TURNOVER AMONG PROFESSIONALS: A LONGITUDINAL STUDY OF AMERICAN LAWYERS

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TURNOVER AMONG PROFESSIONALS: A LONGITUDINAL

STUDY OF AMERICAN LAWYERS

Human Resource Management, Spring 1999, Vol. 38, No. 1, Pp. 61–75

© 1999 John Wiley & Sons, Inc. CCC 0090-4848/99/01061-15

Aaron Cohen

Turnover intentions and actual turnover among lawyers are examined in an attempt to clarify whether common models of turnover can be applied to this professional occupation, which has rarely been examined. Three models are explored in their relation to turnover: personal charac-teristics, work-related variables, and nonwork domain variables. The data are based on responses to the National Survey of Career Satisfaction/Dissatisfaction of the American Bar Association, 1984 and 1990. The findings reveal that work-related variables were the main determinants of turnover intentions, and personal characteristics together with nonwork domain variables were the main determinants of actual turnover.1 © 1999 John Wiley & Sons, Inc.

Introduction

Employee turnover is one of the aspects most studied in organizational research (Mitra, Jenkins, & Gupta, 1992), but very little is known regarding possible causes of turnover among professionals. An important question is whether turnover processes and determi-nants explored in a variety of samples can be applied to professionals or if different models are needed for them. The sparse and relatively old research on this issue suffers from several limitations. First, most research applied the common conceptual framework of the poten-tial conflict between cosmopolitan and local role orientations (Bartol, 1979; Lachman & Aranya, 1986), which was found to explain very little of professionals’ turnover (Bartol, 1979). Second, many studies tested turnover intentions and not actual turnover (Lachman & Aranya, 1986; Sorensen & Sorensen, 1974).

Third, most research on professionals in gen-eral and turnover among professionals in par-ticular is based on a limited number of professional groups. For example, a meta-analysis that tested the relationship between professional and organizational commitment (Wallace, 1993) found no data regarding phy-sicians and lawyers, who are defined as a clas-sic professional group (Gunz & Gunz, 1994), and relied mostly on data for accountants.

A related question is why lawyers have rarely been examined regarding their work at-titudes and behaviors. One reason may be the difficulty in accessing this population, much of which is dispersed in small offices. The few studies performed on the legal profession ex-amined issues such as professional and orga-nizational commitment (Gunz & Gunz, 1994; Wallace, 1995a; 1995b), burnout (Jackson, Turner, & Brief, 1987), and sex discrimina-tion (Foot & Stagner, 1989; Laband & Lentz,

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1993). Thus, our knowledge about work be-haviors of this interesting professional group is limited. By expanding it, this article may contribute to widening the traditional scope of the literature, which tends to concentrate on employees in manufacturing industries or large (100 or more employees) bureaucratic organizations or both, to cover different, much smaller work settings. Law firms are consid-ered large if they consist of 20 or more law-yers (Wallace, 1995b). Settings similar to law firms are prevalent in other professional oc-cupations such as dental hygienists (Mueller, Boyer, Price, & Iverson, 1994) and accoun-tants (Lachman & Aranya, 1986). It is impor-tant, therefore, to examine whether the same mechanisms and factors that affect employ-ees’ turnover in large organizations also oper-ate in smaller and much less complex service sector organizations such as law firms. Such research will increase understanding of hu-man resource hu-management processes and methods in a variety of work settings.

The contribution of this study is three-fold. First it will test whether theories and findings about turnover obtained from other occupations and traditionally based on large organizations are applicable to much smaller professional organizations that constitute a typical work setting of lawyers. Second, these data will test actual turnover in addition to turnover intentions. While most research tested turnover models in relatively short time intervals, one year in most cases, this research will examine the effect of independent vari-ables on long-term turnover measured five years after the measurement of the indepen-dent variable. This will clarify whether the interval between measurements of turnover affect the relationship between determinants and turnover. Third, the effect of nonwork variables will be tested, together with the more traditional personal and situational de-terminants of turnover. Mobley (1982) ar-gued that the organization’s analysis of turnover should include a diagnosis of nonwork values and roles and their relation to job behavior. The importance of testing nonwork domain variables is strengthened by research that has shown the effect of nonwork domains on professionals’ work at-titudes (Bedeian, Burke, & Moffett, 1988; Kirchmeyer, 1993).

Research Model and Hypotheses Personal and Work-Related Variables

Turnover models commonly tested per-sonal and situational variables for their rela-tionship with turnover (Lee & Mowday, 1987; Bartol, 1979; Cotton & Tuttle, 1986). Four personal characteristics that are common an-tecedents of turnover and are widely used in turnover research will be tested here: gender, tenure, marital status, and age. Research has generally found a negative relationship be-tween turnover and the variables of age and tenure (Mobley, Griffeth, Hand, & Meglino 1979; Cotton & Tuttle, 1986). Younger em-ployees with fewer years in the organization are more likely to leave because of the low cost of leaving, having acquired the experience that makes it easier to find a better job (Cohen, 1993). Research evidence also shows that fe-males tend to leave the organization more than males, probably as a result of more family re-sponsibilities; and that married people tend to leave the organization less than nonmarried as a result of heavier responsibilities and their greater need for stable employment than nonmarried people (Mobley et al., 1979; Cot-ton & Tuttle, 1986).

The arguments regarding causes for turn-over among professionals rely on the assump-tion that the basic conflict between loyalties experienced mostly by professionals negatively affects their work attitudes, and hence leads to turnover intentions and probably to actual turnover (Lachman & Aranya, 1986; Sorensen & Sorensen, 1974). The work-related variables tested here provide an indirect assessment of such conflict (Sorensen & Sorensen, 1974; Bartol, 1979; Lachman & Aranya, 1986). Po-sition in the firm will be examined because there is a fundamental difference between a partner’s ties to a law firm and those of an associate. Partnership status normally implies (partial) residual claimancy, whereas associ-ates are employees with no claims to the firm’s profits. The testing of position is particularly important in light of Lachman and Aranya’s (1986) findings that their turnover intentions model has a different effect for partners and sole practitioners from that for employees in accounting firms. In addition, Sorensen and Sorensen (1974) found an increase in bureau-It is important, therefore, to examine whether the same mechanisms and factors that affect employees’ turnover in large organizations also operate in smaller and much less complex service sector organizations such as law firms.

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cratic orientations and a decrease in profes-sional orientations from lower to higher posi-tions, junior through partner. Thus, it can be expected that partners and associates will have different considerations regarding turnover, and the variable position was included to con-trol for these differences.

Job satisfaction has been acknowledged in turnover research as systematically associ-ated with turnover (Lee & Mitchell, 1991; Tett & Meyer, 1993). Employees dissatisfied with their work setting are expected to leave more readily than those who are satisfied. Meta-analysis results indicate relatively consistent negative correlations between job satisfaction and turnover (Tett & Meyer, 1993; Cotton & Tuttle, 1986). Moreover, job satisfaction is one of the most frequently studied variables in re-lationship to turnover among professionals (Lachman & Aranya, 1986; Sorensen & Sorensen, 1974). Mueller et al. (1994) found a strong negative effect of job satisfaction on dental hygienists’ turnover. This important finding strongly justifies testing this variable in our study because dental hygienists’ work setting is similar to lawyers’, namely a small professional organization.

Studies have shown that the less favor-ably the job is perceived, the higher the turn-over. For example, variables such as job autonomy (Marsh & Mannari, 1977) or task repetitiveness (Price & Mueller, 1981; Bartel, 1982) were found to be related to turnover. Thus, a variable representing the amount of professional challenge provided in the job set-ting will also be tested here. This variable seems to represent the idea of professional-ism even better than job satisfaction does, and so is expected to have stronger effect on turn-over than does job satisfaction. Professionals are expected to look for and work in jobs that will meet their professional needs, and a lower professional challenge can demonstrate some conflict between these values and the organi-zation that was not able to meet their needs. Mueller et al. (1994) found, for example, that job variety had a negative effect on dental hy-gienists’ turnover. This finding provides addi-tional support for the inclusion of professional challenge in this study. Another variable fre-quently studied in its relationship to turnover is employee performance (Barrick, Mount, & Strauss, 1994). Employee job performance

may influence job attitudes and ultimately turnover (McEvoy & Cascio, 1987). Good performance will lead to positive attitudes and to a stay decision. For example, Marsh and Mannari (1977) found a negative relationship between perceived performance and turnover, among Japanese electrical company employ-ees. Birnbaum and Somers (1993) found a weak effect of actual performance on turn-over. They concluded, however, that further research is needed before firm conclusions can be made. In light of the significant effect of perceived turnover it would be interesting to replicate Marsh and Mannari’s findings and test the possibility that the perception of per-formance is a stronger determinant of turn-over than is actual performance.

Previous research mentioned above has shown the importance of each group of vari-ables proposed here (Bartol, 1979; Cotton & Tuttle, 1986). Although very little is known about work attitudes and behaviors in the le-gal profession, the basic expectation is that previous theory and findings for other profes-sions will hold here, too. Bartol (1979) found that work-related variables had a stronger effect on professional turnover than did the personal determinants. Thus personal char-acteristics (e.g., gender, marital status, tenure, age) are expected to be related to turnover, but work-related variables (e.g., job satisfac-tion, posisatisfac-tion, professional challenge, per-ceived performance) are expected to add to the explained variance of turnover beyond the effect of the personal characteristics, and in fact to show stronger effect on turnover than the personal variables.

Hypothesis 1: Personal characteristics will be related to turnover intentions and actual turnover, but work-related variables will add to the explained variance beyond the effect of the personal characteristics. External Factors

In all streams of turnover research, very little attention has been paid to the effect of nonwork domains on turnover. Mobley (1982) argued that as dual-career families become more prevalent, as nonwork values become more central, and as more young people at-tach less importance to a stable and secure

In all streams of turnover research, very little attention has been paid to the effect of nonwork domains on turnover.

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career, prediction and understanding of turn-over should include such nonwork variables. While Mowday, Porter, and Steers (1982) ar-gued that the influence of nonwork factors on employee turnover remains perhaps one of the richest areas for future work, the relationships between individual nonwork-related variables and turnover are often neglected (Mobley, 1982; Mowday et al., 1982). A notable excep-tion is the study of Mueller, et al. (1994) who included in their turnover model nonwork variables such as city size and kinship respon-sibility and found that while city size had no significant effect on dental hygienists, in-creased kinship responsibility reduced their turnover by first increasing their intent to stay. In light of growing evidence that shows strong effect of nonwork domain variables on professionals’ attitudes (Bedeian et al., 1988; Kirchmeyer, 1993), four nonwork domain vari-ables will be tested here. First, a variable rep-resenting the amount of annual vacation allowed by the employer will be tested with the expectation that more vacation should re-sult in lower turnover. Hall and Richter (1988) suggested that organizations should acknowl-edge and value employees’ nonwork lives. The way the organization supports the nonwork domains of its employees is expected to affect their withdrawal intentions (Mobley, 1982). Allowing more time for vacation represents one way of demonstrating such support. Sec-ond, nonwork activities will be tested, with the expectation that those active in nonwork or-ganizations, such as social and community organizations, will tend to remain in the orga-nization because of their stronger ties in the community (Kirchmeyer, 1995). Third, spouse’s employment status will be tested, with the expectation that a lawyer whose spouse has a full-time job will find leaving the orga-nization to be a much more difficult decision than will one whose spouse either does not work or works only part time. Conceptual sup-port for testing this variable was provided by Mowday et al. (1982) who argued that an iden-tifiable circumstance in which one may not like a particular job but does not seek termi-nation is where a spouse is limited geographi-cally to a certain region and alternative employment is scarce. The fourth nonwork variable tested here is the amount of satisfac-tion with the locasatisfac-tion of the office or the town

where the office is located. It is expected that those who are more satisfied with the loca-tion will tend to remain in the organizaloca-tion more than those who are not.

Hypothesis 2: Nonwork domain variables will be related to actual turnover and turnover intentions and will add to the explained variance beyond the effect of personal and work-related characteristics. Actual Turnover Versus Turnover Intentions

Mobley et al. (1979) and Bergh (1993a; 1993b) argued that the effect of differing lengths of time between the measurement of independent variables and turnover behavior is infrequently studied, and this topic needs additional research. Morita, Lee, and Mowday (1993) and Marsh and Mannari (1977) dis-cussed the methodological difficulty regarding the time span between the measurement of the dependent variables and the measurement of turnover. Morita et al. (1993) argued that if the time interval is too short, the process being monitored or observed may not have sufficient time to unfold and too little criterion variance may result. If the measurement is too long, fac-tors affecting the process that are not under study may arise, such as firings and layoffs, and too little criterion variance may again result. Marsh and Mannari (1977) argued that short-term turnover maximizes the stability of the independent variables, and work attitudes in particular, but allows inadequate time for op-portunities to leave. Long-term turnover allows more time for opportunities to appear, but also introduces increasing probability that commit-ment levels may change. In their study, turn-over was measured in four time periods, at one, two, three, and four years after the data collec-tion. They found that when the turnover inter-val was longer, the independent variables were better able to predict who would leave. They concluded that the longer time interval provided more opportunities to leave for those who had negative attitudes toward the organization, and this factor more than compensated for the measurement error introduced if employees’ attitudes changed after the initial measurement. Despite the important methodological and practical implications of the above arguments, most research continued to use a short-term Long-term

turnover allows more time for opportunities to appear, but also introduces increasing probability that commitment levels may change.

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turnover approach by measuring turnover af-ter one year. Following Marsh and Mannari’s (1977) finding, it is expected that actual turn-over measured five years after the collection of data on the independent variables will be better explained by the independent variables than will turnover intentions measured at the same time. While the variable turnover inten-tions is not equivalent to short-term turnover, it is the best replacement for such turnover (Steel & Ovalle, 1984).

Hypothesis 3: Actual turnover measured five years after the collection of the inde-pendent variables will be better explained by the research model than turnover intentions measured at the same time as the independent variables.

The above arguments assume that all three groups of variables will contribute to the expla-nation of turnover; however, one can expect that the magnitude of the effect of the three groups of variables will differ for actual turnover and for turnover intentions because of the long time in-terval for the measurement of actual turnover. Marsh and Mannari (1977) themselves argued that the main reason for shorter intervals is that short-term turnover maximizes the stability of the independent variables and work attitudes in par-ticular. That is, in shorter intervals, fewer changes in work attitudes are expected and these fewer changes should increase the prediction of turn-over. This logic leads to two sets of expectations. First, work-related variables will have a stronger effect on turnover intentions than on actual turn-over. Many changes in work attitudes are likely to have occurred during the six-year interval. For example, a person who reported low job satisfac-tion at the time of the survey might have moved to another job, which would cause a higher level of job satisfaction. The prediction was that this person would leave the organization; but as a re-sult of the long interval until the measure of turn-over, changes in the level of job satisfaction were not captured, and the result is an error in the prediction of turnover. Such a scenario is not pos-sible in the case of turnover intentions measured at the same time as job satisfaction. Hom and Griffeth’s (1991) findings that the effect of job satisfaction on turnover decreases over time sup-port the above argument.

On the other hand, the effects of the

per-sonal characteristics and nonwork domain variables need more time to manifest them-selves. For example, older people will need more time for their job search because their age makes them less attractive in the labor market (Becker, 1960). Also, a longer time interval is needed to perceive the effect of the nonwork domain variables. A person might want to leave the organization but her/his ties to the community, demonstrated in nonwork activities in the community or satisfaction with the location of the workplace might make it difficult, and such employees will require a longer time to make up their minds.

Hypothesis 4a: Work-related variables will have a stronger effect on turnover inten-tions than personal characteristics and nonwork domain variables.

Hypothesis 4b: Personal characteristics and nonwork domain variables will have a stronger effect on actual turnover than will work-related variables.

Finally, two research models will be exam-ined to test the above hypotheses. In the first model, presented as Model 1 in Figure 1, the independent variables grouped into personal, work-related, and nonwork variables will be re-gressed on the dependent variable, turnover in-tentions. The second model, presented as Model 2 in Figure 1, includes the same basic indepen-dent variables but differs in two characteristics from Model 1. First, the dependent variable in Model 2 is actual turnover and not turnover intentions as in Model 1. Second, turnover in-tentions, the dependent variable in Model 1, is another independent variable in Model 2 based on the literature that showed strong effect of turnover intentions on actual turnover (Mobley, 1982; Mobley, et al., 1979).

Method Survey Design and Participants

The data are based on responses to the National Survey of Career Satisfaction/Dissat-isfaction, Wave 1, 1984, and Wave 2, 1990, American Bar Association (ABA) Young Law-yers Division (Hirsch, 1992). Hirsch (1992) provides a full description of the research de-sign, the survey administration, and the research

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participants. To summarize, a random probabil-ity sample of 3018 lawyers of all ages was drawn from ABA members and nonmember lists cov-ering 569,706 lawyers (the net sample was 2967 because 51 were defined as out of scope, e.g., deceased, not resident in the 50 states). The survey questions addressed aspects of the re-spondents’ work environment, job history, edu-cational background and demographic characteristics. Hirsch (1985; 1992) argued that demographic characteristics of the sample were representative of the entire legal profession. Most of the data were collected by mail survey and the rest by phone. The total response rate was 76.9%, where 55.6% of the participants re-sponded to the mail survey and 21.3% to the phone survey. In 1990 a decision was made to perform a two-part follow-up of the 1984 study. The first part was a longitudinal study of all in-dividuals who responded to the 1984 study, and the second was based on a sample of all indi-viduals who became members of the bar after 1983. This research focused only on the panel study and not on the cohort one. The net sample for the panel study was 2245. Seven hundred-fifty-three responded to the mail survey (34%), and 703 to the phone survey (31%). The phone survey, as in Wave 1, was a very short version of the mail survey and in some cases included only biographical data. Moreover, some of the par-ticipants who responded to the mail survey in Wave 1 responded by telephone in Wave 2. This procedure together with the length of the ques-tionnaires helps to explain the large number of missing values and the large variety in the sample size across different variables seen in Table I. Finally, there was some biographical informa-tion regarding the respondents: 19% were fe-males; 41% were partners or in executive positions; and 59% were in other positions such as senior or junior associates. Twelve percent were in their first year of employment in their current firm; 30% in their 1st–3rd years; 35% in their 4th–9th years; and 23% had worked there for 10 years or more.

The method of measurement of the inde-pendent and deinde-pendent variables is summa-rized in Table I. Both objective and attitudinal indicators are used to test the applicability of the models.

At the end of the article there is a detailed description of the data analyses that were car-ried out.2

Findings Correlation Analysis

Table II presents the intercorrelations among the research variables together with the means, the standard deviations, and the reliabilities. In addition, because of the miss-ing values problem, the size of the sample for each correlation is also presented. The corre-lations among the independent variables do not exceed .65, and most of them are quite low, thus reducing the possibility of a multicollinearity problem. An interesting pat-tern emerges for the correlations between the dependent and the independent variables: The variable “intention to leave” regardless of op-tion was related significantly to most of the independent variables. “Intention to leave if option was available” had significant correla-tions mostly with all the work-related variables, also with one personal characteristic (age) and one nonwork domain variable (amount of va-cation allowed). Actual turnover had modest correlations with four independent variables (gender, age, perceived performance, spouse’s employment status). Note that job satisfaction, which had high significant correlations with the two dependent variables (-.40; -.48; p<.001), is not related to actual turnover. This finding supports Hypotheses 4a and 4b, which predicted a weak effect of work-related vari-ables on actual turnover and a strong effect of work-related variables on turnover intentions. Logistic Regression Analysis

Table III presents the logistic regression results for the two turnover intention variables, and Table IV for actual turnover. Hypothesis 1, which predicted an incremental effect for the two subsets of variables, is supported by the data when turnover intentions are the de-pendent variables. Results in Table III show that for the two turnover intention variables, personal characteristics had a significant ef-fect (see Figure 1) as demonstrated by the sig-nificant chi-square. All personal variables had a significant effect in the case of turnover in-tentions regardless of options. Tenure and age had significant effect for turnover intentions if option was available. The work-related variables entered in step 2 contributed signifi-All personal variables had a significant effect in the case of turnover intentions regardless of options.

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Measurement of Research Variables.

Variables Categories or measurement procedures Independent variables

Gender 0=female; 1=male

Marital status 0=nonmarried; 1=married

Tenure 1=less than one year; 2=1–3 years; 3=4–9 years; 5=10 or more years Age The actual age of the respondents as reported by their year of birth Position in the firm 1=partner/executive position; 0=all other positions

Job satisfaction A scale consisting of 18 items; each item was rated on a three-point scale: –1 (not satisfied), 0 (neutral), 1 satisfied), and then multiplied by its perceived importance as reported by the respondents on a scale from 1 to 4; sample items include: “The financial rewards are great”; “Superiors provide frequent feedback on my work”; “The opportunity for me to advance is very good”

Professional challenge Sum of agreement with three statements: “The opportunity for profes-sional development is very good”; “The intellectual challenge of my work is great”; “I am respected and treated as a professional colleague by my superiors”; The scale for these items ranged from 1 (very descriptive) to 4 (just the opposite)

Perceived performance “How good do you think you are in the kind of work you are doing?” The scale ranged from 1 (very good) to 4 (not at all good)

Vacation allowed “During the past year, how much vacation time were you allowed?”, on a scale from 1 to 6 (1=5 or more weeks; 2=4 weeks; 3=3 weeks; 4=2 weeks; 5=one week or less; 6=no time)

Nonwork activities “Are you active in any social, community, or political organizations?”, measured as a dichotomous variable (0=no; 1=yes)

Spouse’s employment status 1=working full time; 0=working part time or not working

Satisfaction with office location “Do you find the city or town in which your office is located attractive, do you feel neutral about it, or do you find it unattractive?” –1 (unattrac-tive), 0 (neutral), 1 (attractive) and then multiplied by the importance attributed to this factor by the respondent; this resulted in a five-point scale ranging from –2 to 2, with zero representing a neutral attitude. Dependent variables

Intention to leave This variable was measured by one item: “Do you plan to change your employment within the next few years?” The scale ranged from 0=no to 1=yes; 647 lawyers (78%) responded no to this question and 183 (22%) yes

Intention to leave if Participants who responded no to the above question were asked: “If option is available you felt you had a reasonable alternative option, would you change

your employment?” The answer was 0 (no) or 1 (yes); the sample in this question did not include the 183 lawyers who responded no to the first question

Actual turnover This variable was measured based on two items that were asked in Wave 2 of the survey. Respondents were asked: “Have you been with the same employer/work setting since you completed the previous survey in February–April 1984? Answer yes if still with the same firm or agency even if you have had different positions within the firm/agency”; in a subsequent question respondents were asked about the reason for their leaving: One option was “I was forced to,” and those who reported this reasons were omitted so that turnover could be defined as voluntary; of the 635 who responded to this question, 443 (70%) had stayed in the same firm and 192 (30%) had left their firm between 1984 and 1990

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* p < 0.5 ** p <.01 *** p < .001 Means, Standard De

viations, Reliabilities, and Intercorrelations (N in parentheses).

1 .20*** (639) .18*** (589) -.18*** (1977) .23*** (846) .16*** (558) -.12*** (549) -.08* -.07 (540) .08 (584) -.29*** (495) .10* (562) -.10* (599) -.22*** (811) -.04 (403) SD .39 .45 .95 10.72 .49 .72 .60 .51 1.34 .49 .50 1.09 .46 .41 .50 3 -.56*** (589) .40*** (561) .07 (559) -.05 (552) -.13** (562) -.28*** (542) .18*** (579) -.29*** (457) .07 (563) .01 (129) -.16*** (590) .01 (405) 4 -.24*** (836) -22*** (558) .06 (549) .11** (561) .18*** (540) -.16*** (584) .31*** (496) -.07 (562) .18*** (571) .15*** (821) .12* (403) 5 .27*** (559) -.26*** (550) -.18*** (563) -.26*** (541) .22*** (552) -.27*** (433) .13** (564) -.08 (244) -.21*** (785) -.14** (388) 6 (.81) -.65*** (548) -.20*** (559) -.10* (540) .06 (548) -.08 (430) .17*** (560) -.05 (122) -.48*** (557) -40*** (386) 7 (.68) .24*** (551) .13** (536) -.13** (540) .07 (433) -.16*** (552) .00 (122) .46*** (550) .31*** (380) 8 .17*** (542) -.02 (552) .07 (433) .00 (465) -.22* (123) .11** (560) .14** (388) 9 -.04 (533) .13** (417) -.05 (542) .03 (118) .07 (540) .10* (373) 2 .17*** (584) .16*** (639) .15*** (556) .16*** (552) -.19*** (544) -.09*** (556) -.05 (537) .18*** (586) -.23*** (498) .07 (557) .11 (143) -.19*** (582) -.04 (402) 10 -.11* (458) .13** (553) .07 (123) -11** (577) -.03 (398) 11 -.00 (434) .24* (106) .15*** (456) -.05 (329) 12 .12 (123) -.19*** (561) -.04 (389) 13 .02 (220) -.04 (89) 14Means .81 .72 2.69 1948.4 6 .41 .45 1.67 1.36 2.95 .57 .48 .90 .30 .22 .45 V ariables P ersonal Characteristics

1. Gender (male) 2. Marital status (married) 3. T

enure 4. Age Wo rk-related V ariab les 5. P osition (P artner)

6. Job satisfaction 7. Professional challenge 8. P

erceived performance Nonwor k V ariab les 9. V acation allowed

10. Nonwork activities (active) 11. Spouse’

s employment status (full time)

12. Satisfaction from location Withdrawal V

ariables

13. T

urnover (leave)

14. Intention to leave regardless of option (yes) 15. Intention to leave if option is available (yes)

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cantly to the explanation of the two turn-over intention variables. In both cases the improvement in chi-square was significant. Also, the increase in two other logistic re-gression indicators, the percentage in cases correctly predicted and the R2

L for the two

dependent variables, strengthen the conclu-sion of a significant incremental effect of the work-related variables. The variables of job satisfaction, position, and professional challenge had significant effects on the vari-able intention to leave regardless of option, while the variables of job satisfaction and professional challenge had significant effects on the variable intention to leave if option was available. However, for the variable ac-tual turnover, only partial support for Hy-pothesis 1 was found. The results in Table III show that the effect of the personal char-acteristic variables (see Figure 1) was not significant despite a significant effect of the variable age. Work-related variables in Fig-ure 2 showed a significant improvement in chi-square, and improvement in R2

L, but not

in the percentage of cases correctly pre-dicted. The model itself, however, was not significant. Step 4 in Table IV revealed a significant effect of three personal variables (gender, marital status, tenure) and a sig-nificant effect of only one work-related vari-able, perceived performance. Hypothesis 1 expected a stronger effect of the work-re-lated variable than those found.

Hypothesis 2, which expected a signifi-cant effect of the nonwork domain variables, was partly supported by the data. It was not supported for the two turnover intention variables. As Table III shows, the effect of nonwork domain variables was not signifi-cant, as can be seen from the nonsignifi-cant improvement in chi-square as well as the negligible increase in the percentage of cases correctly predicted and the R2

L for the

two dependent variables (see Table III). In the final equation, in step 3 only one nonwork domain variable, vacation, was found to have a significant effect on one dependent variable, intention to leave re-gardless of option, but the improvement of chi-square was not significant. Hypothesis 2 was strongly supported for actual turn-over (see Table IV). The inclusion of the nonwork domain variables in step 3

im-proved significantly all the measures of the predictive efficacy of logistic regression.

Hypothesis 3 predicted that the re-search model would explain actual turn-over better than turnturn-over intentions. Data support this hypothesis by two relevant measures of the predictive efficacy of lo-gistic regression. As shown in Tables III and IV, the R2

L for actual turnover, which

is .43 (Model 3) and .48 (Model 4), which includes turnover intentions as an indepen-dent variable, is higher than the R2

L for the

two turnover intentions: .32 for intention to leave regardless of option and .18 for in-tention to leave if option was available. Sec-ond, the percentage of cases correctly predicted is higher for actual turnover (85%) than for the two turnover intention variables (83% and 70%). Hypothesis 4a was strongly supported by the data. Work-related variables had a stronger effect on turnover intentions than did personal char-acteristics and nonwork domain variables. Table III shows that the strongest increase in the measures of the predictive efficacy of logistic regression was achieved when the work-related variables were included in the equations. This was demonstrated mainly in the increase in the R2

L and in

the percentage of cases correctly predicted. For example, R2

L increased from .09 to .30

and from .03 to .14 when the work-related variables were entered into the equations of the two turnover intention variables. This was the strongest increase in all three models. Hypothesis 4b, which expected a stronger effect for personal and nonwork domain variables than for work-related ones, was generally supported by the data. This hypothesis was strongly supported by the effect of the nonwork domain variables, which had the strongest effect on all mea-sures of predictive efficacy, as can be seen in Table IV. The results are less supportive for the personal variables because Model 1, which includes these variables, was not significant. Nonetheless, the final equation of actual turnover, Model 4, showed an ef-fect for three personal variables (gender, marital status, tenure) and only one work-related variable (perceived performance). This finding provides additional support for Hypothesis 4b.

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Discussion

This study examined an interesting and important issue that has not been researched frequently in the human resource

manage-ment literature: Does the turnover process with professionals typically working in small service sector organizations, such as the law-yers studied here, differ from that among em-ployees working in larger organizations? The Variables

Intercept

Personal characteristics 1. Gender (male)

2. Marital status (married) 3. Tenure: Less than 1 year

1–3 years 4–9 years 4. Age Work-related variables 5. Job satisfaction 6. Position (partner) 7. Professional challenge

8. Perceived performance: very good Nonwork variables

9. Vacation allowed: 5 or more weeks 4 weeks

3 weeks 2 weeks 1 week or less

10. Nonwork activities (active)

11. Spouse’s employment status (full time) 12. Satisfaction with office location: very low

Low Indifferent High Chi-square Degrees of freedom Improvement Chi-square Degrees of freedom % correctly predicted R2 L Intention to leave regardless of option (yes)

Logistic Regression Models with Turnover Intentions as Dependent Variables.

Intention to leave if option is available (yes) Model 1 -90.93** .50*** .30** -.21 .35* -.18 .05** 64.31*** 6 75.74% .09 Model 2 -69.55 .36* .17 -.09 .31 -.46* .03 -1.11*** .32* 1.15*** .08 186.85*** 10 122.54*** 4 81.29% .30 Model 3 -40.19 .29 -.13 .02 .54* -.66* .02 -1.33*** .26 1.25*** .07 -.05 -.27 .23 -1.12*** 1.07 .13 -.15 5.48 -.70 -1.34 -1.16 140.599*** 21 0 11 82.54% .32 Model 1 -53.13* .14 .04 -.85** .31 .28 .03* 16.89** 6 56.31% .03 Model 2 -43.01 .09 .00 -.60 .31 -.08 .02 -1.21*** .12 .63* -.03 71.28*** 10 54.39*** 4 69.23% .14 Model 3 -39.09 .06 .06 -.83 .58* .01 .02 -1.23*** .17 .44 -.17 -.38 -.25 -.14 -.19 .43 .04 .29 .42 .52 -.07 69.17*** 20 0 10 70.28% .18

Note: All variables with two categories are dichotomized (0–1). The dichotomized variables are gender (males=1), marital status (married=1), position (partner or executive position=1), membership in clubs (member=1), spouse’s employment status (full time=1), intention to leave regardless of option (yes=1), actual turnover (left=1). For variables of more than two categories the “basic” groups of the dummy variables excluded for the regression analysis were: tenure group of 10 years or more, perceived performance group of “moderately good,” vacation allowed group of “no time,” satisfaction with office location group of “very high.” N ranges from 577 to 401 for the variable intention to leave regardless of option and from 364 to 286 for the variable intention to leave if option is available.

*p<.05 **p<.01 ***p<.001

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vast majority of turnover research is based on models developed for employees in larger or-ganizations in both the public and private sec-tor, and naturally the variables examined here relied conceptually on these models. In

gen-eral, the findings show that the variables con-sidered as important determinants of employee turnover in typical bureaucratic organizations can also explain turnover in much smaller pro-fessional organizations. This is demonstrated

TABLE IV

Variables Intercept

Personal characteristics 1. Gender (male)

2. Marital status (married) 3. Tenure: less than 1 year

1-3 years 4-9 years 4. Age Work-related variables 5. Job satisfaction 6. Position (partner) 7. Professional challenge

8. Perceived performance: very good Nonwork variables

9. Vacation allowed: 5 or more weeks 4 weeks

3 weeks 2 weeks 1 week or less

10. Nonwork activities (active)

11. Spouse’s employment status (full time) 12. Satisfaction with location: very low

Low Indifferent High

Withdrawal variables

13. Intention to leave regardless of option (yes) Chi-square Degrees of freedom Improvement Chi-square Degrees of freedom % correctly predicted R2 L

Logististic Regression Models with Actual Turnover as Dependent Variable. Model 1 -78.42 .22 -.32 -.26 -.43 -.07 .04* 4.38 6 73.39% .03 Model 2 -94.10 .21 -.43 -.70 -.54 -.14 .05 -.37 .36 .08 .61* 14.03 10 9.65* 4 72.65% .10 Model 3 -241.46 -1.17 -1.75* .12 -2.13* -.46 .12 -1.51 -.52 -1.12 1.28* -1.47 .25 1.05 1.37 -3.39 .01 -2.13** 1.20 -1.79 -1.26 41.24** 20 27.21** 10 84.15% .43 Model 4 -298.14 -1.85* -2.56* -.26 -3.37* .17 .15 -.175 -1.25 -3.17 1.46** -2.81 -.08 .80 2.50 -3.19 -.16 -3.00** 1.97 -2.96 -2.17 -1.63* 44.84** 21 3.6 1 85.19% .48

Note: All variables with two categories are dichotomized (0-1). The dichoomized variables are gender (males=1), marital status (married=1), position (partner or executive position = 1), membership in clubs (member=1), spouses’s employment status (full time=1), intention to leave regardless of option (yes-=1), actual turnover (left=1). For variables of more than two categories the “basic” groups of the dummy variables excluded for the regression analysis were: tenure group of 10 years or more, perceived performance group of “moderately good,” vacation allowed group of “no time,” satisfaction with office location group of “very high.” N ranges from 124 to 106.

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by the good predictive power of the traditional determinants of turnover tested here, such as tenure, job satisfaction, job challenge, and per-ceived performance, for turnover intentions and/or actual turnover among professionals working mostly in small organizations, namely lawyers’ offices. The lack of effect of the vari-able position (being a partner) on actual turn-over strengthens the above conclusion. If one wants to argue for a unique work setting of lawyers (Gunz & Gunz, 1994), which should result in different causes for turnover, then being a partner or not would be expected to have some effect on actual turnover, in par-ticular for nonpartners who might seek to improve their status in another firm. The find-ings here do not support such an effect.

Thus, the present results support the no-tion that turnover determinants operate simi-larly in many different work settings, from bureaucratic to smaller, more traditional em-ployer-employee arrangements, such as those studied here. The relative magnitude of the effect of each of these variables may differ from that in models tested in more traditional settings, but the main variables tested in turn-over research and the processes they repre-sent appear to be esrepre-sential in the causal

process that leads to employee turnover, re-gardless of the employees’ organizational set-ting. The findings match conclusions reached by the few studies conducted in similar work settings. For example, Wallace (1995a; 1995b) found that traditional determinants of orga-nizational and professional commitment were related to these commitments among lawyers in law firms. Mueller et al. (1994) found that traditional turnover determinants also af-fected turnover intentions and actual turn-over among dental hygienists. Thus, the general conclusion based on the data collected so far supports Mueller et al.’s claim that cer-tain features of work, such as job satisfaction and other job characteristics (e.g., challenge, variety), are probably near-universal features in the employment relationship for affecting turnover; however, more research of turnover in settings similar to the one tested here is needed to support and validate this conclusion.

The findings of this study show that dif-ferent explanations are required for short-term versus long-term turnover. Hom and Griffeth (1991) in their longitudinal design concluded that the causal relations among turnover an-tecedents systematically changed over time, Thus, the present

results support the notion that turnover determinants operate similarly in many different work settings, from bureaucratic to smaller, more traditional employer-employee arrangements. Personal Characteristics: - Gender - Marital Status - Tenure - Age Work-Related Variables: - Job Satisfaction - Position - Professional Challenge - Perceived Performance Nonwork Variables: - Vacation Allowed - Nonwork Activities

- Spouse's Employment Status - Satisfaction with Office

Location

Turnover Intentions

Dependent variable Independent variables

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and while some structural effects grew over time, others decayed. The pattern of their findings in a sample of registered nurses is very similar to the pattern here. Over time there was a decline in the effect of job satis-faction on turnover and a stronger effect of withdrawal cognitions. They explained this by arguing that as job satisfaction declined, it increasingly reinforced expected utility of withdrawal, but decreasingly affected with-drawal cognitions. It is possible that once ini-tiated, withdrawal cognitions developed their own momentum and proceeded without fur-ther causal impetus. Note especially the simi-larity between the findings of this research and those of Hom and Griffeth, despite the differences in the two research designs: Law-yers and a six-year interval between the mea-surement of determinants and turnover in this research versus registered nurses and a one-year interval in theirs. In both studies, the effect of job satisfaction decreased the longer the interval between the measurement of the attitude and the measurement of turnover. All the above strengthens Marsh and Mannari’s

(1977) argument that short-term turnover maximizes the stability of the independent variables, work attitudes in particular. Work-related attitudes will, therefore, affect turn-over measured several months, a year, or perhaps two years after the measurement of the attitudes. A longer time span offers a higher probability for changes in these atti-tudes, causing more prediction errors because employees who had positive attitudes toward their organization and were predicted as stayers might have changed such attitudes during a three-, four-, or five-year period, and have left.

This research tested the effect on nonwork domain variables based on previous recommendations in the literature to do so. For example, Morita et al. (1993) concluded that their survival analysis confirmed theoreti-cal predictions that nonwork variables play a more important role in the departure deci-sion than had been previously demonstrated empirically, but nonwork domain variables were not researched extensively in their rela-tionship to turnover. In the few cases where Personal Characteristics: - Gender - Marital Status - Tenure - Age Work-Related Variables: - Job Satisfaction - Position - Professional Challenge - Perceived Performance Nonwork Variables: - Vacation Allowed - Nonwork Activities

- Spouse's Employment Status - Satisfaction with Office

Location

Actual Turnover

Dependent variable Independent variables

FIGURE 2.Actual Turnover. Turnover Intentions

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AARON COHEN is a senior lecturer in the Department of Political Science, University of

Haifa, Israel. He received his Ph.D. in Management at the Technion-Israel Institute of Technology and taught for three years at the University of Lethbridge, Alberta, Canada. His current research interests include the relationship between politics and work, work/ nonwork relationship, work commitment (and, in particular, organizational commit-ment and union commitcommit-ment), and union participation. His most recent work has been

published in Academy of Management Journal, Journal of Management, Journal of

Orga-nizational Behavior, and Human Relations.

they were examined empirically, they were found to be significantly related to turnover (Kirschenbaum, 1991; Mueller et al., 1994). The findings revealed a very strong effect of spouse’s employment status on actual turn-over, showing that the more ties one develops to one’s community, the more difficult it will be to leave the employer. But such an effect requires time; therefore, some nonwork do-main effects may not be captured if turnover is measured a short time after independent variables are measured. This may explain the weak effect of nonwork domains on turnover intentions and the strong effect on long-term turnover found here. The findings here are in accordance with those of Mueller et al. (1994) who found a strong effect of nonwork vari-ables on dental hygienists’ turnover intentions and on actual turnover, and they strengthen the conclusion that to truly understand the individual at work, not only must his/her life at work be considered, but also his/her life away from work (Kirchmeyer, 1992). Future research should further examine whether the effect of nonwork domains on turnover is stronger in professional settings or is also prevalent in more traditional work settings,

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Wallace, J.E. (1995a). Organizational and professional commitment in professional and nonprofessional organizations. Administrative Science Quarterly, 40, 228–255.

1. An earlier version of this article was presented at the 1996 annual meeting of the Academy of Manage-ment, Human Resources Division, Cincinnati, Ohio. The author thanks two anonymous reviewers for their helpful comments and suggestions.

2. Data Analysis. Because all dependent variables were dichotomous (stay or leave; intent or nonintent) it was decided to use a nonlinear logistic regression model (Morita et al., 1993). Even though multiple regression is used most commonly for this kind of problem, a logistic regression was used here for several reasons. First, logistic regression is preferable to linear probability models (multiple regressions with a dichoto-mous dependent variable) for uncovering the relationship between a dummy variable and several indepen-dent variables because logistic regressions constrain estimated probabilities to between 0 and 1 while multiple regression does not. Second, regression techniques that use ordinal least squares are based on the assumption of constant variance of the error term (homoscedasticity). When a dummy variable is used as a dependent variable, this assumption is violated and the t and F statistics used for hypothesis testing are unreliable. Hence, some results obtained in this manner may be misleading (Aldrich & Nelson, 1984). Logistic models are not interpreted in the same way as linear models. Beta coefficients do not directly predict the value of the dependent variable but the probability that the dependent variable will attain a specific value, in this case 0 (stay) or 1 (leave). In the models that follow, raising the value of variables with significant positive coefficients raised the probability that one would leave the organization, while raising the value of those with significant negative coefficients raised the probability that one would stay. In this way, the signs of the coefficients were interpreted in a way roughly analogous to those in a linear model. Interpretation of the Beta coefficient for the different categories in the categorical independent variables is based on their relation to the comparison group. The continuous variables are interpreted in a procedure similar to the linear regression. Several measures for assessing the predictive efficacy of logistic regression were applied here (Demaris, 1992). First, in logistic regression the analogue of the global F test is a likelihood ratio chi-square test statistic, often referred to as a model chi-square. Second, classification results of the dependent variables present the percentage of the cases correctly predicted. Third, based on Demaris (1992), R2

L, a measure based on the log likelihood and which is an approximation for assessing

predictive efficacy such as the R2, was applied, too. Like R2, R2

L tends to range from 0 to 1, but R 2

L tends

to underestimate the proportion of variation explained. A procedure to test if a subset variable makes a significant contribution to the model was also applied. This procedure is similar to that in linear regres-sion, except that a chi-square difference test takes the place of the increment-to-R2 test. The test is simply

the difference in the model chi- squared for the model with the additional subset of variables versus the model without them. If the null hypothesis is true, this difference has the chi-square distribution with df equal to the number of parameters set to zero in the null hypothesis (Demaris, 1992). The independent variables were entered into the equations in three steps: step 1 the personal characteristic variables, step 2 the work-related variables, and step 3 the nonwork variables. These steps were to be performed in both Models 1 and 2. In Model 2, where actual turnover is the dependent variable, a fourth step was performed in which the variable intention to leave regardless of option was added to the equation.

ENDNOTES

Wallace, J.E. (1995b). Corporatist control and orga-nizational commitment among professionals: The case of lawyers working in law firms. Social Forces, 73, 811–839.

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