Adapting The Revised Self-Leadership
Questionnaire to The Portuguese Context
Pedro Marques-Quinteiro, Luís Alberto
Curral & Ana Margarida Passos
Social Indicators Research
An International and
Interdisciplinary Journal for
Quality-of-Life Measurement
ISSN 0303-8300
Soc Indic Res
DOI 10.1007/
s11205-011-9893-7
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Soc Indic Res
DOI 10.1007/s11205-011-9893-7
Adapting The Revised Self-Leadership Questionnaire
to The Portuguese Context
Pedro Marques-Quinteiro • Luı́s Alberto Curral • Ana Margarida Passos
Accepted: 13 June 2011
Ó Springer Science+Business Media B.V. 2011
Abstract This study aimed to adapt the Revised Self-Leadership Questionnaire (RSLQ)
(Houghton and Neck in J Manag Psychol 17(8):672–691, 2002) for the Portuguese population. 720 professionals, and university and post-graduate students participated in this study.
The RSLQ factorial structure was accessed through exploratory and multi group confirmatory factor analysis. From the 9 sub dimensional and 34 items original scale, only 7 sub
dimensions and 21 items were preserved. The model tested through cross-validation multigroup analysis proved to be totally invariant across the groups, suggesting good model fit.
Keywords Self-leadership Scale adaptation Structural equation modeling Multi-group confirmatory factor analysis
1 Introduction
1.1 The Revised Self-Leadership Questionnaire
The development and adaptation of measurement scales to European and Portuguese
speaking countries is fundamental for theoretical developments and empirical research
(Crocetti et al. 2010; Gouveia et al. 2009; Spagnoli et al. 2010).
P. Marques-Quinteiro (&)
Doctoral Program of Human Resources Management, Instituto Universitário de Lisboa
(ISCTE-IUL), UNIDE, Lisbon, Portugal
e-mail: Pedro_Marques_Quinteiro@iscte.pt
L. A. Curral
Department of WOP-Psychology, Faculdade de Psicologia, Universidade de Lisboa,
Alameda da Universidade, 1649-013 Lisbon, Portugal
e-mail: lcurral@fp.ul.pt
A. M. Passos
Business Research Unit, Instituto Universitário de Lisboa (ISCTE-IUL),
Av. Das Forças Armadas, 1649-026 Lisbon, Portugal
e-mail: ana.passos@iscte.pt
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The RSLQ (Houghton and Neck 2002) has been developed to measure individual selfleadership. Individual self-leadership is a self-regulatory mechanism that drives individual
capacity and enhances self-regulatory processes through 3 correlated factors [behaviorfocused strategies (BFS), natural reward strategies (NRS), and thought pattern strategies
(TPS)], all comprising 9 self-regulatory dimensions.
Self-leadership is empirically known to foster individual positive mood sates, and
subjective well-being (i.e., job satisfaction, task satisfaction) (Neck and Manz 1996), and
also increases individual perception of self-efficacy (Prussia et al. 1998) in organizational
settings. These dimensions in turn are known to be important predictors of individuals’
quality of life (Cicognani et al. 2009; Michalos 2003).
1.2 Behavior Focused Strategies
BFS increase individual self-awareness regarding task performance and self-regulatory
mechanisms (Neck and Houghton 2006). Through BFS, individuals proactively reshape
their environment, adjust their behavior and maximize performance. BFS include: (a) Selfobservation (i.e., the process through which individuals monitor personal behavior and
decide if the behavioral repertoire being used is effective or should it be changed); (b) Selfgoal setting (i.e., the ongoing adjustment of professional and personal performance goals to
environmental cues); (c) Self-reward (i.e., the usage of personal rewards that prompt or
inhibit specific behaviors); and (d) Self-cueing (i.e., the usage of tools such as memos and
pictures to remember things that must be accomplished and happening after goals have
been achieved) (Neck and Houghton 2006). Still regarding BFS, self-regulatory activity
depends on knowledge being available as a feedback and working resource that allows
individuals to perform, which is a necessary requisite for individual effective performance
(Bandura 1991; Manz 1986). Neck and Manz (2010) have argued that effective self-leaders
proactively seek to obtain knowledge when they find they lack the necessary resources to
perform. This suggestion is partially supported by the empirical evidence that highly
conscious individuals are usually more aware of their needs and act proactively towards the
gaining of such resources (Dweck and Legget 1988), being consciousness very close to
individual self-leadership (Houghton et al. 2004). Therefore, we decided to include a selflearning dimension on the Portuguese scale. This decision is also partially supported by
empirical findings connecting learning oriented beliefs and the use of self-leading strategies on the prediction of work role innovation (Curral and Marques-Quinteiro 2009).
1.3 Natural Reward Strategies
NRS concern the search and promotion of pleasant events for those performing a task. These
strategies allow increasing the number of task-positive feelings through environmental and
task modeling, and reduce negative cues that inhibit task intrinsic motivation (i.e., the
exacerbation of positive issues and avoidance of those that are unpleasant through purposefully ignoring them, and the proactive transformation of the environment and the nature
of the task so that they become more satisfying to accomplish) (Neck and Manz 2010).
1.4 Thought Pattern Strategies
TPS help individuals developing task positive mental attitude (Neck and Manz 1997). They
include: (a) Evaluating beliefs and assumptions (i.e., the way individuals analyze their
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values and beliefs in a given momentum and decide either to maintain them or to adjust
them to what is expected to be the most effective strategies and thoughts); (c) Self-dialog
(i.e., when individuals engage in self dialogue as a way to improve thinking processes and
rationales); and (d) Visualizing successful performance (i.e., the way individuals engage in
positive thinking towards task, personal experience and reality and how they mentally
simulate action plans and tasks to be performed) (Neck et al. 1999).
1.5 The Revised Self-leadership Questionnaire: State of the Art
Alves et al. (2006) addressed the growing concern on cultural aspects that differentiate
self-leadership across the nations. Based on Hofstede’s work, the authors argued that as
different cultures value different attributes and practices, self-leadership strategies should
also be differently used. Besides, they emphasized that culture-specific self-leadership
dimensions should emerge (Alves et al. 2006). However, only three empirical studies have
directly addressed this issue. Neubert and Wu (2006) tested the Revised Self-leadership
Questionnaire (Houghton and Neck 2002) in a Chinese sample, and found that while
setting a goal, visualizing successful performance (which cross-loaded other dimensions),
self-talk and self-reward allowed for a good fitting model, while natural rewards and the
remaining dimensions of self-leadership strategies did not. Another finding was that of
Georgianna (2007). Before and after a 2-week training session on self-leadership, a sample
of American and Chinese students was matched. In spite of the training program, selfleadership levels did not increase in both the groups, with the American students scoring
higher on general self-leadership. The Chinese students, apart from their lower score on
BFS, reported higher sense of awareness, when compared with the American students
(Georgianna 2007). According to the author, this could be explained due to cultural differences, mainly dose traits concerning collectivism and group/peer harmony. Another
explanation for this phenomenon, which has not been considered by the author might be
based on the findings by Stewart et al. (1996), concerning the impact of consciousness
levels on the effective acquisition of self-leadership competences. The third study that has
been conducted addressed the re-extension of the Revised Self-leadership Questionnaire to
the Chinese context (Ho and Nesbit 2009), following the work by Neubert and Wu (2006).
Considering the cultural cluster, the authors not only refined some of the already-existing
items, such as natural rewards and evaluating beliefs and assumptions (increasing reliability and strength), but also created three new dimensions that considered socio-relational
issues from collectivist cultures: relation-based natural rewards, social-oriented evaluation
of beliefs, and assumptions- and relation-based self observation (Ho and Nesbit 2009).
1.6 Locus of Control, Thinking Patterns and Cognitive Flexibility
Locus of control is defined in the literature as being a personal self-regulatory trait that
describes how people tend to attribute the causes of specific events on their personal life
and activity either as being due to intrinsic causes (i.e., personal characteristics) or
extrinsic causes (i.e., poor social climate) (Bandura 1991; Hubley and Wagner 2002; Rotter
1966). Although they are not as restrictive as regulatory focus traits, self-leading strategies
also help individuals to scan their environment, reflect upon the situation and make sense
out of them (Neck and Houghton 2006). As they are functionally similar, it is expected that
locus of control and self-leadership positively correlate with each other.
As previously mentioned, self-leadership strategies are known to empirically promote
opportunistic thinking and to reduce negative thinking (Neck and Manz 1997). Therefore,
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it is expected that self-leadership will positively correlate with the opportunistic dimension
of Rotter (1966) locus of control scale and will negatively correlate with the threat
dimension of the scale.
Finally, individual cognitive flexibility comprises the individual capacity to successfully
challenge and replace maladaptative thoughts with more balanced and adaptive thinking
(Dennis and Wal 2010), which has also been empirically shown to be a consequence of
using self-leadership strategies as a way to regulate one’s thoughts and motivations (Neck
and Manz 1997; Neck and Houghton 2006). Therefore, it is also expected that selfleadership positively correlates with cognitive flexibility.
2 Method
2.1 Participants
720 individuals were randomly assigned to an online survey through a link attached to an
email invitation. Data collection went from May 2010 to October 2010. 68.6% of the
participants were females and the average age was 28.3 years (SD = 11.456). 54.86%
were professional workers from distinct sectors such as research, teaching, business consulting and industries and 44.44% were university and post-graduate students from different courses (i.e., psychology, management, finance, marketing).
2.2 Measures
2.2.1 Self-Leadership
Self-leadership was accessed with a translated version (34 items) of the RSLQ (Houghton
and Neck 2002). A 6 point-scale ranging from strongly disagree (1) to strongly agree (6) was
used. To measure self-learning strategies tree new items were added: ‘‘During task performance, when I find I lack any necessary skills, I try to find a way to obtain them so I can
succeed,’’ ‘‘Before beginning a task, I prepare myself looking for information that I believe I
may need,’’ and ‘‘Before I start performing a task, I try to improve my knowledge so I can
perform better.’’ All nine factors were arranged in accordance with the second-order selfleadership dimension concerning BFS (self-goal setting, self-reward, self-observation,
self-cueing, and self-learning), NRS (focusing thoughts on natural rewards), and TPS
(visualizing successful performance, self-talk, and evaluating beliefs and assumptions).
2.2.2 Cognitive Flexibility
Cognitive flexibility was measured using Dennis and Wal (2010) Cognitive Flexibility
Inventory (a = 0.75). Respondents gave their answers on a 6 point-scale ranging from
strongly disagree (1) to strongly agree (6). An item example is ‘‘I try to think about things
from another person’s point of view.
2.2.3 Locus of Control
Locus of control was measured using a previously adapted version from Rotter’s (1966)
locus of control scale measuring internal locus (a = 0.76), external locus (a = 0.84) and
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environment scanning dimensions (threats vs. opportunities). Respondents gave their
answers on a 6 point-scale ranging from strongly disagree (1) to strongly agree (6). An item
example is ‘‘I usually think about the success I intend to achieve in the future’’.
2.3 Procedures
The first step was to translate all the scales to Portuguese language. To do so, we followed
Brislin’s (1980) translation/back-translation procedure to create a Portuguese version of the
scales. Items were translated to Portuguese by the first author and were then submitted to peers
that were fluent in both Portuguese and English. Blind peer back-translation was done to
check item’s consistency and both second and third authors validated the translation process.
The following step comprised dividing the sample into a calibration sample (all students, N = 325) and a validation sample (all professionals, N = 395) to allow for confirmatory factorial analysis (CFA) (Byrne 2010). We also conducted several multi group
confirmatory factor analyses (MCFA) for cross-validation (French and Finch 2008),
comparing different samples paired in two groups by gradually constraining several
parameters to be equal.
To evaluate the model fit through estimating the variance’s real value, the chi-square
index (v2) was considered. As it required not to be significant to express the model fit
(p [ 0.05) (an almost unrealistic rule) (Hu and Bentler 1999), four other indices were used
(Crocetti et al. 2010; DiStefano 2002; Hu and Bentler 1999; Schmitt and Branscombe
2002; Streicher et al. 2008): the ratio value between the v2 value and degrees of freedom
(v2/df), a more secure indicator of the model-fit quality (good fit is suggested when the
index has a value between 1 and 3); the root mean square approximated error (RMSEA),
which measures the discrepancy between the hypothesized model and data by degrees of
freedom (it has to be\0.06 to suggest goodness of fit); the comparative fit index (CFI) that
carries out the comparison between the fit of the hypothesized model and that of a basic
model being represented by a null model (it must range between 0.95 and 1); and the
standardized root mean square of residual (SRMR). Besides the above-mentioned goodness
of fit index, the D v2 difference test (D v2) was carried out to verify for non-invariance
across the models (Byrne 2010).
3 Results
We began the validation process by conducting an exploratory data analysis on the calibration sample (N = 325) using the principal components method, eigenvalue [1 and
varimax rotation, with SPSS 18 (see Table 1).
In average, participants were 22.36 years old (SD = 5.60) and 74.4% were women. The
first factorial analysis showed all nine factors, explaining 68.46% of the total variance.
However, several items revealed two problems: low loadings (\0.5) and saturation in more
than one factor. The excluded items were: Item 2: ‘‘I consciously have goals in mind for
my work efforts’’; Item 5: ‘‘I think about my own beliefs and assumptions whenever I
encounter a difficult situation’’; Item 1: ‘‘I use my imagination to picture myself performing well on important tasks’’; Item 33: ‘‘I often rehearse the way I plan to deal with a
challenge before I actually deal with a challenge’’; Item 8: ‘‘I focus my thinking on the
pleasant rather than on the unpleasant aspects of my professional activity’’’ and Item 17: ‘‘I
try to surround myself with the objects and the people that bring out my desirable
behaviours.’’ The sub dimension of ‘‘self-observation’’ was also discarded given all items
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Table 1 Factorial loadings
1
2
3
4
5
6
Factor 1. Visualizing successful performance, a = .87
(.85)
10. I visualize myself successfully performing
a task before I do it
0.86
19. Sometimes I picture in my mind a successful
performance before I actually do a task
0.87
27. I purposefully visualize myself
overcoming the challenges I face
0.77
Factor 2. Self-goal setting, a = .78 (.84)
2. I establish specific goals for my own performance
0.72
11. I consciously have goals in mind for my work
efforts
0.71
20. I work toward specific goals I have set for myself
0.72
Factor 3. Self-talk, a = .93 (.92)
3. Sometimes I find I’m talking to myself
(out loud or in my head) to help me
deal with difficult problems I face
0.90
12. Sometimes I talk to myself (out loud
or in my head) to work through
difficult situations
0.88
21. When I’m in difficult situations I will
sometimes talk to myself (out loud
or in my head) to help me get through it
0.87
Factor 4. Self-reward, a = .92 (.93)
4. When I do an assignment especially well,
I like to treat myself to something
or activity I especially enjoy
0.90
13. When I do something well, I reward myself
with a special event such as a good dinner,
movie, shopping trip, etc.
0.89
22. When I have successfully completed a task,
I often reward myself with something I like
0.91
Factor 5. Evaluating beliefs and assumptions, a = .65
(.78)
5. I think about my own beliefs and assumptions
whenever I encounter a difficult situation
0.79
14. I try to mentally evaluate the accuracy
of my own beliefs about situations
I am having problems with
0.75
23. I openly articulate and evaluate my own
assumptions when I have a disagreement
with someone else
0.45
Factor 6. Self-learning strategies, a = .80
6. During task performance, when I find
I lack any necessary skills, I try to find a
way to obtain them so I can succeed
0.72
15. Before beginning a task, I prepare myself
looking for information that I believe
I may need
0.77
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Table 1 continued
1
2
3
4
5
6
7
8
0.77
24. Before I start performing a task, I try
to improve my knowledge
so I can perform better
Factor 7. Focusing on Natural Rewards, a = .80 (.74)
26. When I have a choice, I try to do my
work in ways that I enjoy rather than
just trying to get it over with
0.79
32. I seek out activities in my work that
I enjoy doing
0.75
35. I find my own favorite ways to get things done
0.67
saturated in more than one factor. In the end, only 8 sub dimensions were kept (23 items),
explaining 77.77% of the total variance. The full scale (23 items) showed a 0.87 alpha
score. With regard to the three main strategies, namely, BFS (11 items), NRS (3 items),
and TPS (9 items), the scores were 0.79, 0.80, and 0.83, respectively. The factorial
loadings for each factor and sub-dimensions from the original RSLQ (Houghton and Neck
2002) and the adapted version are presented in Table 1.
Following the procedures developed by Bobbio and Rattazzi (2006), and relying on
Byrne’s (2010) suggestions, the first step was to use structural equations modeling (SEM)
to test the second-order model in which only the tree general factors (BFS, NRS, and TPS)
were allowed to correlate (see Table 3). When checking the model fit error’s 23 variance
proved to be negative (-0.512), making the solution inadmissible (Jöreskog and Sörbom
1984). Therefore, item 18 was removed (‘‘I use specific reminders to keep me focused in
things a need to accomplish’’). To avoid poor data relations and inconclusive results item 9
was also excluded, which excluded the sub dimension of ‘‘self-cuing’’ (‘‘I take notes so I
can remember what I have to do’’) (Zuckerman et al. 1993). The three-correlated-factor
model with 21 items and 7 sub dimensions revealed a good fit, [v2 (180) = 372.4,
p & 0.000, RMSEA = 0.06, CFI = 0.95 (v2/df = 2.069), and SRMR = 0.07 (see
Table 3)]. Cronbach’s alphas for each dimension were 0.78 (BFS, 9 items), 0.80 (NRS, 3
items), and 0.83 (TPS, 9 items) (Table 2).
Following this analysis, we then tested tree other arrangements comprising first-order
models (one factor, three factors, and seven factors) in which only perfect simple structures
have been used. All the three first-order models showed lack of goodness of fit, thus
supporting Houghton and Neck’s (2002) previous findings regarding a higher level model
for self-leadership.
Table 2 Means, standard deviations and inter-correlations
M
SD
1
2
3
4
Self-leadership
4.54
0.56
1
0.48**
–
–
Cognitive flexibilitya
4.08
0.45
0.48**
1
–
–
Locus of control 1a
5.38
0.63
0.60**
–
1
–
Locus of control 2b
3.38
1.60
-.31**
–
-.31**
1
** p [ 0.01
a
N = 570, p [ 0.01
b
N = 150, p [ 0.01
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Table 3 Confirmatory factor analysis (21 items)
v2
df
p
v2/df
CFI
RMSEA
SRMR
1 factor
2,544.7
189
0
13.464
0.36
0.196
0.14
3 factor
1,919.5
189
0
10.156
0.53
0.168
0.20
7 factor
715.2
189
0
3.784
0.87
0.093
0.19
3 correlated
factorsa
372.4
180
0
2.069
0.95
0.057
0.07
3 correlated
factorsb
376.1
180
0
2.09
0.96
0.053
0.07
1st order model
2nd order model
a
N = 325 students (calibration sample)
b
N = 395 professionals (validation sample)
Table 4 Cross-validation: multi-sample 1
v2
df
p
v2/df
SRMR
CFI
RMSEA
D v2, p [ 0.05
Model 1
753.2
364
0
2.069
0.066
0.952
0.039
Model 2
767.1
374
0
2.051
0.0679
0.952
0.038
M2 - M1
D v2(10) = 13.9
p [ 0.05
Model 3
770.9
378
0
2.04
0.0675
0.952
0.038
M3 - M2
D v2(4) = 3.8
p [ 0.05
Model 4
779.8
384
0
2.031
0.0667
0.952
0.038
M4 - M3
D v2(6) = 8.9
p [ 0.05
Full sample [Calibration sample (N = 325) and Validation sample (N = 395)]. Model 1 Invariance of
Factor Structure; Model 2 Invariance of Factor Loadings; Model 3 Invariance of Factor Loadings and Factor
Structure; Model 4 Invariance of Factor Loadings, Factor Structure and Factor Structure Variance and
Covariance
After identifying the fittest model for the calibration sample, the second step was to
conduct a confirmatory analysis by verifying the goodness of fit for the validation sample
(N = 395) (see Table 3). 37% of the participants were man. The mean age was 34.19 years
(SD = 12.38), and 61.3% had an at least one academic degree. Once again, only the
tree general factors were allowed to correlate. Results obtained suggested a good fit
[v2 (180) = 376.1, p & 0.000, RMSEA = 0.05, CFI = 0.96 (v2/df = 2.09), and
SRMR = 0.07]. Cronbach’s alphas for each dimension were 0.78 (BFS, 9 items), 0.82
(NRS, 3 items), and 0.83 (TPS, 9 items).
To verify if the factorial structure replicated across independent samples drawn from the
same population (Byrne 2010) both calibration and validation samples were compared.
Cross-validation comparisons were done by cumulatively constraining the model regarding
factorial loadings (Model 1), factorial structure (Model 2); factorial loadings and factor
structure (Model 3); and factorial loadings, factorial structure, and factor structure variance
and covariance (Model 4). Results were totally satisfactory as the model fit proved to be
invariant across both populations (see Table 4).
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After this first cross-validation multisample analysis, two other analyses were carried
out.
For the second cross-validation analysis, respondents were divided into two groups
based on their work experience [\6 years (N = 364) and[5 years (N = 206)]. In average,
participants with less than 6 years of professional experience were 24.35 years old
(SD = 5.03) and 41% were students. 44% had at least one academic degree and 68.1%
were women. Regarding participants with more than 5 years of professional experience,
36.3% were man, participants had in average 41.37 years old (SD = 12.80), and
87.9% were professional workers. Goodness of fit index for each group was satisfactory
[v2 (180) = 394.7, p & 0.000, RMSEA = 0.06, CFI = 0.95 (v2/df = 2.14), and
SRMR = 0.08; v2 (180) = 330.8, p & 0.000, RMSEA = 0.06, CFI = 0.94 (v2/df =
1.84), and SRMR = 0.07] (see Table 5).
The third cross-validation considered two randomized groups (N1 = 300, N2 = 300)
from the full sample (N = 720). Group one (N = 300) had 68.3% of women and in
average participants were 22.9 years old (SD = 3.37) and 51% were professional workers.
Group two (N = 300) had 68% women and in average participants were 33.12 years old
(SD = 15.39) and 45% were professional workers. Goodness of fit index for each group
proved to be satisfactory [v2 (180) = 334.6, p & 0.000, RMSEA = 0.05, CFI = 0.96 (v2/
df = 1.859), and SRMR = 0.07; v2 (180) = 381.5, p & 0.000, RMSEA = 0.06,
CFI = 0.94 (v2/df = 2.119), and SRMR = 0.07]. Model invariance was once again
achieved for the two randomized groups (see Table 6).
Finally, we also correlated the adapted version of the revised self-leadership questionnaire with cognitive flexibility and locus of control (see Table 2). Regarding cognitive
flexibility (N = 570), a correlation of 0.48** (p [ 0.01) was found, thus being in accordance with what was to be expected. As for the locus of control scale, the same expected
correlations were obtained. While self-leadership positively correlated with the locus of
control dimension (r = 0.60**, p [ 0.01), the correlation with the event scanning
dimension (opportunity vs. threat) was negative (r = -0.31**, p [ 0.01).
Table 5 Cross-validation: multi-sample 2
v2
df
p
v2/df
SRMR
CFI
RMSEA
D v2, p [ 0.05
Model 1
728.7
364
0
2.002
0.0746
0.943
0.042
Model 2
734.5
374
0
1.964
0.0757
0.944
0.041
M2 - M1
D v2(4) = 5.8
p [ 0.05
Model 3
736.5
378
0
1.948
0.0748
0.944
0.041
M3 - M2
D v2(4) = 2
p [ 0.05
Model 4
741.6
384
0
1.931
0.0745
0.945
0.04
M4 - M3
D v2(6) = 5.1
p [ 0.05
Experience group [Less than 6 years of experience (N = 364) and more than 5 years of experience
(N = 206)]. Model 1 Invariance of Factor Structure; Model 2 Invariance of Factor Loadings; Model 3
Invariance of Factor Loadings and Factor Structure; Model 4 Invariance of Factor Loadings, Factor
Structure and Factor Structure Variance and Covariance
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Table 6 Cross-validation: multi-sample 3
v2
df
p
v2/df
SRMR
CFI
RMSEA
D v2, p [ 0.05
Model 1
716.1
360
0
1.989
0.0702
0.948
0.041
Model 2
720.7
364
0
1.98
0.0689
0.947
0.04
M2 - M1
D v2(4) = 4.6
p [ 0.05
Model 3
736.2
374
0
1.968
0.0709
0.947
0.04
M3 - M2
D v2(10) = 15.5
p [ 0.05
Model 4
739.8
378
0
1.957
0.0756
0.947
0.04
M4 - M3
D v2(4) = 3.6
p [ 0.05
Randomized group (N1 = 300, N2 = 300). Model 1 Invariance of Factor Structure; Model 2 Invariance of
Factor Loadings; Model 3 Invariance of Factor Loadings and Factor Structure; Model 4 Invariance of Factor
Loadings, Factor Structure and Factor Structure Variance and Covariance
4 General Discussion and Conclusion
Results for model fit were quite satisfactory. Correlations with both cognitive flexibility
and locus of control scales also met expectations, as self-leadership is known to increase
self-efficacy, subjective well being and opportunistic thought (Neck and Houghton 2006).
This study represents a first step towards the development of an individual self-leadership measure in the Portuguese context and for that several recommendations for future
research are presented. Factorial analysis has suggested that several items should be
reviewed, as factorial structure showed that several of them simultaneously had low scores
in more than one factor. This was the case of self-observation. For instance, the GLOBE
project results for the Latin Cluster have shown that the Portuguese culture scored high in
power distance, indicating that individuals and teams strongly rely on the leader to make
decisions and coordinate work effort (Jesuı́no 2002). This in turn ‘‘relieves’’ individuals to
be aware of the task situation, what makes self -monitoring activity unnecessary.
A major contribution of this study is the inclusion of the self-learning strategies. When
knowledge in not immediately available, effective self-leaders will try to obtain the
missing knowledge and then use it for task resolution (Neck and Houghton 2006).
As suggested in the initial paragraphs of this work, academic and professionals may
benefit from this work as it provides an adapted tool to access self-leadership competences
in organizational settings. This in turn will be very helpful in the development of research
towards the understanding of quality of life and work life balance at the work place, and
creation of more adequate training and recruiting programs in organizational settings.
Acknowledgments We thank to the colleagues from the Doctoral Program of Human Resources Management and NIPO research group at ISCTE—Lisbon University Institute for their helpful comments on an
earlier version of this work.
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