Does a corresponding set of variables for explaining voluntary organizational turnover transfer to explaining voluntary occupational turnover?

Journal of Vocational Behavior 70 (2007) 135–148 www.elsevier.com/locate/jvb

Does a corresponding set of variables for explaining voluntary organizational turnover transfer to explaining voluntary occupational turnover? 夽 Gary Blau ¤ Temple University, Human Resource Management Department, 1810 N. 13th St., 384 Speakman Hall, Philadelphia, PA 19122, USA Received 18 July 2006 Available online 8 September 2006

Abstract This study proposed and tested corresponding sets of variables for explaining voluntary organizational versus occupational turnover for a sample of medical technologists. This study is believed to be the Wrst test of the Rhodes and Doering (1983) occupational change model using occupational turnover data. Results showed that corresponding job (occupational) satisfaction and intent to leave organization (occupation) variables were each signiWcant for explaining subsequent organization (occupation) turnover. Job insecurity was found to be a signiWcant correlate for organizational turnover while work exhaustion was found to be a signiWcant correlate for occupational turnover. Study limitations and directions for future research are discussed. © 2006 Elsevier Inc. All rights reserved. Keywords: Occupational turnover; Comparing organizational versus occupational turnover

夽 The author was a member of the Research and Development Committee for the Board of Registry, American Society of Clinical Pathologists. I thank the Board of Registry for permission to use this data. Portions of this paper were written while the author received Summer Research support from Temple University. * Fax: +1 215 204 8362. E-mail address: [email protected].

0001-8791/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.jvb.2006.07.007

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1. Introduction Voluntarily changing organizations (jobs) and occupations are each viewed as separate types of interrole transitions, where a transition is deWned (Louis, 1980, p.330), as “a period during which an individual is changing roles (taking on a diVerent objective role).” Louis (1980) distinguishes Wve types of interrole transitions: entry/reentry; intracompany (transfer), intercompany, interprofession and exit (retirement). This paper will focus on comparing voluntary intercompany (organization) and interprofession (occupation) work transitions. In a voluntary organizational work transition employees leave their organization, while for voluntary occupational turnover an individual changes their occupation. Practitioner literature in the United States suggests that changing organizations happens much more frequently than changing occupations. For example, Bolles (2006) has suggested that the average worker under 35 years of age will go job-hunting in a diVerent organization every one to three years, while over 35 year olds will go look for an interorganization change every Wve to eight years. Bolles (2006) also suggests that many individuals will change occupations at least three times before exiting the work force. Academic research on leaving one’s occupation suggests that it is typically a much more diYcult type of work transition, versus leaving one’s organization, due to the greater “costs,” such as additional needed training and human capital investment, disrupted work relationships, and lost time and income, typically associated with occupational change (Blau, 2000; Blau, Tatum, & Ward-Cook, 2003a; Carson, Carson, & Bedeian, 1995; Cunningham & Sagas, 2004; Neapolitan, 1980). Research designs reXect the diYculty of this transition and collecting actual occupational change data is very rare. Prior survey-based research has focused on intent to change occupations as the outcome variable (e.g., Blau, 2000; Blau & Lunz, 1998; Blau et al., 2003a; Carson et al., 1995; Cunningham & Sagas, 2004; Lee, Carswell, & Allen, 2000; Meyer, Allen, & Smith, 1993; Rhodes & Doering, 1993; Snape & Redman, 2003). The goals of this paper are to: Wrst, test the relationships of individual controls, work attitude antecedents, work attitudes, turnover perceptions, and turnover intent variables for explaining subsequent organizational turnover behavior. Second, the applicability of the same individual controls and corresponding work attitude antecedents, work attitudes, turnover perceptions, and turnover intent variables for explaining subsequent occupational turnover behavior will be tested. 1.1. One set of variables leading to a voluntary organizational turnover decision As part of decision paths #3 and #4b in Lee and Mitchell’s (1994, p.62) unfolding employee job (organizational) turnover model, job dissatisfaction due to negative evaluation leads to job search-related behavior which then leads to evaluating job alternatives and subsequent employee quit decisions to leave their organizations. Lee, Mitchell, Holton, McDaniel, and Hill (1999) found some support for these paths within their model. Decision path #4 is acknowledged by Lee and Mitchell (1994, p.62) to be the most similar path to general psychological models of voluntary organizational turnover (e.g., Mobley, 1977; Mobley, GriVeth, Hand, & Meglino, 1979; Steers & Mowday, 1981). In their integration of turnover theories and corresponding results, Hom and GriVeth (1995) suggested that various job factors, including stresses such as job insecurity, collectively lead to corresponding

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work-related attitudes, i.e., decreased organizational commitment and job satisfaction, which lead to job withdrawal cognitions. These cognitions, along with job search and availability/evaluation of job alternatives, then lead to voluntary job turnover. Meta-analyses of organizational turnover research (Cotton & Tuttle, 1986; GriVeth, Hom, & Gaertner, 2000) provide general support for this set of proposed variables being associated with voluntary organizational turnover. One recent prominent stress is job insecurity. In a 1995 USA Today poll (Anonymous, 1995), the top source of employee stress cited was job security or the fear of losing one’s job. Job insecurity has been deWned by Greenhalgh and Rosenblatt (1984, p.438), as “perceived powerlessness to maintain desired continuity in a threatened job situation.” Job insecurity is a negative antecedent of job satisfaction (Sverke, Hellgren, & Naswall, 2002). In their meta-analysis, GriVeth et al. (2000) found that the demographic variable, number of dependent children, was negatively related to voluntary job turnover. Increasing or at least maintaining one’s income, especially if one is the primary income source for a household, can be an issue when deciding to leave a job (Price, 1977). Therefore, number of dependent children and primary income source will be measured as common control variables. The overall variable set of common controls, and “corresponding” antecedents of work attitudes, work attitudes, turnover perceptions, and turnover intentions to leaving the organization is shown in Fig. 1. By “corresponding” is meant common or shared work referent. For example, organizational commitment has been more strongly linked to organizational withdrawal while occupational commitment has been more strongly linked to occupational withdrawal (Blau et al., 2003a; Meyer et al., 1993). Cumulatively, this suggests the following general hypothesis: Hypothesis 1. beyond the control variables, corresponding antecedents of work attitudes, work attitudes turnover perceptions, and turnover intentions will relate to leaving the organization.

Fig. 1. Presentation of corresponding sets of variables for explaining voluntary organizational versus occupational turnover.

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1.2. A corresponding set of variables leading to a voluntary occupational turnover decision The only general psychological model of voluntary career change found in the literature was presented by Rhodes and Doering (1983) in which changing one’s career, “refers to movement to a new occupation that is not part of a typical career progression” (p.631). Rhodes and Doering (1983) based their model on prior voluntary job turnover models, particularly Mobley et al. (1979), and to a lesser extent Price (1977) and Steers and Mowday (1981). Rhodes and Doering (1983, p. 633) theorized that reduced job satisfaction and career satisfaction lead to career withdrawal cognitions (including intent to change careers), which combined with search and availability of alternatives, then leads to actual career change. In identifying job satisfaction as a key initial “driver” of the career change process, Rhodes and Doering (1983) discuss the prominent role of person–work environment correspondence for aVecting job satisfaction. Work exhaustion could be characterized as one proxy measure of person–work environment correspondence. Work exhaustion has been deWned as “the depletion of emotional and mental energy needed to meet job demands” (Moore, 2000, p.336). Moore (2000) argued that work exhaustion has a wider application to jobs with lower interpersonal contact, and thus encompasses emotional exhaustion, i.e., a lack of energy and a feeling that one’s emotional resources, particularly for dealing with people, are used up. Emotional exhaustion is widely viewed as the initial key component of the burnout process (Cordes & Dougherty, 1993; Lee & Ashforth, 1996). Jackson, Schwab, and Schuler (1986) found a signiWcant relationship between emotional exhaustion and teachers’ considering leaving their education Weld. Doering and Rhodes (1989) also found that burnout was a reason given by teachers for considering an occupational change. If employees perceive that changing jobs but remaining in the same occupation will continue their basic job duties they may view simply changing jobs as going “from the frying pan to the Wre.” One “change cure” for work exhaustion may be to change occupations (Doering & Rhodes, 1989; Neapolitan, 1980). A partial empirical test of the Rhodes and Doering (1983) model, using intent to change careers as the outcome, was supported (Rhodes & Doering, 1993). In applying the Rhodes and Doering (1983) model here several variables will be modiWed. A parallel measure to job satisfaction continuing the focus on an “occupational” referent, would be occupational satisfaction or “career” satisfaction, with the understanding that career means one’s occupation (Blau, Paul, & St. John, 1993). Occupational satisfaction should correlate with, but be distinct from, occupational commitment, just as job satisfaction overlaps with, but is distinct from, organizational commitment (Mathieu & Zajac, 1990). Career commitment has been deWned as one’s attitude towards one’s profession or vocation (Blau, 1985), and it has been found to be negatively related to intent to change occupation (Blau, 1985; Lee et al., 2000). Consistent with the focus on desire to work in an occupation and the corresponding terminology used in more recent literature, career commitment is better labeled aVective occupational commitment (Lee et al., 2000), and intent to leave career and changing career (Rhodes & Doering, 1983) really means intent to leave occupation and occupational turnover (Blau et al., 1993; Lee et al., 2000). A corresponding set of variable linkages for leaving an occupation is also shown in Fig. 1. Again, primary income source and number of dependent children will serve as common control variables. Neapolitan (1980) noted the greater Wnancial obstacle to changing

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occupations generally for someone who was the primary income source for their household. Doering and Rhodes (1989) noted that not having a family to support, as well as having older, self-suYcient children, facilitated occupational change. The variable set of: antecedents of work attitudes, work attitudes, turnover perceptions, and intent to leave occupation, for aVecting voluntary occupation turnover is also shown in Fig. 1. Cumulatively, this research leads to the following hypothesis: Hypothesis 2. beyond the control variables, corresponding antecedents of work attitudes, work attitudes turnover perceptions, and turnover intentions will relate to leaving the occupation. 2. Method 2.1. Sample and procedure Medical technologists (MTs) constituted the study sample, and the data comes from a longitudinal study of the career paths of medical technologists (MTs) by the Board of Registry of the American Society for Clinical Pathology (BOR-ASCP). The study was begun in 1993 when the ASCP mailed surveys to a stratiWed sample of 2002 recently graduated MTs. These MTs had registered with the BOR-ASCP to take some type of certiWcation exam. Of the 2002 surveys mailed out, 1156 (58%) were voluntarily returned. Every year since 1993, ending in 2002, surveys were mailed to this initial respondent base of 1156 respondents. The study here draws from variables collected in 2001 and 2002 (the end of the data collection period). MTs work in a laboratory in a variety of health-related organizations (e.g., hospitals, independent laboratories). They are responsible for the accurate performance of tests (e.g., analyzing blood, urine and tissue samples; and growing cultures) that help determine the presence or absence of disease. Cordes and Dougherty (1993, p.643) characterized laboratory personnel as a lower frequency/lower intensity interpersonal contact occupation. As such it is more appropriate to measure work exhaustion, rather then emotional exhaustion (Moore, 2000). A 1995 decision by the National Labor Relations Board (1995) aYrmed that medical technology is a profession. Consistent with discussion by Lee et al.(2000, p.800) the terms “profession” and “occupation” are used interchangeably in the measures section. In 2001, 501 of 1156 (43%) MTs returned their survey containing demographic, job insecurity, work exhaustion, job satisfaction, occupational satisfaction, organizational commitment, and occupational commitment data. In 2002, 451 out of 1156 (39%) of the MTs returned their surveys containing demographic, job search, occupation search, alternative job opportunity, alternative occupation opportunity, intent to leave job, and intent to leave occupation data. Due to the national nature of the sample, surveys were mailed to respondents’ home addresses in January following the survey year, e.g., in January 2002 for the 2001 survey. Surveys answers were matched over time using respondent social security numbers. Across both surveys, based on the variables used in this study, complete data was available for only 233 MTs. Such a reduction in sample size is not uncommon (WineWeld & Tiggerman, 1990). This sample reduction was primarily due to missing data. In addition, respondents who indicated that they had “changed jobs” (e.g., intra-organization transfer)

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within the two year period were eliminated. Each year respondents were also asked to write in the name of their organization where they currently worked. If a respondent wrote in a diVerent name for the 2001 versus 2002 survey, he or she was eliminated from the study. Such steps should control for intra-organization and inter-organization work transitions (Louis, 1980) during data collection. Within this complete data set a 2001 demographic comparison on gender, age, marital status, and education level of the 233 complete-data MT sample to the 268 (501 ¡ 233) remaining MT sample indicated no signiWcant demographic diVerences. A 2001 demographic breakdown of the sample of 233 MTs showed that: their median age was 33, with a range of 29–63 years old; 82% were women; 95% had a baccalaureate degree, and 5% had an advanced degree; 66% were married; the mean number of years worked in their particular organization was four, and the mean number of years worked in the laboratory was ten. General population demographics collected by the BOR-ASCP in 2000 on 73, 471 MTs showed that 82% were female and the median age was 43. Thus this sample studied is representative for gender but is younger. Using the complete survey respondent sample of 233 MTs, permission was given to contact these individuals by the BOR-ASCP, to ask about voluntary organizational and occupational turnover in 2003, using their home address information. A very short survey, with reply paid envelop, was mailed to each respondent in January, 2004. Follow-up phone calls were made if needed. Ultimately, complete responses were given by 221 of the 233 MTs. 2.2. Measures (year collected) Control variables (2001). Primary income source was measured based on a one-item measure, i.e., “are you the primary source of income for your household?”, answered either 1 D no, 2 D yes. Number of dependent children was measured by asking respondents to indicate how many dependent children they had. Organizational (occupational) tenure was measured by asking respondents the number of years each had worked in the organization (laboratory). Job insecurity (2001) was measured using seven items focusing on the perceived threat of involuntary job loss over projected time ranges, consistent with Sverke et al. (2002). Survey constraints prevented using the more comprehensive 57-item Ashford, Lee, and Bobko (1989) measure. Sample items are: “I am concerned that I may lose my job next year,” and “I am concerned that my job will be negatively aVected by my institution’s downsizing in the next three years.” Unless otherwise noted, all items were responded to using a fourpoint scale, where 1 D strongly disagree, 2 D disagree, 3 D agree, 4 D strongly agree. Research has shown that the proportion of the scale used in a four-point response format is no diVerent than more common Wve-, six-, and seven-point formats (Matell & Jacoby, 1972). For all multi-item scales items were averaged to form scale scores. Work exhaustion (2001) was measured using a shortened adapted seven-item scale based on the Gillespie–Numerof Burnout Inventory (Seltzer & Numerof, 1988). Sample items included are: “my job has me at the end of my rope,” and “I am unable to get out from under my work.” This seven-item scale showed good internal consistency in an earlier study (Blau, Tatum, & Ward-Cook, 2003b). A four-point frequency-based response scale was used, where 1 D hardly ever (once every few months or less); 2 D rarely (about once a month); 3 D sometimes (at least once a week); and 4 D frequently (at least once a day).

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Job satisfaction (2001) was measured using the Job Diagnostic Survey (Hackman & Oldham, 1975). The fourteen items asked respondents to indicate: “how satisWed they were with diVerent aspects of their job,” including co-workers, personal growth options, supervision, and salary. Items were responded to using a four-point scale, 1 D very dissatisWed, 4 D very satisWed. Occupational satisfaction (2001) was measured using Wve items adapted from the Wveitem career satisfaction scale by Greenhaus, Parasuraman, and Wormley (1990). Inspection of the items used by Greenhaus et al. (1990, p.86) suggested that “career” means within one’s organization, e.g., satisfaction with salary, advancement. Accordingly, all Wve of the items within Greenhaus et al. (1990) were modiWed to have an occupational referent. A sample item is: “I am satisWed with the success I have achieved in my occupation.” Organizational commitment (2001) was measured using an adapted version of Meyer et al.’s (1993) six-item measure of aVective organizational commitment (OC). The only change made to relevant items was to word them all positively. Some researchers (e.g., Jackson, Wall, Martin, & Davids, 1993) have argued that using reverse-scored items, to balance the polarity of items within a scale, can introduce systematic error to a scale. A sample items is: “I feel emotionally attached to this organization.” Occupational commitment (2001) was also measured using an adapted version of Meyer et al.’s (1993) original six-item aVective occupational commitment scale. In addition to positively wording all items (Jackson et al., 1993), the occupation referent was changed from nursing (Meyer et al., 1993) to “medical technology.” A sample item is: “I am happy to have entered the medical technology profession.” Job search (2002) was measured using two items. A sample item is: “to what extent did you job search in 2002 in order to leave your current employer.” Similar to Boswell, Boudreau, and Dunford (2004), a four point response scale was used, where 1 D no extent, 2 D a little extent, 3 D some extent, and 4 D great extent. Occupational search (2002) was also measured using a similar two-item measure. A sample item is: “to what extent did you job search in 2002 in order to leave the medical technology profession.” The same 4-point response scale as with job search was used. Alternate job opportunities (2002) were measured using a two-item measure adapted from Price and Mueller (1986). A sample item is: “there are alternative jobs available for me if I decide to leave my current job.” Alternate occupational opportunities (2002) were measured using a two-item measure adapted from Carson et al. (1995) career entrenchment measure. A sample item is: “there are alternative occupations available for me if I decide to leave my current profession.” Intent to leave organization (2002) was measured using three items adapted from Blau (2000). A sample item is “I intend to leave this organization as soon as possible.” Intent to leave occupation (2002) was measured using three items also taken from Blau (2000), e.g., “I intend to leave the medical technology profession as soon as possible.” Voluntary organizational turnover (2003) was measured using one-item, “did you voluntarily change organizations, but still remain in medical technology,” and a No D 1, Yes D 2 response scale was used. Of the 221 respondents, 47 (21%) said they had voluntarily changed organizations in 2003. To try and ensure that respondents who said “yes” in fact did change organizations, they were asked to write in the name of the new organization where they worked during 2003. This 2003 organization name was compared to the organization name written in 2002, and this data indicated that all 47 did change organizations between 2002 and 2003.

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Voluntary occupational turnover was measured for 2003 using one-item, “did you voluntarily change and leave the medical technology profession during 2003,” and a No D 1, Yes D 2 response scale was used. Of the 221 respondents, 25 (11%) indicated that they had changed professions in 2003. To try and ensure that respondents who said “yes” in fact did change professions, (either a new occupation or commitment to a degree in a new Weld, Rhodes & Doering, 1983), they were asked to write in what their new job title was. All 25 “changers” did, and the new titles included: sales manager, dental student, science teacher, data manager, claims processor, epidemiologist, clinical systems specialist, medical student, and residential manager. 3. Results 3.1. Background Given the large amount of missing data, an analysis was done to determine if subject attrition was biasing the study results. Goodman and Blum (1996) have recommended using logistic regression because it models the probability of being included in one of two response categories, remaining in or leaving the sample. Results indicated that none of the study variables was signiWcantly related to staying in versus leaving the sample. Means, standard deviations, reliabilities and correlations among study variables are reported in Table 1. Variable means are based on the response scale used for that scale. All multi-item scales had internal consistencies (Cronbach alpha) of at least .70 (Nunnally, 1978). Correlation results indicate suYcient discriminant validity between corresponding referents and type of turnover behavior. There are two “measurement issues” inXating the correlation of .57 between organizational—occupational turnover, i.e., for the majority of MTs sampled, there was no movement in either changing organizations or occupations, plus if an employee leaves their occupation they typically leave their present organization. However by deWnition (Louis, 1980), there is a diVerence in these types of turnover. 3.2. Test of hypotheses Since the two dependent variables, voluntary job and occupational turnover, are each dichotomous, it is more appropriate to test each hypothesis using logistic regression (Norusis, 1994). Variables will be entered in chronological order, i.e., Wrst 2001, then 2002, for explaining each type of turnover. Closer time proximity of an antecedent to the actual turnover behavior can increase its relationship to the behavior (Mitchell & James, 2001). The logistic regression results for testing H1 are presented in Table 2. Of the 2001 corresponding variables entered in Step 1, only job insecurity and job satisfaction were signiWcantly related to subsequent organizational turnover. Primary income source, number of dependent children, organizational tenure, and organizational commitment were non-signiWcant. For Step 2, 2002 job search and intent to leave organization were signiWcant, but alternate job opportunities were not. These results partially support H1, and 23% of the variance in organizational turnover was accounted for. The results are more modest for H2, and are shown in Table 3. For Step 1, of the four 2001 corresponding variables entered, only two were signiWcant, work exhaustion and occupational satisfaction. Primary income source, number of dependent children, occupational tenure, and occupational commitment each had a non-signiWcant relationship to voluntary

Table 1 Means, standard deviations, reliabilities and correlations among study variables Variable

M a

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

1.46 .50 (NA) 1.25 1.07 ¡.09 (NA) 4.02 3.71 .12 10.41 6.86 .11 1.99 .66 .08 2.08 .67 .13 2.84 .53 ¡.07 2.79 .55 .05

.15 (NA) .07 .25 (NA) .10 ¡.17 ¡.06 (.90) .03 .08 .14. .20 (82) ¡.08 .13 .09 ¡.28 ¡.20 (.93) .03 .15 .04 ¡.25 ¡.19 .39 (.88)

2.61

.48

.10

.06

.08

.16 ¡.17 ¡.22

.41

.36

(.81)

2.73

.51

.11

¡.02

.06

.18 ¡.14 ¡.20

.31

.38

.45

1.66 1.59

.74 ¡.05 .73 ¡.07

¡.04 ¡.05

¡.15 ¡.17

¡.05 ¡.06

.27 .18

.19 ¡.35 ¡.26 ¡.11 ¡.12 (.75) .12 ¡.13 ¡.10 ¡.07 ¡.08 .22 (.71)

1.43 1.38

.70 ¡.12 .67 .04

¡.09 ¡.07

¡.10 ¡.06

¡.13 ¡.11

.12 .10

.17 ¡.08 ¡.06 ¡.24 ¡.21 .14 ¡.03 ¡.02 ¡.11 ¡.13

.20 .04

.05 .13

1.88

.62 ¡.08

¡.10

¡.15

¡.08

.22

.16 ¡.30 ¡.27 ¡.07 ¡.06

.21

.17

.05

.04

(.81)

1.53

.59 ¡.11

¡.12

¡.06

¡.13

.15

.21 ¡.20 ¡.15 ¡.31 ¡.29

.05

.06

.21

.23

.38

(.83)

1.21

.38 ¡.02

¡.03

¡.10

¡.04

.22

.18 ¡.29 ¡.25 ¡.14 ¡.12

.17

.15

.10

.11

.32

.15

(NA)

1.11

.40 ¡.04

¡.08

¡.05

¡.12

.14

.21 ¡.12 ¡.11 ¡.22 ¡.19

.11

.06

.18

.15

.10

.24

.57

G. Blau / Journal of Vocational Behavior 70 (2007) 135–148

1. Primary income source, 01 2. Number of dependent children, 01 3. Organizational tenure, 01 4. Occupational tenure, 01 5. Job insecurity, 01 6. Work exhaustion, 01 7. Job satisfaction, 01 8. Organizational commitment, 01 9. Occupational satisfaction, 01 10. Occupational commitment, 01 11. Job search, 02 12. Alternate job opportunities, 02 13. Occupational search, 02 14. Alternate occupation opportunities, 02 15. Intent to leave organization, 02 16. Intent to leave occupation, 02 17. Voluntary organizational turnover, 03a 18. Voluntary occupational turnover, 03 a

SD

(.87)

(.72) .18 (.71)

(NA)

Note. N D 228, r > .13, p < .05; r > .17, p < .01. 01 D 2001; 02 D 2002; 03 D 2003, internal consistencies in diagonal; NA, not applicable. a Dichotomous variable, 1 D No, 2 D Yes, N D 221.

143

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Table 2 Hierarchical logistic regression using voluntary organizational turnover as the dependent variablea Variable

Beta

Step 1 Primary income source, 01 Number of dependent children, 01 Organizational tenure, 01 Job insecurity, 01 Job satisfaction, 01 Organizational commitment, 01

¡.24 ¡.08 ¡.10 .20¤ ¡.30¤ ¡.22

Step 2 Job search, 02 Alternative job opportunities, 02 Intent to leave organization, 02 ¡2 Log likelihood 2 (9 degrees of freedom) R2 (Cox & Snell) Percentage of cases correctly classiWed

.23¤ .39 .45¤¤

Standard error .49 .35 .28 .09 .13 .15 .10 .21 .18 114.33 43.81¤¤ .23 88%

Note. N D 221. a Standardized coeYcients for all continuous variables. b 01 D 2001, 02 D 2002. ¤ < p .05 (two-tailed). ¤¤ p < .01. Table 3 Hierarchical logistic regression using voluntary occupational turnover as the dependent variablea Variable

Beta

Step 1 Primary income source, 01 Number of dependent children, 01 Occupational tenure, 01 Work exhaustion, 01 Occupational satisfaction, 01 Occupational commitment, 01

¡.18 ¡.09 ¡.12 .23¤ ¡.25¤ ¡.20

Step 2 Occupational search, 02 Alternative occupation opportunities, 02 Intent to leave occupation, 02 ¡2 Log likelihood 2 (9 degrees of freedom) R2 Percentage of cases correctly classiWed

.21 .31 .28¤

Standard error .39 .28 .17 .11 .12 .13 .15 .20 .12 172.26 95.26¤¤ .15 82%

Note. N D 221. a Standardized coeYcients for all continuous variables. b 01 D 2001; 02 D 2002. ¤ p < .05 (two-tailed). ¤¤ p < .01.

occupational turnover. For Step 2, only 2002 intent to leave occupation was signiWcant, while occupational search and alternative occupation opportunities were not. Overall, the results partially support H2 and 15% of the variance in occupational turnover was accounted for.

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4. Discussion Overall the results of this study support voluntary organizational turnover versus occupational turnover as separate types of interrole transitions (Louis, 1980). A literature search suggests that this is the Wrst study testing these types of turnover using a corresponding variable set framework for each type of turnover. The results of the study are partially supportive of previous voluntary job (organizational) turnover Wndings (Cotton & Tuttle, 1986; GriVeth et al., 2000) in Wnding job insecurity, job satisfaction, job search and intent to leave the organization as signiWcant corresponding correlates. Study results are consistent with other research Wnding job satisfaction to be a key “driver” of voluntary employee turnover (Hom & Kinicki, 2001). It was disappointing not to Wnd organizational commitment to be a signiWcant antecedent. One study limitation was only measuring the aVective dimension of organizational commitment, and not also the normative and continuance commitment dimensions (Meyer et al., 1993). This study is believed to be the Wrst test of the Rhodes and Doering (1983) model using occupational turnover data. Prior research has used intent to change/leave career (occupation) as the outcome variable (e.g., Blau et al., 2003a; Carson et al., 1995; Rhodes & Doering, 1993). Additional research design strengths include having a prospective study design over a several year period, controlling for intra-organizational transfers, and respondent organizational stability, as well as behavioral “checks” on the self-reported turnover data. Occupational satisfaction also emerged as a signiWcant correlate, suggesting that satisfaction may be an important “driver” variable for both organizational and occupational turnover. In addition, work exhaustion was found to be signiWcant, consistent with previous qualitative work on career changers (Doering & Rhodes, 1989). Intent to leave occupation was also a signiWcant antecedent of occupational turnover. Across both types of turnover, signiWcance consistency of the satisfaction and intent variables’ supports a “corresponding variable set framework” for explaining each type of turnover. Unfortunately, similar to organizational commitment, occupational commitment did not emerge as a signiWcant correlate of occupational turnover. Again, only measuring aVective and not the normative or continuance dimensions for occupational commitment (Meyer et al., 1993) must be noted. A limited amount of variance was explained for each type of turnover. It can be argued that measure “crudeness” (e.g., primary income source) and restricted measure range (e.g., number of dependent children) contributed to this. Although logistic regression was an appropriate data analytic technique (Norusis, 1994), the improvement in classiWcation accuracy for organizational turnover was marginal and even less for occupational turnover. Given the 21% rate of organizational turnover, and the predictive accuracy of 88% with nine variables, this suggests a very modest gain of 9% (21% ¡ 12%) using the nine correlates studied. The improvement for occupational turnover is worse. An alternate approach to consider is survival analysis (Mossholder, Settoon, & Henagan, 2005). However, survival analysis would require organizational and occupational turnover data access over the time period needed. In addition, the model used was underspeciWed since some relevant variables were not included. Unfortunately labor market inXuences such as unemployment rate, along with job embeddedness, withdrawal expected utility and comparing alternatives were not

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measured, and prior research suggests that each variable can be part of the job turnover process (Hom & GriVeth, 1995; Hom & Kinicki, 2001; Mitchell, Holton, Lee, Sablynski, & Erez, 2001). More recent employee organizational turnover research (Mossholder et al., 2005) found that two relational variables, i.e., network centrality or coworker ties, and interpersonal citizenship behavior, were each important impediments to voluntary employee turnover. Including at least several of these variables would have probably increased the amount of voluntary job turnover variance accounted for. One reason for not including these variables, aside from survey length constraints, was trying to create “corresponding variable sets” for comparing voluntary job versus occupational turnover. Comparable scales for occupational: embeddedness, withdrawal expected utility and comparing alternatives, as well as occupational relational variable scales, await development. Looking at future research directions, testing the “robustness” of the job insecurityorganizational turnover and work exhaustion-occupational turnover relationships found here using other measures of job insecurity (e.g., Sverke et al., 2002) and work exhaustion (e.g., Schaufeli, Leiter, & Kalimo, 1995) is important. It is important to also acknowledge that both types of turnover, job and occupational, were self-report and had limited base rates (Adkins, Werbel, & Farh, 2001). Another limitation to acknowledge is the sample, including its relative homogeneity, i.e., primarily female medical technologists with a bachelor’s degree. In addition, during the time period studied, there was a currently a shortage of qualiWed laboratory professionals, including MTs, in the United States (Ward-Cook & Tamar, 2000). As such, this may explain the somewhat lower overall level of job insecurity indicated by the sample. Such a shortage could be expected to encourage increased voluntary job turnover of MTs. High demand employees are arguably in a stronger position to capitalize on their human capital and obtain external job opportunities (Hom & GriVeth, 1995). In addition, such a labor shortage may also contribute to greater work exhaustion in one’s present job situation (Cordes & Dougherty, 1993). Therefore, testing the generalizability of the study’s Wndings is important. The research design only allowed for year-long measures to be collected. Individuals may well use shorter job and occupation search cycles (Steel, 2002), and such cycles may be more sensitive to explaining a greater amount of voluntary organizational and occupational turnover. DiVerentiating between passive versus active job (occupational) search may also help to tease out stronger relationships with each type of turnover (Blau, 1993). From a practical perspective, what is important about these results? The higher intent to leave and actual organizational turnover means, versus comparable intent and actual occupational turnover variable means, support practitioner Wndings about greater frequency of organizational turnover (Bolles, 2006). One concern is how an organization can “realistically defuse” job loss insecurity perceptions. Adkins et al. (2001) discuss the importance of organizations providing timely, accurate and suYcient information about any restructuring. The aftermath of any restructuring is important to monitor, including task overload for survivors (Cascio, 1993), which may help lead to their work exhaustion. Such exhaustion may contribute to not just organizational but occupational turnover. To conclude, it is hoped that the presented Wndings will stimulate additional study comparing the voluntary organizational versus occupational turnover processes. Continued study may help researchers to understand how people decide whether to engage in one type of turnover versus the other.

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