weighting process of PO fit and PJ fit, the re-sults from the present study suggest that, within the same occupa-tional family, the weight of PJ fit was influenced by the degree ofknowledge intensiveness, but PO fit was not, which is consistent

with our prediction. The weight of PO fit remains the same evenif the nature of tasks (knowledge intensiveness) varies.

To further evaluate the manipulations of our studies, we con-ducted a small follow-up survey in which 20 business practitionersrated their perceived knowledge-intensiveness of the positionsused in Study 1 and Study 2 with a 7-point scale. Pared-samplet-tests confirmed that perceived knowledge intensiveness was sig-nificantly higher for the in-house attorney position than for themanagerial position (t = 2.81, p < .05) and for the registered nurseposition than for the certified nurse assistant position (t = 5.21,p < .01). This result lends additional support for the positive rela-tionship between the degree of knowledge-intensiveness in taskelements and the weight of PJ fit not only through the comparisonbetween managerial and legal positions as in Study 1 but alsothrough the comparison between different nurse positions in thepresent study.

General discussion

Through the within- and between-subject policy capturing de-sign, our studies examined how hiring decision makers integratePO fit and PJ fit information when they evaluate job candidates,and how the process is influenced by the position characteristics.Results of our studies generally support our prediction that thecombining process of PO fit and PJ fit includes the nonlinear, dis-junctive rule and that the weighting process of PO fit and PJ fit isinfluenced by the position characteristics.

Our studies extended the theoretical development of PO fit andPJ fit construct in the context of selection decision making by shed-ding light on the several notable differences between the charac-teristics of PO fit and PJ fit when hiring decision makers integratethem in the evaluation of job candidates. With regard to the char-acteristics of PJ fit, our findings indicate that hiring decision mak-ers pay more attention to a low level of PJ fit than that of PO fit toform a lower evaluation of job candidates. This finding suggeststhat a low level of PJ fit rather than that of PO fit is more likely usedas the reason to reject job candidates (i.e., the threshold effect). Be-cause PJ fit has solid legal support to be used in making selectiondecisions (e.g., Uniform Guidelines, 1978), rejecting job candidatesbased on a low level of PJ fit might be easily justified.

Moreover, our findings indicate that the weight given to PJ fit inthe evaluation of job candidates is sensitive to both contract dura-tion and knowledge intensiveness, suggesting that decision makersare likely to adjust the weights of PJ fit according to the job char-acteristics. This might be because the nature and criticality of tasksvary significantly across positions, and the possibilities of theimprovement of PJ fit also varies across the time span.

With regard to the characteristics of PO fit, our findings indicatethat decision makers are relatively tolerant to a low level of PO fit,


although they still use PO fit in a noncompensatory manner whencombining it with PJ fit. This might be because, contrary to the caseof PJ fit, PO fit has less legal support to be used in making selectiondecisions. Thus, rejecting job candidates based on a low level of POfit might be less justified.

In addition, our findings indicate that the weight of PO fit is rel-atively insensitive to the characteristics of task elements althoughit is influenced by contract duration. Thus, our findings suggestthat PO fit is weighted in a relatively stable manner across variouspositions involving different tasks. This might be because the effectof PO fit for employee outcomes is considered as universal and sta-ble regardless of the nature of the tasks. In fact, PO fit has strongerrelationships with work attitudes and attachment to the organiza-tion than to job performance (e.g., Arthur et al., 2006; Kristof-Brown et al., 2005). The characteristics of task elements might beless relevant to PO fit’s relationships with work attitudes and orga-nizational attachment. Moreover, because the level of PO fit is rel-atively unchanged over time, the effect of PO fit on work attitudesand attachment endures. Positive work attitudes and attachmentto the organization are always desirable regardless of the type ofjob, especially when employees continue to stay in the sameorganization.

Limitations

One potential limitation of our studies is the relatively weakmagnitude of effect sizes associated with the hypotheses, whichshould be considered in interpreting our findings. For example,incremental variance explained by nonlinear components of POfit and PJ fit combination was fairly small, whereas the linear com-pensatory model explained a large portion of the variance in theevaluation. Several reasons are possible for this result. First, be-cause of the characteristics of the experimental design (e.g., timeconstraints of the voluntary study participants, the necessity to re-duce the cognitive load of the experimental tasks), only a smallnumber of variables were manipulated. Thus, participants maynot have to rely heavily on simplifying strategies (i.e., noncompen-satory rules) to process information. However, research suggeststhat, as the number of attributes for evaluation increases, and theinformation processing becomes more complex, decision makersrely more on nonlinear noncompensatory rules (Billings & Marcus,1983; Ganzach, 1993; Hitt & Barr, 1989; Ogilvie & Schmitt, 1979).Thus, the magnitude of effect sizes could have been larger if morevariables, both relevant and irrelevant to post-hire outcomes, wereincluded in our studies. Second, decision making literature hasconsistently shown that linear combination models are robustand explain a large portion of variance in dependent variables eventhough nonlinear integration processes are actually included(Dawes & Corrigan, 1974; Ganzach, 1998; Goldberg, 1971). None-theless, there is a possibility that the actual combining process ofPO fit and PJ fit is largely compensatory.

In relation to the weighing process, the effects of position char-acteristics explained only a small portion of variances in theweights of PO fit and PJ fit, indicating that the rest of the variancewas still unexplained by the differences in position characteristics.This might be because manipulation was not clear enough in theexperimental procedure. For example, although we intended toexamine the effects of knowledge-intensiveness in the position,we did not manipulate it directly but did so indirectly by using dif-ferent kinds of job positions. Thus, factors other than the differ-ences in the degree of knowledge-intensiveness might haveintervened in the results of our studies. Individual differences ofthe participants might also have influenced the weights of PO fitand PJ fit.

Another potential limitation is that, although we tried to ensurethat our experimental materials were as realistic as possible, and

the majority of participants in our study had considerable hiringexperience, our studies may not have represented the real worldvery well. Because the purpose of our research was to understandthe process of integrating PO fit and PJ fit information when bothtype of information are available to decision makers, we provideda summary of fit information in the job candidate profiles. None-theless, we admit that it may not have been the exact way hiringdecision makers receive job candidate information in real hiringsituations. Selection decisions could be different if decision makerssubjectively assessed PO fit and PJ fit of job candidates and usedthem in the evaluation of job candidates.

Implications and future research

Results of our studies have implications for research on fit in thecontext of selection decision making. First, we found that, becauseof the conjunctive rule, a candidate with a small difference be-tween levels of PO fit and PJ fit is more attractive to hiring decisionmakers than one with a large difference, even though the averagelevels of fit are the same. However, we still do not have clear evi-dence that the former candidate will be more successful than thelatter after hiring. In order to understand whether the findingson the conjunctive rules reflect the correct way to hire the bestcandidate or merely reflect the cognitive biases hiring decisionmakers have, research on criterion-related validity of fit shouldexamine the effects of PO fit and PJ fit on employee outcomessimultaneously, including the nonlinear effects and the interactionbetween PO fit and PJ fit. Several studies have examined the effectsof multiple types of fit on work attitudes (e.g., Cable & DeRue,2002; Kristof-Brown, Jansen et al., 2002; Resick, Baltes, & Shantz,2007) but few studies have examined the combined effects of POfit and PJ fit on job performance, the most widely used criterionin selection decision making.

Similarly, although we found the weights given to PO fit and PJfit in the evaluation of job candidates were adjusted according toposition characteristics, these adjustments do not necessarily re-flect the correct method of selecting the best candidate. However,there is a scarcity of studies that examine the position-specific fac-tors as boundary conditions on the effects of PO fit and PJ fit on em-ployee outcomes. Almost no studies have included contractduration or time factors as moderators on the relationship betweenPO fit and PJ fit and employee outcomes. It is also surprising that fitresearchers have paid little attention to the characteristics of tasksas the boundary conditions of the effects of fit on employee out-comes. Therefore, future studies should examine the moderatingrole of position characteristics on the relationship between multi-ple types of fit and employee outcomes.

Future research should also examine other contextual factorsthat would influence the use of PO fit and PJ fit information in mak-ing selection decisions. For example, although our findings suggestthat the weight of PO fit was less influenced by the characteristicsof task elements, it could be influenced by several organization-le-vel factors such as organizational structure (e.g., the degree of cen-trality) and organizational culture (e.g., its strength). Differences insocial systems and national cultures across countries may alsoinfluence the use of PO fit and PJ fit in making selection decisions.Finally, in order to extend our findings, future studies could usefield data in real hiring situations and also examine the processin which hiring decision makers subjectively assess job candidates’PO fit and PJ fit. As mentioned in the introduction section, past re-search suggests that the subjective judgment of job candidate fitcould be influenced by various factors such as the similar-to-mebiases held by decision makers and influence tactics or impressionmanagement conducted by job candidates (Higgins & Judge, 2004;Kristof-Brown, Barrick et al., 2002). Because hiring organizationsmay want to ensure that decision makers share common


frameworks for assessing job candidate fit, incorporating the sub-jective assessment of fit into the research framework could haveimportant implications regarding whether fit information shouldbe clearly and unambiguously presented or hiring decision makersare allowed to judge job candidate fit by themselves in makingselection decisions.

Acknowledgement

We would like to thank Associate Editor, Paul Levy, and threeanonymous reviewers for their insightful comments and sugges-tions during the review process. We also thank Chad Higgins,Xiao-Ping Chen, and Suresh Kotha for their comments on an earlydraft of this paper.

Appendix 1:. Sample verbal and graphical representation of fitinformation

‘‘Job-related assessments indicate high compatibility betweenthis candidate’s academic degree, work experience, knowledgeand skills and the nature of this job. Organization-related assess-ments indicate medium compatibility between this candidate’spersonal values, personal goals, and personality, and our organiza-tion’s culture, goals, and employees.’’

Assessment results

Job-related assessment
Level of match/compatibility Match between this person’s
academic degree and jobrequirements

Low- - - - - - - - - � - - High

Match between this person’swork experience and jobrequirements

Low- - - - - - - - � - - - High

Match between this person’sknowledge/skills and jobrequirements

Low- - - - - - - - - � - - High

Organizational-related assessment
Level of match/compatibility Match between this person’s
personal values and corporateculture

Low - - - - - � - - - - - - High

Match between this person’spersonal goals and corporategoals

Low - - - - - - � - - - - - High

Match between this person’spersonality and those of ourtypical employees

Low - - - - - - � - - - - - High

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The use of person–organization fit and person–job fit information in making selection decisions