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The International Journal of Human Resource Management,
Vol. 19, No. 9, September 2008, 1553–1568
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Using job embeddedness factors to explain voluntary turnover
in four European countries
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Cem Tanovaa* and Brooks C. Holtomb
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a
Faculty of Business and Economics, Eastern Mediterranean University, Gazimagusa, North Cyprus via
Mersin 10; bMcDonough School of Business, Georgetown University, Washington, DC 20057, USA
The aging of the European workforce coupled with existing deficits of skilled workers in vital
sectors (e.g., information and communication technology) make the attraction and retention of
skilled workers a critical strategic human resource management issue. The large-scale,
multi-country study reported in this article investigates the causes of voluntary turnover.
The study is based on a large European dataset that contains information about a wide variety
of variables that have been shown to influence voluntary turnover. The results indicate that the
traditional turnover model, where ease of movement and desirability of movement are
regarded as important predictors of turnover, receives support. Importantly, the study also
shows that a new theory of employee retention – job embeddedness – explains a significant
amount of variance above and beyond the role of demographic and traditional variables.
In sum, the evidence suggests that the turnover decision is not only about the individual’s
attitudes towards work or about the actual opportunities in the labour market, but also job
embeddedness.
Keywords: job embeddedness; voluntary turnover; ease of movement; desire of movement
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Introduction
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The attraction, development and retention of skilled employees are critical issues for
organizations and nations. While enlargement of the European Union and intensified European
integration have introduced the possibility of greater labour mobility, there continue to be
marked differences between the EU and United States. Data from Eurostat indicate that in a
given year approximately 1% of the EU population changes region, compared with nearly 6%
who moved from one county to another in the United States (Fuller 2002). The same dataset
shows that Europeans are half as likely to change jobs as Americans in a given year. Given these
differences between Europe and the United States and the fact that the vast majority of academic
research on voluntary turnover has been published using models and samples from the
United States, there is an urgent need to rigorously test the generalizability of these models in the
European context.
From the perspective of the organization, employee turnover creates both tangible and
intangible costs. The tangible costs include recruitment, selection, training, adjustment time,
possible product and/or service quality problems, and the costs of agency workers/temporary
staff (Morrell, Loan-Clarke and Wilkinson 2004a). The intangible costs, which may be even
more significant than the tangibles, involve the effect of turnover on organizational culture,
employee morale, social capital and organizational memory (Morrell et al. 2004a).
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*Corresponding author. Email: cem.tanova@emu.edu.tr
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ISSN 0958-5192 print/ISSN 1466-4399 online
q 2008 Taylor & Francis
DOI: 10.1080/09585190802294820
http://www.informaworld.com
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At the national level, some labour mobility is viewed as positive for national economies
because of job mismatches (Jovanovic 1979). Some employees may have accepted a job that was
below their abilities in difficult times and they may wish to switch when they find the
opportunity. Or some organizations may have employed an individual who was not a good
match for the position. Jovanovic (1979) views jobs as ‘experience goods’ and as ‘search goods’.
Based on the job employee matching model, workers move across jobs in order to find a good
match which pays for their aptitudes and meets their expectations (Davia 2005).
For an individual, turnover (including both voluntary and involuntary) will mean making a
break with existing social networks, the stress of a new environment and an adjustment process.
For some employees there may be direct losses related to benefits that they were receiving as
being part of the organization (Griffeth, Hom and Gaertner 2000). There may also be some
advantages. For example, Davia (2005) reports that employees at the early stages of their career
who voluntarily leave, experience positive increases in their wages compared to those that do not
change jobs. Furthermore, Davia (2005) found that turnover may pay in the mid-term even for
involuntary movers although at a decreasing rate.
One reason that a high rate of voluntary turnover is alarming for many managers is the fear
that the employees with better skills and abilities will be those who are able to leave whereas
those who remain will be those who cannot find other jobs. Thus, while voluntary turnover may
be beneficial to the employee, it may not be for the organization. Though additional factors
beyond individual preferences come into play especially in the European context (e.g., there are
many other issues that prevent employees from leaving other than not finding alternative jobs),
this topic merits rigorous scientific attention because of the significant consequences at the
individual, organizational and national levels.
Not surprisingly, turnover continues to be a topic of interest among researchers from
disciplines such as management, psychology, economics and sociology. Shaw, Delery, Jenkins
and Gupta (1998) report over 1,500 studies on the subject. There have also been several metaanalyses on the determinants of turnover (Cotton and Tuttle 1986; Cohen 1993; Hom and
Griffeth 1995; Griffeth et al. 2000). Yet, there still is no universal agreement on the factors that
explain why some employees leave and some stay. Simple logic suggests that assuming
employees have viable job alternatives, if they are satisfied with their current job they will stay,
but if they aren’t they will leave (Mitchell, Holtom, Lee, Sablynski and Erez 2001). However,
scientific literature shows that work attitudes (like job satisfaction) play only a relatively small
role overall in turnover (Hom and Griffeth 1995; Griffeth et al. 2000). After a comprehensive
review of the turnover literature Morrell, Loan-Clarke, and Wilkinson (2001) conclude that there
is need for new theory in the study of turnover. The purpose of this article is to test a relatively
new theory of employee retention (job embeddedness) developed and tested in the United States
to assess its generalizability in the European context.
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Turnover models
Much of the current theoretical and empirical work on the topic of turnover is based on March
and Simon’s (1958) foundation. They emphasized that employees’ degree of perceived ease of
movement and perceived desirability of movement determines their likelihood of seeking a new
job. The desirability of movement depends on job-related attitudes and internal opportunities
while ease of movement depends on external factors such as availability of alternative jobs and
unemployment levels.
It is possible to distinguish turnover models as being from either the ‘psychological school’
or ‘labour market school’ (Morrell et al. 2001). The labour market school focuses on employees
as rational, homogeneous and equally affected by external factors. The emphasis may
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be interpreted as deterministic since external influences are seen to determine actions (Morrell
et al. 2004a). The topics discussed include job search (Fahr and Schneider 2004; Mano-Negrin
and Tzafrir 2004), labour market flexibility, job mobility and wage mobility (Royalty 1998;
Davia 2005), unemployment level (Trevor 2001) and person-job matching (Jovanovic 1979).
The psychological school focuses on topics related to explaining or predicting leavers’
behaviour. The emphasis can be interpreted as voluntary, since individual choice is emphasized
(Morrell et al. 2004a). The topics discussed are individual characteristics, stress, burnout,
emotional exhaustion, personality, job satisfaction, organizational commitment, and job
involvement (Hom and Kinicki 2001).
Further, a general withdrawal construct has been proposed whereby individuals engage in
activities (e.g., lateness, absenteeism, diminished performance) that are seen as leading
indicators of turnover (Hulin 1991; Sablynski, Mitchell, Lee, Burton and Holtom 2002). More
recently the image theory of decision making has been integrated with the unfolding model of
turnover (Lee and Mitchell 1994; Morrell et al. 2004a; Morrell, Loan-Clarke and Wilkinson
2004b). This approach demonstrates that for many the decision to leave is not the result of
gradual built up of negative attitudes but rather the result of a shock, or some event that is
significant enough to overcome the existing inertia.
A new construct, job embeddedness, is conceptualized as influencing the decision to remain
through the level of links a person has to other people or activities, the extent that the person’s
job and community are congruent with the other aspects of their life, and the sacrifices a person
would make in the process of leaving their employment (Mitchell et al. 2001). The well-known
work of Granovetter (1985) introduced ‘embeddedness’ as a way to explain how social
relations influence economic action. Building on this idea, the ‘job embeddedness’ (Mitchell
et al. 2001) construct views the individual as a part of a complex web of relationships and
attachments. The more extensive the web is, the more lines that connect the many aspects of
the individual’s life. Thus, the more elaborate web will have a stronger influence on an
individual who is considering making changes in one part of the web because that change will
affect many other features of the individual’s life (Holtom, Mitchell, and Lee, forthcoming).
The critical aspects of job embeddedness are: 1) the links that people have on and off the job;
2) the fit that they perceive between their self-concept and the environment that they live and
work in; and 3) the sacrifices that they would make in giving up their job in terms of how this
action would affect the other aspects of their life. Links, fit and sacrifice all have on-the-job and
off-the-job dimensions.
Links to the organization are the relationships that the individual has with the organization
(e.g., department, work team) and the relationships that they have with others at work (e.g.,
coworkers, boss, mentor). The links to the community include the ties that the individual has in
the area especially with friends, relatives and organizations. Fit with the organization assesses
how the individual perceives their work in the organization and whether the individual feels that
there is congruence between what she wants to do or can do and what she is actually doing. Fit
with the community is the perception of fit between the individual’s concepts of the community
that they would like to be a part of and the community that they actually live in. This includes the
values of the community as well as the physical offerings such as housing and leisure activities.
The sacrifice associated with leaving a job focuses primarily on the tangible losses that would be
incurred if the individual left the job (e.g., benefits). Also, the community sacrifice dimension
captures tangible losses involved if the employee was contemplating leaving the community
because she was leaving the organization.
Mitchell et al. (2001) conceive job embeddedness as a mediating construct between an
individual’s work and personal life. Each dimension may have different degrees of importance
for different individuals at different times or stages of their lives, however the magnitude of total
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embedding forces will have an influence on a person’s decision to leave. In 2001, Mitchell et al.
produced evidence that job embeddedness predicts employee turnover over and above the
effects of gender, psychological factors (e.g., job satisfaction), and labour market factors
(e.g., job search behaviours). Further evidence of the predictive power of job embeddedness was
presented in 2004 by Lee, Mitchell, Sablynski, Burton, and Holtom. In a large multi-national
bank they found that job embeddedness not only predicts who leaves but also predicts in-role
performance as well as extra-role performance. Additionally, Mallol, Holtom, and Lee
(forthcoming) found that though levels of job embeddedness varied systematically between US
born and non-US born employees (pre-dominantly Hispanic), the overall construct predicted
voluntary turnover for both groups.
The current article proposes a model that incorporates both the ease and desirability of
movement variables together with job embeddedness factors to understand turnover using the
available data from European Community Household Panel survey.
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Expected relationships
Personal characteristics
Age
Younger employees are more likely to take risks at the beginning of their careers. They are also
more likely to accept positions that are below their abilities and expectations at the beginning of
their career and move on to better jobs when those jobs become available. Meta-analytic
research supports the negative age-turnover relationship (Griffeth et al. 2000).
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Gender
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Female workers traditionally have been seen as having a lower attachment to the labour force
than men. However, in their meta-analysis, Griffeth et al. (2000) show only a negligible
difference between men and women in terms of turnover (women are slightly more likely to
leave their jobs than men). Further, Royalty (1998) found gender differences in turnover are due
to the behaviour of less educated women (many of whom leave the labour force when they leave
a job). In sum, educated women and men are very similar in their turnover behaviour.
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Income
As a person’s income from a job increases, the probability of their leaving the job decreases.
This result has been demonstrated in diverse occupations, demographic groups, and across
gender. Though the effect is typically modest (e.g., r ¼ 2 .11; Griffeth et al. 2000), it is
persistent.
Hypothesis 1: Age and income will be negatively related to voluntary turnover.
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Desirability of movement
Job satisfaction
Job satisfaction has been found across many studies to be a reliable predictor of turnover, though
the amount of variance explained is typically modest (Griffeth et al. 2000). Van Breukelen, van
der Vlist and Steensma (2004) also show a negative relationship between job attitudes and
turnover. Measuring job satisfaction at two different times before the measurement of leaving or
staying, they find that there was stability in employees’ attitudes. However, they report that after
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the variance explained by intentions to leave the other variables did not explain much. Trevor
(2001) analyzed interactions between actual ease of movement determinants such as education
level and market conditions and found that when unemployment rates were low job satisfaction
had greater impact on turnover decisions. Moreover, he found the effects of job satisfaction and
unemployment rate depended upon levels of education, cognitive ability and occupation-specific
training.
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Ease of movement
Unemployment level
Armknecht and Early (1972) found that up to 78% of the variance in aggregate quit rate could be
explained on the basis of present and expected economic conditions. Clearly, labour markets
shape the quality and quantity of alternative opportunities (Mano-Negrin and Tzafrir 2004) and,
thus, an individual’s behaviour or even attitudes are likely to be different in times of high versus
low unemployment (Trevor 2001).
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Education
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Higher levels of education are likely to increase an individual’s turnover likelihood by
increasing his or her opportunities. Moreover, an unobservable characteristic that could be
associated with higher levels of education may be labelled ‘career mindedness’ (Royalty 1998).
A career-minded individual would be more likely to take the risk of changing a job for potential
improvements in their career.
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Withdrawal behaviours
Withdrawal
According to Hulin (1991), individuals are likely to progressively enact more extreme
manifestations of job withdrawal. This could begin with putting less physical and mental energy
to their job, or going to work late and lead to absenteeism and eventually reaching voluntary
turnover. With regard to behavioural predictors, the literature shows a small, positive
relationship between job withdrawal behaviour such as tardiness or absenteeism and turnover
(Griffeth et al. 2000).
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Job search
Another sign of possible withdrawal occurs when individuals start looking for other jobs.
The turnover literature shows a strong, positive relationship between actual job search
behaviours and turnover (Griffeth et al. 2000).
Hypothesis 2: Desirability and ease of movement factors as well as withdrawal behaviours will
predict variance in turnover beyond that explained by demographic variables and
income.
Job embeddedness
As discussed previously, job embeddedness has explained variance in voluntary turnover above
and beyond gender, desirability and ease of movement variables as well as job search behaviours
(Mitchell et al. 2001). In that study and subsequently (Lee et al. 2004), job embeddedness has
been operationalized as the combination of six factors: an employee’s fit with the community, fit
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with the organization, links with the community, links with the organization, sacrifice of leaving
the community and sacrifice of leaving the organization. Put differently, an individual who has
strong links to the community and the organization, who perceives that there is a good fit
between her expectations and what the community and the organization is currently providing,
and stands much to lose if these links are broken will be less likely to change jobs than a person
who is less ‘embedded’.
Hypothesis 3: Job embeddedness factors will improve the prediction of turnover beyond
demographic factors, income, desirability and ease of movement and withdrawal
behaviours.
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Method
Data
The European Community Household Panel (ECHP) survey is a harmonized, cross national
annual longitudinal survey that focuses on household income and living conditions and provides
information on employment and personal demographic characteristics. It has been collected
since 1994 under Eurostat coordination. In the first wave approximately 130,000 adults aged 16
years and over were interviewed in about 60,500 nationally represented households in the then
12 member states. However not all countries entered the survey at the same time and for
Germany, Luxembourg and United Kingdom the original sample has been replaced with
harmonized versions of household panels already being produced nationally. For researchers a
further anonimized sub-sample of the original data is available under strict contractual
conditions. Further information on the ECHP and discussion on attrition, non-response, and
weighing procedures can be found in Peracchi (2002). Some of the variables do not have
identical response sets for some countries which limit the analysis that can be carried out using
those variables. For example, the education level for the Netherlands seems to have been
different after the third wave (i.e., some respondents who had higher levels of education
were reported to have missing data or lower education levels in subsequent waves).
Luxemburg, Ireland, Greece, Portugal, Austria, Sweden and The United Kingdom had many
missing data for variables of interest in the current study and the question regarding reason for
leaving the previous job in Germany indicated that there was a different set of responses used in
this country in the recent waves of the survey, hence these countries were not included in the
analysis. That allowed us to proceed with four European countries: Denmark, Italy, Spain, and
Finland.
Using the data for years 2000 and 2001 – the most recent waves – from the ECHP, we
identified the individuals that had a full-time job (working at least 30 hours per week) other
than self-employment in the year 2000 who were between the ages of 20 – 59. The age and the
working hour limitations were used in order to focus on individuals who had a relatively
stronger attachment to the labour market. The data for 2001 was then checked to see if the
individual had changed jobs. This was done by looking at the responses to a question that
asked the reason for stopping the previous job. The responses could be ‘obtained better/more
suitable job’, ‘obliged to stop by employer’, ‘end of contract/temporary job’, ‘sale/closure of
own or family business’, ‘marriage’, ‘child birth/need to look after children’, ‘looking after
old, sick, disabled persons’, ‘partner’s job required to move to another place’, ‘study/national
service’, ‘own illness or disability’, ‘wanted to retire or live off private means’, ‘other’ or ‘not
applicable’. If the response was something other than not applicable this would indicate a
stopping the previous job. To determine when the job change took place, we looked at
questions that asked the year and the month of leaving the previous job, if this was some time
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after their ECHP survey in 2000 and before their ECHP survey in 2001, this indicated that the
survey questions in 2000 were filled out before leaving the job and the 2001 survey would
give us information on the person’s new condition. We created a turnover variable based on
reason for leaving the previous job which placed individuals in three groups; ‘those that left
their previous job voluntarily’, ‘those that had to leave their previous job’, and ‘those that did
not change their job’. The data for the two years was then matched. Since the objective was to
see how personal characteristics, satisfaction levels, and job embeddedness predict future
turnover, we use measures of all independent variables from 2000 and the turnover data
from 2001.
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Measures
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For each respondent three categories were available, those that left voluntarily, those that were
forced leave their job (involuntary turnover) and those that stayed.
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Voluntary turnover
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Personal characteristics
Age and gender were taken directly from the survey. Gender was coded 1 for male.
Income
The ‘income to occupational average’ variable was created by taking the purchasing power
parity for each country, calculating the mean earnings from work for each of the nine occupation
groups for the corresponding countries based on purchasing power parity, calculating the annual
earnings from work for each individual based on purchasing power parity and dividing that
figure to the related occupational average.
Desirability of movement variable
Job satisfaction for year 2000 was measured with a single question that asked in the year 2000:
‘Satisfaction with work’ the responses ranged from 1 not satisfied to 6 fully satisfied.
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Ease of movement variables
The unemployment rates for each country in year 2000 for men and women based on their level
of education was taken from Eurostat database and was matched with the individuals based on
sex, education level, and country in the dataset.
The education level was measured based on the International Standard Classification of
Education (ISCED). Third level education (ISCED 5– 7) was coded as 3, second stage
of secondary level education (ISCED 3– 4) was coded as 2, and less than second stage of
secondary education (ISCED 0 –2) was coded as 1.
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Withdrawal behaviours
The question in the survey used to identify withdrawal behaviour/absenteeism – ‘how many
days over the last four weeks other than holidays did you miss work?’ was used to measure
absenteeism.
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Job search behaviour
The question ‘are you currently looking for work’ was used as well as the response to ‘currently
working but looking for more hours or different job’ to indicate that the respondent was looking
for another job.
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Job embeddedness
As noted by Mitchell et al. (2001), job embeddedness is a formative measure. It represents a focus
on the accumulated, generally non-affective, reasons why a person would not leave a job. Mitchell
Q6 et al. (2001) further explained that ‘Job embeddedness is an aggregate multidimensional construct
formed from its six dimensions (Law, Wong and Mobley 1998). More specifically, its indicators
are causes of embeddedness and not reflections (MacCallum and Browne 1993)’. Thus, the
measures that will be used to model job embeddedness are causal (and not effect) indicators.
Also, as observed by Edwards and Bagozzi (2000), “associations of formative measures with
Q2
their construct are determined primarily by the relationships between these measures and
constructs that are dependent on the construct of interest’ (p. 159). This point is further clarified
Q3 by Heise (1972) who noted that a construct measured formatively is not just a composite of its
measures; rather, ‘it is the composite that best predicts the dependent variable in the analysis....
Thus, the meaning of the latent construct is as much a function of the dependent variable as it is a
function of its indicators’ (p. 160). In sum, while we would prefer to have measured job
embeddedness using the Mitchell et al. (2001) instrument, that was not possible in the current
context. Thus, we have identified 16 items that closely resemble the items used by Mitchell and
colleagues in the seminal papers on job embeddedness. There are at least two items for each
dimension. Moreover, the items are substantially similar to an 18-item measure introduced
Q4 recently by Mitchell and colleagues (Holtom, Tidd, Mitchell and Lee 2006).
Regarding the questions in the ECHP that reflect job embeddedness, we recoded them so that
higher scores (or 1 in the case of dichotomous variables) would be related to high embeddedness.
Then, we conducted an exploratory factor analysis with varimax rotation. The loadings of the
variables are provided in Table 1. The links-community dimension was measured with a variable
about marital status (1 ¼ married) and a variable regarding the presence of children in the
household. The links-organization dimension was measured with the variable on job status
(1 ¼ permanent) and a variable regarding job tenure. The fit-organization dimension was
measured with questions related to having education or training related to the current job and
whether this education or training contributes to the job. The fit-community dimension was
measured with the variables ‘satisfaction with leisure activities’ and ‘satisfaction with housing
situation’. The sacrifice-organization dimension was measured with several questions: ‘employer
provides child minding or crèche – free or subsidized’, ‘employer provides health benefits – free
or subsidized’, ‘employer provides education or training – free or subsidized’, ‘employer provides
sports, leisure or vacation facilities’, and ‘employer provides housing – free or subsidized’. The
sacrifice-community dimension was measured by home ownership, rent free housing and
outstanding mortgage on the house. The mean of the six dimensions was calculated first. Then, the
two sub-dimensions (embeddedness in the community and embeddedness in the organization)
were calculated. Finally, they were combined to arrive at the overall level of ‘job embeddedness.’
Analysis
In order to perform the analysis, we used the Cox Proportional Hazard Model:
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hðtÞ ¼ ½h0ðtÞ exp ðb1X1 þ b2X2 þ . . . bkXkÞ
ð1Þ
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0.01
20.03
0.16
20.06
0.19
0.42
Notes: Extraction method: Principal component analysis; Rotation method: Varimax with Kaiser normalization.
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0.05
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0.25
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0.22
0.26
0.03
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0.94
0.93
0.53
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0.11
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0.23
0.08
Sacrificecommunity
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0.11
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0.02
0.00
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2 0.11
0.09
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0.03
0.03
0.78
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399
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0.00
0.09
0.00
0.94
0.95
0.04
0.03
20.06
398
Sacrificeorganization
396
397
0.41
20.08
0.74
0.85
0.04
0.05
0.04
0.04
0.06
403
Fit-community
395
0.66
0.86
0.08
20.02
20.02
20.01
20.13
0.01
20.08
408
Fit-organization
394
Marital status(1married)
children under 12 in house(1 yes-0 no)
Job status(1 permanent)
Job tenure
Has training or education related to job
The training/education contributes to job
Satisfaction with leisure
Satisfaction with housing situation
Employer provides child minding or
crèche/free or subsidized
Employer provides health benefits/free
or subsidized
Employer provides education or
training/free or subsidized
Employer provides sports, leisure or
vacation facilities
Employer provides housing/free or
subsidized
owns the home(1 yes-0 no)
either owns or house is rent free(1 yes-0 no)
there is outstanding mortgage (1 yes-0 no)
415
Links-organization
393
Links-community
Table 1. Factor loadings of job embeddedness variables.
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447
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Where h(t) ¼ the hazard function at time t, h0(t) is the baseline hazard function for an
individual if the value of all the independent variables could be zero.
The independent variables were gender, age, income, higher education, relevant
unemployment rate, job satisfaction, job search behaviour, absenteeism and embeddedness.
The dependent variable was the dichotomous voluntary turnover variable, indicating whether the
person left their job voluntarily. If they were forced to leave or stayed they were treated as
‘censored’. The model predicts the hazard function h(t), in our case the probability that an
individual will leave their job voluntarily at time (t). Time was based on the question ‘when did
you begin your present job’ that was asked in the year 2000, and the total time without a job
change was calculated for the 2001 responses.The model does not make assumptions about the
nature or shape of the hazard function; however it assumes that changes in levels of
the independent variables will produce proportionate changes in the hazard function,
independent of time. Also, it assumes a log linear relationship between the hazard function and
the independent variables. The hazard function provides an estimate of the relative risk of
voluntary turnover per unit time for an individual that has not left voluntarily up to that time.
In the analysis exponentiated coefficients of the independent variables mean the expected
change in h(t) for a unit change in the independent variable.
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460
461
462
463
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465
466
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473
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477
478
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481
482
483
484
485
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Results
The means, standard deviations and correlations between variables are presented in Table 2.
The average age of the respondents is 38.87. Of the respondents 61% are men. Although the
figure varied by country, 29% of the respondents had university level education: 36% in
Denmark 11% in Italy, 37% in Spain and 41% in Finland. We can see that men are more likely to
leave voluntarily (r ¼ 0.04, p , 0.01).
As predicted, age is negatively related to turnover (r ¼ 2 0.13, p , 0.01). The correlations
are (r ¼ 2 0.18, r ¼ 2 0.10, r ¼ 2 0.15, r ¼ 2 0.13 all p , 0.01) in Denmark, Italy, Spain, and
Finland. Income, which is the ratio of the respondents’ earnings from work to the average
earnings in their occupation for their country, is negatively related to turnover (r ¼ 2 0.06,
p , 0.01). This figure was fairly stable across countries: r ¼ 2 0.05, p , .05; r ¼ 2 0.07,
p , .01, r ¼ 2 0.07, p , .01, r ¼ 2 0.07, p , .01 for Denmark, Italy, Spain, and Finland
respectively. Hypothesis 1 is supported across the countries.
With regard to desirability of movement variables, job satisfaction, as expected, was
negatively related to the turnover decision (r ¼ 2 0.05, p , 0.01). Among ease of movement
variables, unemployment was negatively related to turnover (r ¼ 2 0.04, p , 0.01). But,
interestingly there was no significant relationship between having a university level education
and leaving voluntarily.
Of the withdrawal behaviours, job search behaviour was strongly correlated with turnover
(r ¼ 0.15, p , 0.01). Absenteeism was not significantly related to turnover (r ¼ 2 0.01, ns).
Job embeddedness was negatively correlated with turnover (r ¼ 2 0.10, p , .01). This
relationship was highest (r ¼ 2 0.18, p , 0.01) in Spain and lowest (r ¼ 2 0.07, p , 0.01) in
Italy. Job embeddedness was also positively correlated with job satisfaction (r ¼ 0.31,
p , 0.01) and negatively correlated with job search behaviour (r ¼ 2 0.09, p , 0.01).
It is also interesting to note that income was positively related to gender, which indicates
men in general have higher earnings than women compared to the average in their occupation.
Furthermore, there is a negative relationship between gender and unemployment. Since
unemployment was calculated using unemployment rates based on gender and education level,
the negative relationship between gender and unemployment shows women’s unemployment
rates are higher compared to unemployment rates of men.
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
510
511
506
6
502
503
7
500
8
498
499
501
504
505
507
508
509
512
514
515
517
518
519
521
522
9
10
11
2 0.11**
0.00
0.04**
0.23**
0.04**
0.31**
2 0.48** 2 0.28** 20.07** 20.15**
2 0.03**
0.13**
0.05**
0.09** 2 0.11**
2 0.01
0.01
20.12** 20.10** 2 0.03** 2 0.19**
2 0.10**
0.00
20.02* 20.03**
0.02*
0.00
0.05**
0.00
0.09**
0.19**
0.17** 2 0.12**
0.22** 20.07** 0.03**
2 0.11**
0.34**
0.30**
0.26** 2 0.25**
0.26** 20.06** 0.05** 0.18**
2 0.07**
0.29**
0.32**
0.28** 2 0.25**
0.31** 20.09** 0.05** 0.74** 0.79**
5
497
Notes: *p , .05, **p , .01, N ranges from 9,277 to 10,072.
513
4
496
Voluntary turnover
0.04 0.20
Gender
0.61 0.49
0.04**
Higher education
0.29 0.46
0.02
Age
38.87 10.01 2 0.13**
Income
1.01 0.47 2 0.06**
Unemployment
8.78 4.51 2 0.04**
Job satisfaction
4.36 1.14 2 0.05**
Job search behaviour
0.08 0.27
0.15**
Absenteeism
0.91 3.66 2 0.01
Embeddedness community
1.88 0.68 2 0.07**
Embeddedness organization 1.46 0.74 2 0.08**
Job embeddedness
3.34 1.09 2 0.10**
516
3
493
1
2
3
4
5
6
7
8
9
10
11
12
520
2
494
495
1
492
SD
491
M
Table 2. Means, standard deviations and correlations.
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541
542
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545
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547
548
549
550
551
552
553
554
555
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C. Tanova and B.C. Holtom
Hypothesis 2 asserts that desirability and ease of movement factors along with behavioural
intentions will predict variance in turnover beyond that predicted by personal characteristics.
The results of the Cox Proportional Hazards Regression can be seen in Table 3 and Table 4. In
the first model, gender, age and income were entered into the equation of the hazard function.
From the exponentiated coefficients we can see age reduces the likelihood of changing jobs. The
exponentiated coefficient of 0.91 tells us that each increment increase in age (measured in years
in our study) will reduce the likelihood of turnover decision by 9% when all gender and income
variables are controlled. From Table 4 we can see that the situation is similar in the four
countries. When the role of gender is investigated we see that men are 82% more likely to leave
their jobs compared to women. However, in the four countries we see that the figure gets as high
as 161% more likely in Italy, and 135% more likely in Spain.
As we expected, income had an exponentiated coefficient below one. This indicates as the
income increases, there is a decrease in the likelihood of voluntary turnover. In model two we
entered the desirability and ease of movement variables together with withdrawal behaviours.
There was a statistically significant change in model chi square. And among the new variables
entered, job search behaviour had an impressive 3.36 exponentiated coefficient which would
translate into 236% increase in turnover likelihood if the person was looking for other
employment. Higher education also increased the likelihood of turnover, while job satisfaction
and the unemployment rate reduced it. The results indicate that self reported absenteeism does
not reliably predict turnover in this sample.
Hypothesis 3 states that job embeddedness will improve the prediction of turnover beyond
that predicted by variables representing personal characteristics, desirability and ease of
movement, and withdrawal behaviours. In Tables 3 and 4 we can see that in model 3 job
embeddedness is introduced into the model. In the overall model the chi square change is 51.78
and is significant at p , .001. After the effect of all the previous variables are controlled, job
embeddedness explains a significant amount of variance in actual turnover. The exponentiated
coefficient for community embeddedness is 0.84 which means that an increment in
job embeddedness will bring 16% decrement in actual turnover and the organizational
embeddedness has an exponentiated coefficient of 0.55 which means that it reduces the actual
turnover likelihood by 45%.
570
571
572
573
Table 3. Exponentiated coefficients from proportional hazards regressions of voluntary turnover for the
total sample.
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
Gender
Age
Income
Higher education
Unemployment
Job Satisfaction
Job search behaviour
Absenteeism
Embeddedness community
Embeddedness organization
Chi-square
Change in Chi-square
1
2
1.822***
0.906***
0.373***
1.578***
0.907***
0.431***
1.279*
0.972
0.926
3.355***
0.989
389.44028***
605.33546***
110.424***
3
1.278
0.919***
0.520***
1.616***
0.950**
1.011
3.443***
0.996
0.835*
0.548***
678.04715***
51.781***
Notes: Values exceeding 1 for exponentiated regression coefficients (eb) indicate a positive effect on turnover risk as the
variable increases, while values below 1 indicate a negative effect as the variable decreases; ***P , 0.001;
**P , 0.01; *P , 0.05; N ¼ 8,952.
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
2,849
125.355***
182.499***
37.152***
255.430***
68.767***
1.193
0.943***
0.757
1.333
0.951
0.900
2.014**
0.938
0.677**
0.297***
b
1,797
97.900***
Finland
2.017**
0.896***
0.317***
2.003
0.915***
0.541**
0.829
0.990
0.786***
2.257***
0.930
Spain
2.347***
0.912***
0.484***
165.551***
33.755***
1.894*
0.906***
0.299***
1.074
0.989
0.956
4.422***
0.979
159.624***
27.989***
1.322
0.913***
0.287**
2.415*
0.900
0.928
3.564***
1.004
0.991
0.502**
188.396***
12.082**
1.685
0.921***
0.373**
0.967
0.928
1.037
4.567***
0.985
0.635**
0.569*
169.457***
7.563*
3
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601
Notes: *P , 0.05 **P , 0.01 ***P , 0.001; Values exceeding 1 for exponentiated regression coefficients (e ) indicate a positive effect on turnover risk as the variable increases,
while values below 1 indicate a negative effect as the variable increases.
Chi-square
Change in Chi-square
N ¼
Gender
Age
Income
Higher education
Unemployment
Job Satisfaction
Job search behavior
Absenteeism
Embeddedness community
Embeddedness organization
2,667
1,571
90.132***
602
176.562***
16.693***
598
1.913
0.904***
0.228***
2.441*
0.950
0.884
3.798***
1.001
597
Italy
2.615**
0.906***
0.169***
596
151.350***
25.108***
1.056
0.896***
0.645
1.544
0.944
0.856
2.696***
0.997
0.780
0.466***
2
591
110.251***
1.264
0.888***
0.463*
1.363
1.017
0.834
2.923***
0.994
1
590
Chi-square
Change in Chi-square
N ¼
Denmark
1.295
0.888***
0.406*
3
589
Gender
Age
Income
Higher education
Unemployment
Job Satisfaction
Job search behaviour
Absenteeism
Embeddedness community
Embeddedness organization
2
599
1
592
593
Model
Table 4. Exponentiated coefficients from proportional hazards regressions of voluntary turnover for 4 countries.
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Discussion
The results of this study demonstrate the continuing utility of March and Simon’s (1958) model
of turnover, where desirability and ease of movement are regarded as important predictors of
turnover. Importantly, however, a new theory of voluntary turnover – job embeddedness – also
plays a key role in predicting turnover even after extensive controls. This indicates that the
turnover decision is not only influenced by the individual’s attitudes towards work or about
the actual opportunities in the labour market, but also influenced by a number of interrelated
connections both on and off the job. Moreover, these results point to the generalizability of the
construct. Notwithstanding differences in labour laws, cultural factors and unemployment rates
between Europe and the US, job embeddedness appears to predict turnover effectively on both
continents – at least in the four different countries where it was possible to conduct a rigorous test.
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Practical implications
The application of job embeddedness has important ramifications for line managers. Leaders in
European organizations who are worried about losing their most valuable employees should not
only study external pay equity or the job satisfaction of their employees, but they should also try
to identify viable methods for helping employees become embedded in the organization and the
community. For example, the managers could establish mentoring programmes to strengthen
the links employees have with others in the organization. Or managers might encourage work
groups to take a prominent role in deciding who to hire into the work group. To strengthen the
links with the community, organizations can support community outreach programmes to give
their employees opportunities to volunteer and become more integrated into their communities.
To strengthen the fit between the individual and the organization, employers need to be
thoughtful in the recruitment and selection of their employees. They need to provide realistic
information to the candidates before employment and subsequently, employers need to assist
their employees in planning their careers. To strengthen the fit between the individual and the
community, the employers can provide information about community events or amenities.
Employers should also consider locating plants or offices near housing sectors that offer
affordable dwellings for employees.
Finally, organizations are encouraged to offer unique benefits that are hard for other
companies to replicate. Family-friendly hours or benefits provide strong incentives for workers
with children to stay for the long term. Allowing individuals to customize their workspace
creates a sense of home or familiarity that might be hard to find in other companies. Sacrifice
incurred by potentially leaving the community can be systematically increased by providing
credit counselling, help finding real estate agents, and partnerships with non-profits that help
employees navigate the home-buying process.
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Strengths and limitations
There are a number of strengths to this article. One important factor that differentiates this study
from others is the use of actual turnover data rather than turnover intentions. Even though there
are theoretical arguments and empirical evidence that turnover intentions are very good
predictors of actual turnover (Ajzen 2002; van Breukelen et al. 2004), these studies ignore the
employees who do not plan to leave the company but do leave because of internal or external
events (Lee, Mitchell, Holtom, Hill, and McDaniel 1999; Morrell et al. 2004a). A second
strength is the use of a large-scale, multi-country, longitudinal data set where Time 1 variables
were used to predict turnover at Time 2.
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At the same time, there are limitations that must be acknowledged. First, the data set was not
specifically designed to test the theory of job embeddedness. The items chosen exhibit face
validity for each of the six sub-dimensions and predictive validity in terms of both job search
behaviour and actual turnover. However, it is possible that the items have not fully captured the
construct domain. A second limitation is the lack of greater geographic or cultural representation
of the diverse European countries. Unfortunately, complete data was only available for the four
countries studied.
Nevertheless, we believe that this study makes an important contribution to understanding
about voluntary turnover in the European Union. Further, when combined with other studies
(e.g., Mallol et al. forthcoming), it points to the generalizability of job embeddedness theory.
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Acknowledgements
The present research was funded by the European Commission under the 6th Framework Programme’s
Research Infrastructures Action (Transnational Access contract RITA 026040) hosted by IRISS-C/I at
CEPS/INSTEAD, Differdange (Luxembourg).
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