The Role of Different Types of Person-Organization Fit in Japanese Recruiters' Judgments

Transcription

1 The Role of Different Types of Person-Organization Fit in Japanese Recruiters' Judgments of Applicant Qualifications: An Experimental Policy-Capturing Investigation Tomoki SEKIGUCHI Osaka University of Economics* ABSTRACT This study investigates the relative importance of supplementary P-O fit, needs-supplies P-O fit, and demands-abilities P-O fit when Japanese recruiters judge the qualifications of job applicants in college recruitment. An experimental policy-capturing study was conducted using a sample of 33 individuals who held full-time jobs in Japanese industry. Results demonstrated that on average, participants weighted needs-supplies P-O fit lower than supplementary P-O fit and demands-abilities P-O fit. Results also demonstrated that participants combined the three types of P-O fit information not only in an additive manner but also in a multiplicative manner. Through exploratory analyses, two distinctive clusters were identified in terms of the participants judgment policies: One cluster included participants who gave the most weight to demands-abilities P-O fit and less weight to supplementary P-O fit and needs-supplies P-O fit, and another cluster included participants who gave the most weight to supplementary P-O fit, a medium weight to demands-abilities P-O fit, and the least weight to needs-supplies P-O fit. It is also found that age and company size were associated with the relative weight of supplementary P-O fit and needs-supplies P-O fit. Based on these findings, future research directions and implications for practice are discussed. Key words: person-organization fit, college recruitment, judgment, policy-capturing, hiring simulation * From September, 2003 This paper is accepted for publication at the Japanese Association of Industrial/Organizational Psychology Journal. Acknowledgements: I wish to thank Vandra Huber, Chad Higgins and Xiao-Ping Chen for their helpful comments on previous versions of this manuscript.

2 1 INTRODUCTION Japanese practitioners often use the following comparison to describe the characteristics of Japanese hiring practice: "The hiring practice in Japan typically means hiring a person who fits the organization, while the hiring practice in the U.S. means finding a person who fits the job." This characteristic of Japanese hiring practice has been pointed out by academicians as well (e.g., Morishima, 1995; Sako, 1997). This might be especially true when Japanese firms hire a cohort of fresh school graduates. The fresh school graduates usually do not have significant work experience and the jobs they will be assigned are often unrelated to the subject of study at university or college (Sako, 1997). Therefore, Japanese firms place less emphasis on the match between applicant skills and specific job requirements in hiring fresh school graduates (Morishima, 1995). Alternatively, the match between individuals and organizations, or personorganization (P-O) fit, would be the critical factor in hiring school graduates for most Japanese firms. However, researchers point out that the concept of P-O fit is in fact complex and multidimensional. Kristof (1996)'s seminal review on P-O fit literature suggests there are at least three different dimensions of P-O fit. They are (1) supplementary P-O fit, (2) needs-supplies P- O fit, and (3) demands-abilities P-O fit. The major purpose of this study is to investigate the relative importance of these three types of P-O fit when Japanese recruiters judge the qualifications of job applicants. In other words, this study examines how recruiters use, add, or integrate different types of P-O fit information that are obtained through firm's selection processes to make judgment of applicant qualifications. In this study, I focus on the context of hiring college graduates for entry-level

3 2 white-collar generalist positions. White-collar generalists are usually considered to be core employees of Japanese firms and many of them will be promoted to the managerial positions in the future. Therefore, the match between white-collar generalists and organizations may be very important. Another purpose of this study is to use the policy-capturing methodology to analyze the individual judgments of applicant qualifications. Although the policy-capturing methodology is not widely used among Japanese industrial-organizational (I-O) psychologists, it is quite popular in judgment and decision-making studies outside of Japan. As will be discussed later, the policycapturing methodology has many advantages over other methodologies in investigating human judgment and decision-making. PERSON-ORGANIZATION FIT Person-organization fit (P-O) fit can be broadly defined as the compatibility between people and organizations (Kristof, 1996). In personnel selection research, P-O fit can be conceptualized as the match between an applicant and broader organizational attributes (Judge & Ferris, 1992; Rynes & Gerhart, 1990). Researchers and practitioners alike argue that P-O fit is the key to maintaining a flexible and committed workforce that is necessary in a competitive business environment and a tight labor market (e.g., Bowen, Ledford & Nathan, 1991; Kristof, 1996). The roots of P-O fit research can be traced back to Schneider's (1987) Attraction- Selection-Attrition (ASA) framework. Schneider argues that individuals are not randomly assigned to situations, but rather seek out situations that are attractive to them. Ultimately, individuals will be selected to be a part of that situation, and by remaining in that situation, help to determine the situation. Schneider applies this ASA framework to the functioning of

4 3 organizations. It is argued that organizations are one situation that people are attracted to, are selected to be a part of, and remain with if they are a good fit or leave if they are not a good fit. Dimensions of P-O fit. The concept of P-O fit is considered to be complex and multidimensional. Based on the previous literature, Kristof (1996) identified several major dimensions of P-O fit. The first dimension is the distinction between supplementary P-O fit and complementary P-O fit. Supplementary P-O fit focuses on the similarity between personal attributes and organizational attributes, meaning that a good fit occurs when a person supplements, embellishes, or possesses characteristics that are similar to other individuals in an organization. Complementary P-O fit primarily focuses on the demand-supply relationship between the person and the organization, meaning that a good fit is the mutually offsetting pattern of relevant characteristics between the person and the organization. Another dimension of P-O fit is the distinction between needs-supplies P-O fit and demands-abilities P-O fit. Needs-supplies P-O fit occurs when an organization satisfies a person s needs, desire, or preferences. Demands-abilities P-O fit occurs when a person has the abilities that are required to meet organizational demands. This second dimension is considered to be an extended conceptualization of complementary P-O fit (Kristof, 1996). That is, the complementary P-O fit can be divided into needs-supplies P-O fit and demands-abilities P-O fit. Figure 1 summarizes the relationship among different types of P-O fit. Three different types of P-O fit, namely, supplementary P-O fit, needs-supplies P-O fit, and demands-abilities P- O fit, are used in this study to examine their simultaneous impacts on recruiters judgment of applicant qualifications Insert Fig. 1. about here

5 4 Empirical evidence shows that a high level of P-O fit is related to a number of positive outcomes. First, P-O fit was found to be correlated with work attitudes such as job satisfaction and organizational commitment (Boxx, et al., 1991; Bretz & Judge, 1994; Chatman, 1991; Downey, Hellriegel, & Slocum, 1975; O'Reilly, Chatman & Caldwell, 1991; Postner, Kouzes & Schmidt, 1985; Tziner, 1987; Vancouver & Schmitt, 1991). Second, P-O fit was found to predict intention of quit and turnover (Chatman, 1991; O'Reilly et al., 1991; Vancouver et al., 1994). Third, P-O fit was also related to prosocial behaviors such as organizational citizenship behaviors (O'Reilly & Chatman, 1986), self-reported teamwork (Posner, 1992), and contextual performance (Goodman & Svyantek, 1999). Fourth, P-O fit was related to self-report work performance (Tziner, 1987) and objective measures of work performance (Downey et al., 1975; Bretz & Judge, 1994). In the employee selection context, researchers found that interviewers' perception of applicant P-O fit predicted invitations of the next interviews (Adkins, Russell, & Werbel, 1994) and hiring recommendations (Cable & Judge, 1997). Cable and Judge (1997) also found that interviewers' hiring recommendations directly affected organizations' hiring decisions. Despite the large body of the empirical literature on P-O fit, most studies have operationalized P-O fit as unidimensional. Specifically, most studies on P-O fit in employee selection have operationalized P-O fit as supplementary fit (e.g., value congruence) or as an overall P-O fit perception (e.g., Adkins, Russell, & Werbel, 1994; Cable & Judge, 1997; Kristof- Brown, 2000; Rynes & Gerhart, 1990). Therefore, we know little about the effects of different types of P-O fit on recruitment and selection outcomes.

6 5 HYPOTHESES AND RESEARCH QUESTIONS This study focuses on the relative importance of different types of P-O fit when Japanese recruiters judge the qualifications of job applicants for white-collar generalist positions. It seems that the importance of the three types of P-O fit, namely, supplemental P-O fit, needs-supplies P- O fit, and demands-abilities P-O fit, is widely recognized among recruiters in Japan. However, the relative importance of these types of fit may differ considerably. First, Japanese recruiters may place a greater weight on the ability side of job applicant characteristics. A study by the Japan Institute of Labor (JIL) found that Japanese recruiters preferred job applicants who were energetic and active, cooperative and balanced, and/or creative and suggestive (The Japan Institute of Labor, 2000). Another study found that communication skills, proactive attitudes, challenging spirits, integrity, cooperativeness, and dependability were the major characteristics of desirable job applicants reported by Japanese firms (Nikkeiren, 2001). Because the multiple-choice items used in these surveys were predetermined, there was a possibility that employers were also looking at other applicant characteristics that were not on the lists. However, it is inferred from these studies that recruiters are likely to focus on the ability components of P-O fit in evaluating job applicants. Note that the ability concept here does not necessarily mean the specific knowledge, skills and abilities (KSAs) to do a particular job. In fact, even for white-collar technical employees, fewer than 10% of the firms reported primarily emphasize the specific technical skills when selecting job applicants (Fujiwara, 1993). Rather, the ability concept here means an overall ability and personality characteristics that would satisfy organizational demands. Therefore, demandsabilities P-O fit, which focuses on the match between the organizational demands and abilities of the job applicants, may be quite important when recruiters judge applicant qualifications.

7 6 Second, because white-collar generalists are considered to be a core workforce, are anticipated to be the companies managerial staff, and are expected to keep highly committed to the organizations, Japanese firms may want to hire employees who share the companies mission, values and culture. Also, Japanese employment practice in general is characterized as long-term, which is especially true for white-collar employees. To keep a long-term good relationship with other organizational members, newcomers should have similar values and personality characteristics with other members. Therefore, the role of supplementary P-O fit may also play a critical role in selecting white-collar generalists. The fact that many Japanese firms use multirater sequential interviewing for selecting job applicants may reflect such emphasis on supplementary P-O fit. Needs-supplies P-O fit may also be important because employees whose needs are met by organizational supplies may keep highly committed and motivated. However, the prevalent approach of hiring among Japanese organizations is to clarify who are the ideal job candidates and find out such people. In this approach, the question of how organizations can supply what job applicants need may be less focused. Even Japanese organizations that try to appeal their consideration of employee needs, it is suspected that in reality, the priority of needs-supplies P-O fit may be lower than other types of P-O fit. Hypotheses 1: The relative importance of needs-supplies P-O fit will be lower than supplementary P-O fit and demands-abilities P-O fit when Japanese recruiters judge the qualifications of job applicants. Next, research on decision-making suggests that people often combine information nonlinearly (e.g., Brehmer, 1974; Hitt & Barr, 1989). Therefore, when recruiters make a judgment of applicant qualifications, the way they integrate the three types of P-O fit information

8 7 may not only be linear (i.e., information is used in an additive manner), but also be nonlinear or configural (i.e., includes multiplicative terms). From this point, there may be interactions of the different types of P-O fit when recruiters judge the qualifications of job applicants. Based on the previous discussions, there may be an interaction of the two relatively important P-O fit dimensions, that is, supplementary P-O fit and demands-abilities P-O fit. The applicant who are high in both supplementary P-O fit and demands-abilities P-O fit may be judged more favorably by recruiters that goes beyond the simple additive or linear manner of the two types of P-O fit. Because the previous hypothesis predicts that the relative importance of needs-supplies P-O fit will be low, there may be no significant interactions between needs-supplies P-O fit and other types of P-O fit. Hypotheses 2: Supplementary P-O fit and demands-abilities P-O fit will interact with each other in a way that there is a drive for a high evaluation by Japanese recruiters for job applicants who are higher in both types of fit. In addition to these two hypotheses, two other research questions are explored in this study. The first question is concerned with some patterns in which Japanese recruiters give weight to each type of P-O fit. In other words, if there is individual difference in terms of the relative weights of each type of P-O fit, are there any clusters that can be distinct from one another regarding the relative weights of P-O fit? If there are any meaningful clusters, recruiters in a certain group may weigh a particular type of P-O fit more heavily than others, and recruiters in another group may weigh different type of P-O fit more heavily than others, and so on. The second question is concerned with the effect of demographic variables on the relative weight of the three type of P-O fit. That is, do any demographic variables affect the ways recruiters weigh particular types of P-O fit? If there is any demographic variable that explains the variability of

9 8 P-O fit use, it may also be associated with the clusters of recruiters who give different weights to particular types of P-O fit. POLICY-CAPTURING METHODOLOGY In this study, I used an experimental policy capturing methodology to test hypotheses. Policy-capturing is a regression-based methodology that is employed by researchers to assess how decision makers use available information when making evaluative judgments (Zedeck, 1977). The purpose of this methodology is to capture individual judgment or decision-making policies, that is, how they weight, combine, or integrate information (Zedeck, 1977). Within that context, the term policy capturing refers to the studies that analyze judgments made on the basis of multidimensional stimuli by means of a linear model (Brehmer & Brehmer, 1988). Although this methodology does not appear to be popular among Japanese I-O psychologists, many researchers outside of Japan have applied it for a variety of content areas such as job search (Cable & Judge, 1994; Rynes & Lawler, 1983), job analysis (Sanchez & Levine, 1989), performance appraisal (Zedeck & Cascio, 1982), compensation (Sherer, Schwab & Heneman, 1987; Viswesvaran & Barrick, 1992), employment interviews (Doughety, Ebert, & Callender, 1986; Graves & Karren, 1992), employee selection (Dunn, Mount, Barrick & Ones, 1995), promotion (Viswesvaren, Schmidt, & Deshpande, 1994), and motivation (Zedeck, 1977). In a policy capturing study, the respondent is given a relatively large set of scenarios, each of which is composed of several stimuli, and the respondent is asked to make a judgment in response to each scenario (Stewart, 1988). The numeric values corresponding to each level of each stimulus cue within each scenario are then entered into a multiple regression equation to predict the respondent s judgments. In this way, the relative importance of each cue in the

10 9 respondent s judgments can be quantified. The regression equation, with its indices of the relative weight given to each variable (e.g., Beta weights), represents the individual s judgment policy (Stewart, 1988). Policy-capturing is considered to be the better way to assess the relative weights that decision-makers place on attributes than directly asking them such weights. There is ample evidence in the psychological literature that direct importance ratings or rankings of variables considered in decision making do not correlate very well with actual importance placed on the same (Ones & Viswesvaran, 1999). Repeated findings from decision-making literature suggest that raters have only moderate degree of insight into their own ratings and on the relative weights they place on attributes (e.g., Hobson & Gibson, 1983). Compared with approaches that directly quiz decision makers on the relative importance of different variables hypothesized to affect the decision, policy capturing, by indirectly inferring (via regression equations) the relative importance of the different variables for the decision, is less intrusive. Social desirability influences are mitigated to a certain extent by this technique, as respondents are never asked to directly rate the importance of any characteristic (Cable & Graham, 2000). Also, the level of experimental control in policy capturing designs facilitates causal inferences, enabling researchers to better assess the effects of the within-subjects factors (Cable & Graham, 2000). Some researchers may criticize the use of scenarios (i.e., hypothetical applicants in this study) because of the problem of generalizability. However, Cleveland (1991) examined whether decision-makers in an organization responded similarly to both actual and experimentally generated hypothetical applicants and obtained similar results from both types of applicants. In general, the bulk of the evidence supports the idea that the use of "paper" representations of real cases for the capturing of judgment policies does not seriously distort the

11 10 realism (Brehmer & Brehmer, 1988). Therefore, using hypothetical applicants is appropriate if the study is designed to capture the essential feature of human judgment and decision-making processes. METHODS Sample. Thirty-three individuals who held full-time positions in Japanese industry participated in this study. I sent messages to 52 potential participants in the name list that was available to me. Through the message, the potential participants were invited to voluntarily participate in this study. The potential participants included MBA graduates from a university in Kanto area, and other college graduates from major universities in Kanto and Kansai areas. Then, the 33 individuals were agreed to participate in my study, yielding a response rate of 63.5 %. Twenty-seven (81.1%) of the participants were males, and 6 (18.2%) were females. Twenty-two (66.7%) of the participants were in their 30 s, 6 (18.2%) were 40 s, and 5 (15.2%) were 20 s. Fourteen (42.4%) of them were from the companies with over 1000 employees, 7 (21.2%) were with , 5 (15.2%) were with , 5 (15.2%) were with 50 or less, and 2 (6.1%) were with Their jobs covered diverse fields including 9 (27.3%) professionals, 6 (18.2%) administrative, 5 (15.2%) information technology, 4 (12.1%) sales, 2 (6.1%) marketing, 2 (6.1%) research and development, and 5 (15.2%) others. Eighteen (54.5%) participants had actual experience of being involved in college recruitment activities. Considering their age and positions as full-time, other 15 (45.5%) participants may also have been familiar with college recruitment practices. Procedure. Participants visited the Internet website created for this policy-capturing study. In the website, a hiring simulation program was developed to run a policy capturing procedure. Participants who visited the website were asked to complete the minute hiring

12 11 simulation study. In the simulation study, they were instructed to play the role of an employee who works for a hypothetical Japanese company. The company was described as a "typical" Japanese company and participants were allowed to think of their own employers if they thought that they worked for typical Japanese companies. They were told that the employee in the hypothetical company was involved in the annual college recruitment activity and his/her major role was to evaluate 30 job applicants for white-collar generalist positions. Participants were told that these job applicants had survived the first prescreening process and that they had to eliminate the number of job applicants for subsequent selection steps. After given these instructions, they were shown a series of job applicant profiles. The job applicant profile consisted of six-item assessment results. For each profile, participants were asked to rate the degree to which they wanted to hire the applicant (i.e., the judgment of applicant qualifications) using 7-point scale from (1 = not at all) to (7 = absolutely). After they finished evaluating all applicants, participants demographic information was collected and the session was finished. Scenarios. To determine the relative importance of the three type of P-O fit, the level of each was manipulated in the web-based hiring simulation. Review of the literature identified several operationalizations for each type of P-O fit (e.g., Kristof, 1996). To maintain balance, two operationalizations were selected for each type of P-O fit, and they were used for creating fit cues in the applicant profiles. Therefore, each applicant profile consisted of six assessment items that are relevant to the three types of P-O fit. The level of each type of P-O fit, which includes two items each, was manipulated as high (8-10), middle (5-6), or low (1-3). These levels of fit cues were shown using the combination of graphs and numbers to ensure manipulation. This way of cue representation had advantages such as (a) being easy to read and interpret, (b) providing pictorial display of cue information, and (c) providing a clear relative indication of

13 12 where each cue value sits with respect to values of all other cues, and (d) an indication of where each cue value sits with respect to the total overall range of values for that cue (Cooksay, 1996). A sample applicant profile is shown in Figure 2. The levels of cues were completely crossed to make all possible combinations, resulting in the 27 different profiles (3 x 3 x 3 = 27) for each participant. Profiles for replications are added to assess the reliability of judgment, resulting in the total 30 profiles for each participant. Participants were shown these profiles in a randomized order Insert Fig. 2. about here RESULTS Capturing individual judgment policies. In order to obtain as much information as possible about participants judgment policies, I conducted multiple analyses for the policycapturing data. As the first step, I conducted a multiple regression analysis for each participant to assess the effects of the linear combination of the three fit cues on the judgment of overall applicant qualifications. In the analysis, a participant s judgments on the applicant qualifications were regressed onto the three types of P-O fit cues. Table 1 shows means and standard deviations of the standardized regression coefficients for three types of fit cues, and information of judgment consistency and reliability. A table that contains all of the 33 individual regression equations can be obtained from the author Insert Table 1 about here Because fit cues were orthogonal, the standardized regression coefficients can be interpreted as relative weights. With regard to the means of standard regression coefficients, the

14 13 relative weight of demands-abilities fit was the highest (.64), the weight of supplementary P-O fit was in the middle (.41), and the weigh of needs-supplies P-O fit was the lowest (.23). Multiple correlation coefficient derived from individual regression analysis were interpreted as a measure of consistency with which the participant used the captured policy. The average of multiple correlation coefficients across all participants was.86. Reliability of judgments was also estimated for each participant by calculating correlation between original and replicated profiles. The average of the reliability estimates across all participants was.75. To test whether the average relative weights differ significantly, I conducted three pairedsample t tests. Results show that the average weight of demands-abilities P-O fit was significantly higher than that of needs-supplies P-O fit (t = 8.08, p <.01), the average weight for demands-abilities P-O fit was also significantly higher than that of supplementary P-O fit (t = 2.86, p <.01), and the average weight of supplementary P-O fit was significantly higher than that of needs-supplies P-O fit (t = 3.02, p <.01). Therefore, on average, the weight of demandsabilities P-O fit was the highest, the weight of supplementary P-O fit was in the middle, and the weight of needs-supplies P-O fit was the lowest. These results support Hypotheses 1. That is, when participants judged the qualifications of job applicants, the relative importance of needssupplies P-O fit was significantly lower than the weights of supplementary P-O fit and demandsabilities P-O fit. Repeated-measures regression analysis with pooled data. Hypothesis 2 states that recruiters judgments of applicant qualifications include nonlinear or configural cue use. To test this hypothesis, I conducted a repeated measures regression analysis (Cohen & Cohen, 1983) by aggregating individual policy-capturing data. This analysis enabled me to maintain enough statistical power to test the non-linearity while controlling individual effect for the dependent

15 14 variable. The dependent variable was the judgment of applicant qualifications and independent variables were different types of fit cues. Dummy variables were entered in the first step of a hierarchical regression analysis. This procedure controlled for person effects in all subsequent steps in the analysis. After the three types of P-O fit cues were entered in the hierarchical regression analysis, the two-way and three way interaction terms were entered as the last step. Consistent with the recommendations by Cohen and Cohen, (1983), the fit cues were centered on their means before the interaction terms were computed. The results from the repeatedmeasures regression analysis are provided in Table Insert Table 2 about here Consistent with the individual policy capturing results, the regression coefficients for supplementary P-O fit, needs-supplies P-O fit, and demands-abilities P-O fit were.39,.16, and.61, respectively. Variances accounted for supplementary P-O fit, needs-supplies P-O fit, and demands-abilities P-O fit were.15,.03, and.38, respectively. This confirms the previous analysis that supported Hypothesis 1. That is, on average, the relative weight of demandsabilities P-O fit was the largest, followed by the weight of supplementary P-O fit and the weight of needs-supplies P-O fit. In terms of interactions, all of the two-way and three-way interactions were significant. Thus, Hypothesis 2 was supported but all of other possible interactions were also significant. The regression coefficients of the interaction between demands-abilities P-O fit and supplementary P-O fit was the largest (.11) and statistically significant at p <.01 level, while the coefficients of other interaction terms were about.04 and were statistically significant at p <.05 level. This indicates that while all of possible interactions were significant, the effect size of the interaction between supplementary P-O fit and demands abilities P-O fit was larger than

16 15 any other interactions. Figure 3 illustrates the two-way interaction between supplementary P-O fit and demands-abilities P-O fit. Consistent with Hypothesis 2, one-unit increase in supplementary P-O fit produced a larger increase in the judgment of applicant qualifications when demands-abilities P-O fit was higher than when it is lower. Hierarchical cluster analysis. Because individual difference in relative cue weights were observed from individual policy capturing data, hierarchical cluster analysis was conducted to determine whether participants could be grouped together on the basis of relative weights obtained from the policy capturing. SPSS CLUSTER program were used to perform the analysis. Squire Euclidean distances were employed as the basis for cluster formation. Clusters were formed by grouping individuals into bigger and bigger clusters in successive stages according to Ward s method. At each stage, the CLUSTER program provided a coefficient that represented the value of the distance between the two most dissimilar points of the clusters being combined. Visual inspection of the Horizontal Hierarchical Tree Plot (Dendrogram) and changes of distance coefficients indicated the four-cluster solution. However, two of these clusters had only one participant and majority of participants were grouped into one of the two major clusters. Next, each participant was assigned to one of the 4 clusters identified by the hierarchical clustering procedure. To interpret the meaning of the clusters, the standard regression weights were averaged within each cluster. There results were presented in Table Insert Table 3 about here A series of paired sample t-tests were conducted to examine whether there were statistically significant differences among the average weights of the three types of P-O fit for each cluster. For cluster 1, the average weight of demands-abilities P-O fit was significantly

17 16 larger than those of supplementary P-O fit and needs-supplies P-O fit (t = 8.80, p <.01; t = 9.52, p <.01, respectively). There was no significant difference between supplementary P-O fit and needs-supplies P-O fit. This indicates that cluster 1 included participants who gave the most weight to demands-abilities P-O fit and less weight to supplementary P-O fit and needs-supplies P-O fit. For cluster 2, the average weights of supplementary P-O fit and demand-abilities P-O fit were significantly larger than that of needs-supplies P-O fit (t = 11.60, p <.01; t = 3.85, p <.01, respectively). Also, the average weight of supplementary P-O fit was significantly larger than that of demands-abilities P-O fit (t = 3.02, p <.01). This indicates that cluster 2 included participants who gave the most weight to supplementary P-O fit, a medium weight to demandsabilities P-O fit, and the least weight to needs-supplies P-O fit. Other two clusters that include only one participant each are considered to be outliers and thus it may not be meaningful to count as different clusters. These findings indicate that participants relative weights could be grouped into two homogeneous clusters: those who gave the most weight to demands-supplies P- O fit, and those who gave considerable weights to supplementary P-O fit and demands-supplies P-O fit with a greater weight to the former. The relative weight of needs-supplies P-O fit was low in both clusters. Hierarchical linear modeling. Finally, I performed hierarchical linear modeling (HLM) (Bryk & Raudenbush, 1992) using the policy-capturing data. The purpose of this analysis was to examine whether participants' demographic variables were related to relative weights of the three types of P-O fit cues. The HLM 5 hierarchical linear modeling (Raundenbush, Bryk, & Congdon, 2001) program was used to estimate the model. The HLM analysis in this study included level 1 and level 2 models. Similar to the first analysis of this study, the level 1 model was a within-subject regression equation in which

18 17 judgment of applicant qualifications was regressed onto the three types of P-O fit for each person. The level 2 models were between-subject regression equations in which parameters in level 1 model (e.g., regression slopes) were regressed onto demographic variables. As a preliminary step, I performed a random coefficient regression model to examine whether there are significant variations of in the slope parameters in level-1 model (Bryk & Raundenbush, 1992). As expected, there were significant variances on the slope for each of the fit cue in level 1, which justified the subsequent analysis. ( 2 = , p <.01 for supplementary P-O fit slope, 2 = 80.75, p <.01 for needs-supplies P-O fit slope, and 2 = , p <.01 for demands-abilities P- O fit slope). Next, I conducted a slope-as-outcome model in which the slopes of the three type of P-O fit in the level-1 model were regressed separately onto gender, age, company size and hiring experience. Although these demographic variables were measured as categorical with range, they were interpreted as interval scales (i.e., quasi-interval variables) for this analysis, except for gender and hiring experience that were coded as dichotomous variables. There were no significant correlations among these demographic variables, indicating that multicolinearity problem was not likely in this analysis. The result from the slope-as-outcome model in HLM analysis is provided in Table Insert Table 4 about here First, there ware no significant difference in terms of the P-O fit slopes between those who had direct experience of college recruitment activities and those who did not. This finding indicates that college recruitment experience did not affect the way participants weighed different types of P-O fit.

19 18 Second, age was significantly and negatively associated with the variance in the needssupplies P-O fit slope ( = -.25, t = , p <.01), suggesting that older participants were likely than younger participants to give a smaller weight to needs-supplies P-O fit. Third, company size was significantly and positively associated with variance in the needs-supplies P-O fit slope ( =.11, t = 3.589, p <.01), suggesting that participants from larger firms were more likely than those from smaller firms to place a greater weight on needs-supplies P-O fit. Company size was also significantly but negatively associated with variance in the supplementary P-O fit slope ( =.09, t = , p <.05), suggesting that participants from larger firms were more likely than those from smaller firms to place a smaller weight on supplementary P-O fit. Two highly significant (i.e., p <.01) effects of demographic variables were on the needssupplies P-O fit slope. Thus, the result from HLM was not useful to understand the difference of the two groups identified in the hierarchical cluster analysis because the weight of needs-supplies P-O fit was the lowest in both clusters. DISCUSSION The major purpose of this study was to investigate the relative importance of supplementary P-O fit, needs-supplies P-O fit, and demands-abilities P-O fit when Japanese recruiters evaluate job applicants in college recruitment. An experimental policy-capturing design was employed to test two hypotheses and explore other research questions. Results from this study support the two hypotheses. Regarding Hypothesis 1, results show that needs-supplies P-O fit was weighted lowest among the three types of P-O fit. On average, the relative weight of demands-abilities P-O fit was the highest, followed by the weight of supplementary P-O fit. There is a possibility that this finding can be generalizable to the hiring practice in general (i.e.,

20 19 not specific to the Japanese context) and a more general fit concept (i.e., not specific to P-O fit). For example, it may be reasonable to argue that, in general, demands-abilities fit is always weighted more heavily than needs-supplies fit from the employers' perspective. However, it is less clear whether supplementary fit is always weighted more heavily than needs-supplies fit in other countries because the emphasis of supplementary P-O fit is one of the distinctive characteristics of the Japanese hiring practice. Regarding Hypothesis 2, results show that participants used the three types of P-O fit information not only in an additive manner but also in a multiplicative manner. Especially, supplementary P-O fit and demands-abilities P-O fit interacted with each other in a way that there was a drive for a higher evaluation if the applicant were both higher in the two types of P-O fit, which supported Hypothesis 2. Unexpectedly, the analysis with pooled data detected all possible interactions of the three types of P-O fit. This finding suggests that the process of evaluating job applicants is more complex including nonlinear or configural information integration. There were also other findings through exploratory analyses. First, hierarchical cluster analysis indicated that participants in this study can be grouped into two homogeneous clusters in terms of relative weights. One cluster included participants who gave the most weight to demands-abilities P-O fit and less weight to supplementary P-O fit and needs-supplies P-O fit. Another cluster included participants who gave the most weight to supplementary P-O fit, a medium weight to demands-abilities P-O fit, and the least weight to needs-supplies P-O fit. What is particularly interesting is the role of supplementary P-O fit in the two clusters. For participants in one cluster, the relative weight of supplementary P-O fit was as small as needssupplies P-O fit. For participants in another cluster, the relative weight of supplementary P-O fit

21 20 was the highest. That is, the relative importance of supplementary P-O fit varies greatly across the two clusters. In both clusters, demands-abilities P-O fit weighted considerably, suggesting that participants may not ignore the demands-abilities side of P-O fit in any case. One possible reason of the large variation of the weight of supplementary P-O fit is the role of organizational culture on the recruiters' judgment policies. In companies with strong organizational culture, corporate goals, values and norms are extensively shared among employees (O'Reilly & Chatman, 1996). In such a case, recruiters may weigh supplementary P-O fit considerably compared with other types of P-O fit in the selection process. In contrast, recruiters from companies with weak organizational culture may not weigh supplementary P-O fit heavily because for these organizations, the shared corporate goals, values and norms may be less critical in selecting newcomers. Next, the analysis using HLM found that there ware no significant difference between those who had direct experience of college recruiting and those who did not. This result can be interpreted in two ways. First, as described in the method section, regardless of college recruiting experience, participants might have been familiar with at least early stages of college recruitment activities in Japanese organizations. Therefore, they represent the same sample of interest and thus resulted in no statistical difference in terms of the relative weights. Second, the findings of this study could be genaralizable to the Japanese employees in general, regardless of their college recruiting experience. That is, even if an employee does not have college recruiting experience, he or she may be expected to weigh the three types of P-O fit in the same way as this study found. However, it is hard to conclude which one is an appropriate explanation only from this study.

22 21 The HLM analysis also found that age and company size affected the relative weight of P-O fit. First, older participants gave a smaller weight on needs-supplies P-O fit than younger participants did. This may be because younger participants might have more fresh memories of college recruitment as job seekers, and thus they might have conducted the hiring simulations considering job seekers needs. Second, participants from larger companies gave a greater weight on needs-supplies P-O fit than participants from smaller companies did. This may be because large Japanese firms have more sophisticated human resource management systems that consider employee needs as well as organizational demands. Thus, participants from large companies might reflect such a tendency to consider applicant needs. However, participants from larger companies gave a smaller weight to supplementary P-O fit than participants from small companies did. This may contradict with the notion that larger organizations may consider supplementary P-O fit more because shared value would be critical to control a large number of their employees. Note that the explanations and discussions above are tentative. Caution should be made in interpreting the results in the above way because hierarchical cluster analysis and HLM were used explanatorily in this study without any prior hypotheses. Also, sample size in this study was not large enough to thoroughly conduct the between-subject analyses, and demographic variables used in HLM were quasi-interval or dichotomous scales. There are possibilities that the results in the exploratory analyses capitalized on chance. Limitations. Although this study using the policy-capturing methodology had many advantages and was considered appropriate in analyzing recruiters judgments, some limitations should also be mentioned. First, this study only focused on the judgment of recruiters from hiring organizations and thus did not investigate final hiring decisions. Typical recruiters from

23 22 Japanese firms are only involved in the early stages of the selection process and may not represent corporate final hiring policies. Literature on decision-making and personnel selection suggests that characteristics of final hiring decisions and early prescreening decisions are different (e.g., Beach & Mitchell, 1987; Kahneman & Tversky, 1979; Huber, Neale & Northcraft, 1987). Therefore, findings from this study may not be generalizable to the final hiring decisions in Japanese firms. Second, although the experimental nature of the policy-capturing methodology enabled the research design to manipulate and control various variables, some factors still remained uncontrolled. For example, some participants may have used their employers' selection criteria and others may have used their own criteria in judgment tasks. Similarly, some participants may have been aware of the detailed selection policies of their employers and others may have had only the limited information about the selection process. Because sample size of this study was not large, some of these uncontrolled factors may have affected the results of this study. Future research directions. In light of above discussions, two major issues should be investigated in the future. First, although this study identified two distinctive clusters of participants in terms of the relative weights to the three types of P-O fit, demographic variables collected and analyzed in this study could not provide clear explanations for such difference. Therefore, future research should explore what cause the difference in recruiters use of the three types of P-O fit by including between-subject design with a large sample. As discussed above in this section, one possible factor that affects the relative weights would be the strengths of organizational culture. If companies have strong organizational culture, they may want to hire employees who fit very well with such culture. Thus, recruiters from such companies may rely heavily on supplementary P-O fit in evaluating job applicants. Recruiters industrial

24 23 backgrounds or types of job openings may also affect the differences. For example, if organizations want to hire employees for more technical jobs, or if they are in the industries such as high-tech or bio-technology where technological skills are crucial, recruiters from such organizations may rely heavily on demands-abilities P-O fit in evaluating job applicants. Second, in addition to investigating the role of P-O fit when organizations hire people, future research should also investigate the role of the three types of P-O fit on post-hire outcomes. There are descriptive and prescriptive approaches to study the employee selection practice. A prescriptive approach focuses on what organizations should do in order to select the right applicant. A descriptive approach focuses on what organizations actually do in hiring employees. Because this study employed a descriptive approach about what recruiters actually do in evaluating job applicants, it is not clear whether the relative weights they actually place are really desirable for organizations. Therefore, research is needed to investigate the criterion-related validity between different types of P-O fit as predictor variables and a variety of post-hire outcomes as criterion variables. Obtaining empirical findings both from prescriptive and descriptive approaches would provide implications for theory and practice on employee selection. Implications for practice. Findings from this study have implications both for employers and job seekers. For employers, results of this study suggests that, even if the best fit between the person and the organization might be desirable in selecting employees, the relative weights recruiters place when they evaluate job applicants differ significantly. One clear finding is that there may be a bias toward underestimating the importance of needs-supplies P-O fit. However, research shows that needs-supplies P-O fit, as well as other types of P-O fit, will have positive effects on individuals and organizations. For example, Cable & DeRue (2002) empirically demonstrated that needs-supplies fit perceptions was positively related to employee satisfaction

25 24 and organizational commitment. Because all types of P-O fit are considered to produce positive employee and organizational outcomes, organizations might want to correct such recruiters bias by providing recruiter training or developing objective P-O fit assessment tools. This study also found that there are considerable variations in the relative weights of P-O fit, particularly supplementary P-O fit. Organizations may want to know whether their recruiters use the same judgment policy (i.e., relative importance of the three types of P-O fit) consistently. If recruiters in the same organizations use different judgment policies (e.g., some recruiters weigh supplementary P-O fit heavily and others do not), the organization cannot achieve consistent and reliable selection process. It may not be ideal both for post-hire outcomes (e.g., satisfaction, commitment and job performance) and applicants perceived fairness against the selection process. For job seekers, results of this study suggests that job seekers should focus more on the supplementary P-O fit and/or demands-abilities P-O fit when they apply for the job. Because the effect of recruiters perception of needs-supplies P-O fit on the selection outcomes may be weak, appealing needs-supplies P-O fit may not be an effective strategy for job applicants. More importantly, job seekers should investigate in advance which types of P-O fit, particularly which of supplementary P-O fit and demands-abilities P-O fit, would be more salient in the selection process of the target company. This would lead to more effective job application strategies (i.e., resume writing and interview preparations) for job seekers.

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