Comparing Organizational and Individual Support, Employee Engagement and
Turnover Intentions across public, NFP and FP organizations
This paper uses positive organizational behaviour and Social Exchange theoretical
frameworks to compare the impact of individual and organizational support on
employee engagement and turnover intentions across public sector (PS), Not-ForProfit (NFP) and For-Profit (FP) organizations.
Paper-based survey data was collected from 250 public, 113 NFP and 280 private
sector employees (n=643). Structural Equation Modelling (SEM) using AMOS and an
ANOVA were used to undertake analysis.
The SEM findings indicate a good fitting model, with some paths confirmed,
indicating that individual support and organisational type (PS, NFP, FP) explained
over fifty percent of engagement and organisational type predicted turnover
intentions. The ANOVA results show that NFP and FP have similarly higher
satisfaction with management, higher engagement and lower turnover intentions.
Keywords; management support; employee engagement; turnover intentions, NFP
Comparing Organizational and Individual Support, Employee Engagement and
Turnover Intentions across public, NFP and FP organizations
INTRODUCTION
This paper uses a positive organizational behaviour (POB) (Luthan et al, 2006) and a
Social Exchange theoretical (SET) (Cropensano et al 2005) framework to compare the
impact of individual and organizational support on one type of employees across
public, private and Not-for-Profit (NFP) organizations. This study is positioned on a
platform of comparative research showing the significant differences between public
and private sector organizations and their employees’ work characteristics and
attitudes (Rainey, 2014) and some similarities in attitudes between public and NFP
employees (Borzaga & Tortia, 2006). Also Bulock and Stritch (2015); Brunetto et al
(2012, 2015a, b, c, 2016) and Bloom, Genaskos and Sadun (2012) comparing
different aspects of performance monitoring, target-setting and people management
have identified significant differences in management practices and employee
outcomes across public and private sector employees of numerous countries
(including USA, Australia, UK, Brazil, Italy and Malta), however, there have been
less studies comparing employee perceptions of management and outcomes across the
three sectors .
Using POB and SET behavioural theories, two factors have been identified as
positively impacting on employee outcomes: organizational support which depends
of the quality of management support perceived by employees and individual support
which depends on the quality of individual attributes (internal strength) an employee
has to buffer himself/herself against any difficult situations. This paper compares the
impact of these two types of employee support across public, private and NFP
organizations. The contribution of the paper is that whilst there has been some
research attention comparing the impact of organizational support on employee
outcomes across the three sectors (although mainly across public and private sector
employees), there has been minimal research comparing the impact of individual
support on employees across the three sectors, and even less comparing the
differential impacts of individual and organizational support on their outcomes.
One of the differentiating factors affecting employee outcomes across the three
sectors appears to be the implementation of public sector reforms (New Public
Management (NPM)) increasing the discretionary power of managers in some
countries especially in the past decade since the GFC (Kuipers et al (2014). Pollitt and
Bouckaert (2011, 118) differentiated public organizations between “core NPM”
countries (such as USA, Australia, UK and NZ) that has implemented budgetary
controls, increased accountability and performance measures with the aim of
improving management systems; and laggards (such as Italy, France and Germany)
that selectively implemented some reforms at their own pace. As a consequence, the
quality of organizational support was affected in public organizations by the
implementation of a selection of private sector management reforms at different rates
aimed at improving “performance-, cost-, efficiency” (Diefenbach 2009, 893). Whilst
all sectors have been operating in increasingly resource-constrained environments, in
core NPM countries, the way reforms have been implemented has negatively affected
how change was managed (Diefenbach, 2009) especially in the healthcare sector
(Porter & Lee, 2013).
On the other hand, all employees enter an organization with a certain level of
individual support (attributes) depending on their level of psychological capital
(PsyCap). The higher their level of PsyCap, the greater their access to personal
emotional resources which helps them perceive the “positive aspects” of life and as a
consequence, it provides them with a natural buffer against stress (Luthan et al, 2009).
PsyCap is a factor discovered within the discipline of Positive Organizational
Behaviour (POB) and as such POB can be used as a lens for examining how it
impacts employee outcomes. The contribution of POB to NFP research is that positive
individual attributes (such as high PsyCap) delivers a somewhat untapped source of
higher employee outcomes which benefits the employee, their colleagues, managers
and public (through better quality services) and in turn, increases organizational
effectiveness (Story et al, 2013).
In this paper we compare the impact of individual and organizational support on
employee engagement and turnover intentions across public, private and NFP
organizations. Previous research by Borzago and Tortia (2006) suggests similarities in
attitudes for employees in NFP and public settings, however, they did identify
potential differences in motivation between the two groups arguing that employees
choose NFP organization because they believed in the mission and for intrinsic
reasons relating to autonomy and growth. Recent research about public sector
employees by Bakker (2015) suggests that employee engagement is linked to public
sector motivation, with chronic under-resourcing thwarting engagement and public
sector motivation over time. It will therefore be interesting to benchmark engagement
across the three sectors. Previous research has compared the impact of organizational
support on engagement and turnover intentions (across all three sectors: See Public
sector - Brunetto, Shacklock, Teo, & Farr-Wharton, 2014: Private sector - Bakker,
Demerouti & Sanz-Vergel, 2014; Saks, 2008; NFP – Parkes & Langford, 2007) and
across the public and private sector of Australia and Italy (Brunetto, Xerri, Trinchero,
Farr-Wharton, Shacklock & Borgonovi, 2016). The contribution of this paper is it
compares the impact of both individual attributes and organizational support on
employee outcomes across the three sectors. POB research provides a new platform of
research focused on how organizations can use employee’s positive attributes to
achieve increased organizational effectiveness (Story et al, 2013; Avert et al, 2011).
The research questions guiding the study are:
RQ1: What is the impact of individual and organizational support on the engagement
and turnover intentions of employees working in public, NFP and FP organizational
settings?
RQ2: What are similarities and differences in the impact of individual and
organizational support on the engagement and turnover intentions of employees
working in public, NFP and FP organizational settings?
The findings provide benchmarks of the impact of individual and organizational
support across the three types of organizations within a core-NPM country and
evidence-based recommendations for improving management practices. The next
section provides a review of relevant literature with hypotheses emerging to identify
the void.
BACKGROUND
Theoretical Frameworks
1. Positive Organizational Behaviour (POB) Theory
POB is a relatively new discipline, and it is growing quickly as academics and
practitioners become aware of its potential value in improving employee outcomes by
building their psycho-emotional capacity (Luthan 2006; Avery et al., 2008; Story et
al., 2013). Within the POB umbrella, those constructs which are positive in nature and
impact are identified so that organizations can use them to increase their
effectiveness. In particular, variables such as PsyCap have been discovered (Luthan et
al., 2006) which could benefit managers across public, NFP and private organizations
to achieve higher employee outcomes.
2. Social Exchange Theory
SET is based on the idea that effective workplace relationships build trust and respect,
which over time leads to mutual reciprocity. This means that when managers treat
employees with respect, employees develop a feeling of obligation to respond
positively in return (Shore et al, 2009). On the other hand, if employees are
continually under-resourced, but still expected to meet the client’s needs, continuous
under-reciprocation is likely to promote a breakdown of the social exchange
relationship (Cropanzano & Mitchell, 2005). When employees perceive poor support
from management, they are likely to respond by reducing their positive work
outcomes in the organization (Brunetto et al, 2014, 2015, 2016).
Independent Variables
SET variable – Leader Member Exchange (LMX)
Leader-Member Exchange (LMX) theory argues that in the workplace, supervisors
form good relationships with some employees (in-group) and poorer relationships
with others (out-group), however, increasingly organizations expect that supervisors
will form effective relationships with all those they supervise so that mutual
reciprocity is created in terms of resourcing, support and information (Dulebohn,
Bommer, Liden, Brouer, & Ferris, 2012; Brunetto et al, 2014, 2015).
An effective relationship is characterized by supervisors providing adequate resources
respectfully for employees to do their job, however, a factor impacting the quality of
LMX is the resource-constrained environment across all sectors, which has “strained”
supervisor – employee relationships because subordinates are increasingly expected
“to do more with less”, and as a consequence, comparisons of employees across the
public and private sector suggest public sector employees have lower levels of
satisfaction (Diefenback 2009, Brunetto 2015) and comparisons across countries
suggest that core-NPM countries (Australia) have lower supervisor satisfaction
compared with NPM-laggards (Italy) (Brunetto et al, 2016). Whilst there is minimal
empirical research comparing NFP with FP employee outcomes, research suggests
that public and NFP employee experience similar work environment s and lower pay
compared with FP employees (Light, 2008) and therefore we expect FP employees to
perceive higher levels of organizational support compared with public and NFP
employees doing the same job.
POB variable: Psychological Capital (PsyCap)
Luthan et al (2006) identified PsyCap as a higher –order construct comprising four
dimensions: self-efficacy (that encapsulates the extent to which an employee has the
knowledge, skills and personal qualities to successfully complete a task); optimism
(that encapsulates the extent to which an employee has a personal vision which shapes
the way they face each new activity so as to achieve positive outcomes); hope (that
encapsulates the extent to which an employee deliberately plans and acts on their
beliefs to achieve positive outcomes); and resilience (that encapsulates the extent to
which an employee can bounce back from a difficult position) (Avery et al., 2009).
Together and separately, the findings using mostly US private sector samples suggest
that high PsyCap is associated with high employee work job outcomes (Avery et al.,
2011). Even more importantly, the findings suggest that if managers have high
PsyCap, then it is likely that their employees will also have higher PsyCap and those
with low PsyCap can be upskilled (Story et al, 2013). Hence we compare the PsyCap
levels across public, NPF and FP sector nurses and assistants to determine the level of
individual support available to those undertaking both clinical and emotional labor in
the healthcare sector.
In terms of past POB research, Storey et al. (2013) found that high LMX is associated
with high PsyCap, and therefore we expect to find the same outcome (See H1).
H1: High LMX is associated with high PsyCap.
Employee Outcomes
1. Employee Engagement
Schaufeli et al. (2003) conceptualised engagement as a function of vigour, (energy),
‘dedication’ (enthusiasm), and ‘absorption’ (working happily) of employees.
Research suggests that highly engaged employees have higher work outcomes, so
there are benefits for organizations that have highly engaged employees (Kular et al,
2008). In terms of antecedents, Trinchero et al (2014) found that high LMX was
associated with high engagement for Italian nurses and likewise, the findings were
similar for Australian nurses and police officers (Brunetto et al, 2014). Also, research
by Serrano and Reichard (2011) argued that managers can coach, upskill and
empower employees to ensure higher levels of PsyCap as a means of increasing
engagement. Avery et al (2008) found that high PsyCap was associated with high
engagement which in turn reduced change resistance for those organizations
undergoing change. Consequently we expect to find similar outcomes for healthcare
workers working in public, NFP and FP organizations.
H2: High LMX is associated with high engagement.
H3: High PsyCap is associated with high engagement.
Variable 4: Turnover Intentions
Turnover intention is a construct capturing the willingness of employees to leave an
organization in the next year. There is a shortage of different types of healthcare
workers including nurses in numerous countries and a higher than normal turnover
rate is one cause of the shortage (Brunetto et al, 2014), hence finding the antecedents
of turnover intentions is an important pursuits because replacing skilled healthcare
employees is costly to terms of training, recruiting and orientating within the
organizational setting as well as negatively impacting patient safety (Buerhaus 2008).
Past research by Avery et al (2011) found that high PsyCap was associated with low
turnover intentions; however, this has not been tested for healthcare workers such as
nurses. On the other hand, past research found that high LMX was associated with
low turnover intentions for nurses (Brunetto et al, 2014).
H4: High LMX is associated with low turnover intentions.
H5: High PsyCap is associated with low turnover intentions.
H6: High engagement is associated with low turnover intentions.
Context: Public NFP and FP organizational settings
Academics claim that there are significant differences across NFP, FP and public
sector workplaces for employees because of differences in their missions and
management approaches (Borzaga and Tortia 2006), but there is minimal empirical
evidence to support the claim. Public and NFP employees are often assumed to have
similar attitudes because it is argued that they are more likely to be motivated by
intrinsic rewards compared with FP employees. It was also found that public and NFP
employees stay in their job despite the pay gap and staff /resources shortages
conditions because they get satisfaction from fulfilling their intrinsic motivation,
which includes the opportunity for self-fulfilment and improving society – especially
in the case of NFP employees (Borzaga & Tortia 2006; Light 2008). Hence,
employees undertaking similar roles across the public and NFP on the one hand, and
on the other hand – in FP settings experience differences in salaries, benefits, task,
and performance criteria (Liu & Tang 2011). However, Borzago and Tortia (2006)
also found some differences between employees in NFP and public settings arguing
that employees choose NFP organization because they believed in the mission and for
intrinsic reasons relating to autonomy and growth. For this reason we expect
engagement to be highest for NFP compared with public sector and FP nurses and
assistants.
H7: NFP nurses and PCs will have higher engagement compared with nurses in
public and FP hospitals/nursing homes.
As stated, employees in NFP organizations choose to work there because they believe
it gives them access to more process-related rewards such as professional
development, creativity of the job, and recognition of contribution to society (Borzaga
and Tortia, 2006). Also Narcy (2011) found that monetary incentives had limited
impact on French workers effectiveness in the public and the NFP sector, but
especially for NFP employees. For this reason we expect that the PsyCap will be
highest for NFP employees because they are motivated by intrinsic factors, and
therefore we argue that they are more likely to have higher psycho-emotive
resources/attributes supporting their life choices. For this reason we expect NFP
employees to have higher PsyCap and lower turnover intentions.
H8: NFP employees will have higher PsyCap compared with public and FP
employees.
H9: NFP employees will have lower turnover compared with public and FP
employees.
However, in Australia, in the case of nurses, there is a movement of public sector
nurses to the private sector because nurses believe that the management is better,
workload is less and flexibility is greater (Brunetto et al, 2012). We therefore expect
employees to have higher levels of satisfaction with management in the private sector.
H10: Public and NFP employees will have lower satisfaction with management
compared with FP employees.
METHODS
Justifying the Population group
In comparative studies it is important for validity reasons to ensure comparisons are
across “apples and apples” and not “apples and oranges”. Hence, this study is focused
on one type of employee – nurses and healthcare assistants found in large numbers
across FP, NFP and PS organizations. Nurses within healthcare organisations were
chosen for comparison because it is a sector that is expanding quickly and in many
countries both PS and NFPs are subsidized and therefore employee management
effectiveness is vital to meet future demand. However, comparisons across the three
sectors are rarely, especially in healthcare management – despite the growing
presence of NFP (Australian Institute of Health and Welfare (AIHW), 2014).
Australia has a healthcare system similar to that of Canada with the Australian
government (and the state and territory ministers) co-ordinating the health care system
and providing over 60% of acute services. However, there is a growing presence of
NFP and FP organizations across both the provision of acute and sub-acute health
services (AIHW, 2014). The workforce – both professionals (doctors, nurses, allied
health), and non-professionals (nursing assistants, personal carers) operate across the
three organizational types with the increasing use of contracting out of services from
public to private and NFP hospitals and vice versa to meet demand, in turn making
the borders blurry. Nurses make up the bulk of the healthcare system. Healthcare
services in acute settings (such as hospitals) are provided by registered nurses (RN)
(with university qualifications since the 1990s), enrolled nurses (EN) (with a diploma)
and assistants in nursing (AIN) (with a certificate). In sub-acute setting (such as
nursing homes), alongside RN, EN and AINs, in some states, there may also be
certificate-trained personal carers (PCs) in place of AINs (AIHW, 2014).
Justifying the Sample
The study compares healthcare workers such as nurses (RNs, ENs) and assistants
(AINs/PCs) working across healthcare settings (hospital/nursing homes) because they
use both clinical expertise and emotional resources in their job of caring for
patients/residents in varying stages of recovery or dying and we argue that without
adequate organizational and individual support, engagement would be low and
turnover intentions would be high.
Process for collecting data: Paper-based survey data was collected from nurses and
AIN/PCs working in PS, NFP and FP hospitals/nursing homes. Public Hospital
nurses/assistants: 750 surveys were distributed and 237 completed surveys (response
rate of 32%) were received from nurses employed at five general acute Australian
hospitals. The main demographic characteristics were a mostly female group (83.2%)
aged between 41 and 60 years of age (64.8%), and nearly 90% had nursing or nursing
assistant qualifications. NFP nurses/assistants: 250 surveys were distributed and 111
completed their surveys (44% response rate). The main demographic characteristics
were a mostly female group (88.7%) aged between 41 and 60 years of age (40.4%),
and nearly 80% had nursing or nursing assistant qualifications. FP nurses/assistants:
1000 surveys were distributed and 280 completed surveys (28% response rate) were
received from nurses working in 6 general acute hospitals. The main demographic
characteristics were a mostly female group (86%) aged between 41 and 60 years of
age (40.6%), and nearly 87% had nursing or nursing assistant qualifications.
Measures: We used previously validated scales to operationalise the constructs in the
path model using SEM (Amos). These were measured on a six-point Likert-type
scale, ranging from 1 (strongly disagree) to 6 (strongly agree). Leader–member
exchange (LMX) validated uni-dimensional scale (LMX-7), developed by Graen and
Uhl-Bien (1995) is used to capture employee’s satisfaction with the relationship with
their supervisor. An example of a statement is, ‘I am certain to what extent my Line
Manager will go to back me up in my decision-making’. Psychological capital
(PsyCap) was measured using the four subscales from Luthans et al. (2006).
Following earlier research, this construct was operationalized as a second order, latent
variable, comprising four sub-dimensions of PsyCap (efficacy, hope, resilience, and
optimism). Sample items included “When things are uncertain for me at work, I
usually expect the best” and “I always look on the bright side of things regarding
my job”. Employee engagement was measured using a nine-item scale from Schaufeli
and Bakker (2003). Sample items included, ‘At my work, I feel bursting with energy’
and ‘I find the work that I do full of meaning and purpose.’ Intention to quit was
measured using a three-item scale from Meyer, Allen and Smith (1993) to
operationalise intention to quit - the dependent variable. Sample items included, ‘It is
likely that I would search for a job in another organisation.’ We used one exogenous
(observed) variable to represent sector in our path model and ANOVA analysis, 1 =
FP, 2 = NFP, 3 = PS employees. SEM standardises variables when computing
analysis. We also used one control variable being gender, where 1= male, and 2 =
female.
A confirmatory factor analysis (CFA) using AMOS 22 was undertaken for all multiitem measures (measurement model). One LMX item, twelve PsyCap items and two
engagement items were deleted. The composite reliability coefficients of the multiitem measures demonstrated good internal consistency (Table 1).
<<insert table 1 here>>
All items displayed acceptable normality, with kurtosis and skewness scores between
the required -2 and +2 (George & Mallery, 2010). Furthermore, model fit scores (for
the structural model) were well within the acceptable thresholds indicated by Hair et
al., (2011). As such, the comparative fit index (CFI) was .940 (above the required .9),
the Tucker-Lewis index was .933 (above the required .9), the RMSEA was .047
(below the required .05), and the Chi-square of degree of freedom was 2.377 (below
the required 5).
Using the process outlined by Podsakoff et al. (2003) to reduce common method bias,
the measurement of the criterion and predictor variables were separated and the
surveys were completed anonymously. A Harmon’s single factor analysis test was
undertaken to determine the possibility of common method variance (Podsakoff et al.,
2003). One factor was explained by 27% of the variance, indicating a low chance that
common method variance had an impact on the data.
RESULTS
<<insert table 2 here>>
Optimally, group comparison analysis would be used to compare model, mean and
path equivalence across the three employee sample groups (FP, NFP and PS) (Byrne,
2010). However, the small sample size of the NFP employees meant that a
comparable model fit score could not be realistically achieved. As such, the samples
were grouped together, and the sector was included as an exogenous (observed)
variable in the model. Through this the model can account for the variance of latent
variables – LMX, PsyCap, Employee Engagement and Turnover Intention, from
membership within a particular sector. The results of the path model are displayed
below.
<<insert figure 1 here>>
The path model indicates that membership within the sector is significantly related to
LMX (R=-.142***, R2=.02). In combination, membership to the sector, in addition to
PsyCap, were a significant predictor of employee engagement (R=-.60***,
R=.671*** respectively, R2=.55). Additionally, LMX and PsyCap were significantly
linked (.417***). Finally, the only significant predictor of Turnover Intention was
sector membership (R=.542***, R2=.36).
In summary, the path model offers two underpinning insights. Firstly, sector
membership (FP, NFP and PS) significantly impacts on the kind of supervisorsubordinate relationship that ensues in a workplace, and has an impact on employees’
level of engagement and their turnover intentions. Secondly, in all settings, the
biggest predictor of employee engagement was the level of individual psychological
capital (PsyCap), and LMX had a significant relationship to this variable. The
intersection between these points is that a large portion (55%) of employee
engagement is predicted by employee’s PsyCap in combination with the sector (FP,
NFP, PS) in which they work.
Hypotheses 7-10 were tested using an ANOVA examining for significance in
similarity and differences for the means for LMX, PsyCap, Engagement and Turnover
Intentions (1=Strongly Disagree – 6=Strongly Agree). The findings suggest
significant differences in LMX, PsyCap, Engagement and Turnover Intentions across
the three groups and all hypotheses were accepted, and the results are displayed in the
following table.
<<insert table 3 here>>
Private
LMX
PsyCap
Mean
S.D.
Mean
S.D.
Mean
S.D.
Mean
S.D.
5.1190
.58983
4.6902
.50427
4.6643
.70428
2.3583
1.15495
Employee
Engagement
Turnover
Intention
***p<.001, **p<.01, *p<.05
NFP
5.0060
.71912
5.1007
.50027
4.9550
.70709
2.4084
1.28211
Public
4.9086
.73715
4.5014
.62742
4.2245
.81719
4.2799
1.31160
F score
6.320**
44.330***
41.843***
174.996***
Hypothesis 7 argued that engagement would be higher for NFP nurses and assistants
compared with FP and public sector nurses. The means for engagement are: FP
(m=4.66, SD=.589), NFP (m=4.95, SD=.707) and public (m=4.22, SD= .831). The
ANOVA results (F=41.843, p<.001) indicates that NFP nurses are significantly more
engaged as hypothesized, compared with PS nurses and assistants, who were the least
engaged of the three groups. Hypothesis 8 expected that NFP nurses/ assistants would
have higher PsyCap compared with those in FP and public hospitals. This is accepted
because the ANOVA results comparing the means (F=44.330, p<.001) indicate that
PsyCap is significantly higher for NFP (m=5.1, SD=.5) compared with FP (m=4.69,
SD=.5), and public (m=4.50, SD= .62) groups. Hypothesis 9 argued that the turnover
rate of NFP nurses and their assistants would be lower than the other two groups and
this was partially accepted because the ANOVA results (F=174.99, p<.001) indicate
that the means for turnover intentions were lowest for NFP (m=2.358, SD=1.15) and
FP groups (m=2.4, SD=1.28) and significantly different only to the public group
(m=3.866, SD= .914). Also Hypothesis 10 argued that FP nurses and assistants would
have the highest mean for management (LMX) compared with public and NFP
groups. The hypothesis was partially accepted because the ANOVA results
(F=11.2438, p<.001) comparing the means for LMX is FP (m=5.119, SD=.59), NFP
(m=5, SD=.719 and public (m=4.819, SD= .85), showing that the means for the FP
and the NFP group are significantly higher than the mean for the public group.
DISCUSSION AND IMPLICATIONS
This paper aimed to benchmark NFP employee outcomes with FP and PS employees.
In particular, the paper compared the impact of individual and organizational support
on the engagement and turnover intentions of one group of employees - nurses and
nursing assistants working in public, NFP and private settings. The findings indicate
that for nurses/assistants, individual support from PsyCap was much stronger in
impact on engagement than the organizational support from supervisors. Whilst
previous research indicated that organizational support via LMX and individual
support via PsyCap did significantly impact employee performance (Brunetto et al,
2015; Story et al, 2013), the contribution of this paper is that it showed that for nurses
and their assistants (AINs/PCs), the impact of individual support from PsyCap on
engagement was much stronger.
Together PsyCap and the sector in which employees worked (FP, NFP, PS)
determined over half of the engagement of this type of employee – nurses and nursing
assistants. The outcome from this study adds an interesting dimension to the research
examining the link between organizational support (in the form of job resources),
engagement and motivation in the public sector (Bakker, 2015; Lavigna, 2015).
Bakker (2015) found that long term under-resourcing eroded both engagement and
over time, public sector motivation. One interpretation of the findings from this study
is that the organizational setting of employees (FP, NFP, PS) in turn affects the
resources managers have to work with and the most engaged employees are those
with the most resources, because resourcing adequately is part of the SET agreement
involving managers treating employees with respect (Cropensano et al, 2005).
Additionally, research by Light (2008), Borzaga and Tortia, (2006) and Narcy (2011)
suggest that NFP employees are more intrinsically motivated than other types of
employees and therefore it is not surprising that NFP employees had the highest
engagement across the three groups.
Additionally, in contrast to previous research (See Avery et al, 2008 in the case of the
impact of PsyCap on turnover intentions and Brunetto et al (2014) in the case of LMX
on turnover intentions) in this study of nurses/assistants, neither PsyCap nor LMX
significantly impacted turnover intentions directly. The strongest predictor of turnover
intentions was the sector in which they are employed and the group with the highest
turnover intention were PS employees. FP and NFP employees had similarly low
turnover intentions. This finding supports previous research suggesting that PS
employees have been negatively impacted by reforms, especially in core-NPM
countries (Brunetto et al (2012, 2015, 2016; Bloom, et al, 2012) and also explains the
movement of nurses/assistants away from the PS towards the FP sector.
Previous research by Light (2008), Borzaga and Tortia, (2006) and Narcy (2011)
identified strong similarities between NFP and PS work contexts but with differences
in their attitudes and impact. We confirmed significant differences in the perceptions
of PS and NFP employees. The contribution of this paper is that the perceptions of FP
and NFP employees are similar and in most cases, significantly different to PS
employees’ perceptions of psychological capital, management, engagement and
turnover intentions. These findings significantly challenge past research that tended to
place public and NFP employees together arguing that the mission and lower salaries
compared with FP employees led to them having similar attitudes and work outcomes.
In Australia the base salaries for nurses and their assistants are controlled by
legislation and short term contracts are a feature across all types of organizations,
hence the differentiating factor is more likely to be missions of each type of
organization. The findings from this study indicate that the work context for nurses
and AIN/PC working in NFP and FP organisations is similarly significantly better
than working in public sector organisations. Turnover intentions were also less for
NFP and FP groups and significantly greater for public sector employees. More
empirically based research across other sectors is required to ensure that the finding of
this study of nurses/assistants in the healthcare sector is not an aberration.
Another potential reason as to why NFP nurses/assistants have significantly higher
engagement and lower turnover intentions could be explained by their significantly
higher PsyCap compared with the public and FP cohorts. One explanation could be
that people with higher PsyCap self-select to work in NFP because they believe in the
mission and objectives of the organization. In this study, PsyCap predicted
engagement and high engagement predicted low turnover intentions. This is important
information because as the healthcare sector grows and in particular, the segment
made up of NFP and FP organizations expands, it is important to know what type of
people to recruit and how to manage them so as to achieve their potential.
Additionally, past public sector reforms aimed to embed private sector management
tools in the public sector, such that employees working in the public sector would
experience a similar work context as an employee working in the private sector.
However, the empirical findings from this paper suggest that public sector employees
experience a significantly worse work context. One explanation is that the way
reforms were implemented in the public sector was overshadowed by a monetary
imperative to cut costs which over-shadowed all other agendas.
The limitations of this study include the use of only one type of employee - nurses
and nursing assistants and therefore further studies are required to examine other
types of employees that can be found across the three organizational types.
Additionally, the study took place in one country –Australia, and therefore further
studies are required that examine the similarities and differences for employees
undertaking similar work in different types of countries – using examples from both
core-NPM and NPM-laggard countries as per Pollitt and Bouckaert (2011)
classifications. A further potential limitation is common methods bias from using selfreport surveys. We followed Podsakoff, MacKenzie, Lee and Podsakoff (2003)
process of separating the measurement of the criterion and predictor variables
psychologically and ensuring the survey is anonymous and the results of a Harmon’s
one-factor test and conducted a common latent factor analysis using AMOS, and the
results show that common method bias is not an issue.
Conclusion
Both SET and POB frameworks were used to compare individual and organizational
support across the three types of organizations. Using the two frameworks, the two
recommendations are made. Using the SET framework, the implications of the
research are that management practices are better in FP and NFP organizations and
therefore public sector organizations have to focus far more on improving
management practices. One evidence-based approach is to improve management
competencies in the area of workplace relationships using SET principles in the NFP
sector. The lack of performance measures so as to promote improved people
management is in stark contrast to the abundance of performance measures use to
audit financial performance across public sector organisations at least (such as health,
education, local government).
The second recommendation emerging from using POB as a framework is that
because the positive impact of high PsyCap is clearly evident on the increased
engagement and lower turnover intentions of nurses and their assistants, upskilling in
PsyCap along with a change in recruitment procedures to ensure pre-testing for
PsyCap has a lot of advantages for PNF organizations that require both expertise and
emotional resources to undertake tasks appropriately.
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Table 1: Reliability and Cross-Correlation Scores
C.R. AVE
MSV
ASV
1
1. Employee .868 .525
.514
.245
.724
Engagement
2. LMX
.868 .527
.182
.111
.371
3. PsyCap
.915 .561
.514
.249
.717
4. Turnover
.915 .782
.084
.049
-.290
Intention
2
3
4
.726
.427
-.112
.749
-.225
.885
Table 2: Correlation Analysis
1
1. Sector (Private, NFP, Public)
2. LMX
3. PsyCap
4. Employee Engagement
5. Turnover Intention
6. Gender
***p<.001, **p<.01, *p<.05
n = 628
2
3
4
-.143***
1
-.129**
.427***
1
-.255***
.371***
.718***
1
.572***
-.112*
-.226**
-.299***
1
.066
-.018
.095*
.127**
-.053
Table 3: Analysis of Means and Group Mean Variance (ANOVA)
Private
NFP
Public
LMX
PsyCap
Mean
S.D.
Mean
5.1190
.58983
4.6902
5
6
1
5.0060
.71912
5.1007
4.9086
.73715
4.5014
F score
6.320**
44.330***
1
S.D.
.50427
Mean
4.6643
Employee
S.D.
.70428
Engagement
Mean
2.3583
Turnover
S.D.
1.15495
Intention
***p<.001, **p<.01, *p<.05
n = 628
Figure 1: Path Model
.50027
4.9550
.70709
2.4084
1.28211
.62742
4.2245
.81719
4.2799
1.31160
41.843***
174.996***