This document describes a study that aimed to test the psychometric properties of the Romance of Leadership Scale (RLS) and investigate factors influencing faculty followership's perception of institutional leaders in Malaysian institutions of higher learning. An online survey using the 32-item RLS was distributed to faculty members at 16 public universities. The study hypothesized that the RLS would demonstrate valid and reliable three-factor structure in the Malaysian context. Demographic information and RLS responses were collected from 434 faculty members. Preliminary analyses of the data were conducted to examine the sample characteristics and properties of the RLS.

1. 1
Empirical Investigation of Factors Influencing Faculty Followership’s
Perception of Institutional Leader in Malaysian Institutions of Higher Learning
Hairuddin Bin Mohd Ali
Institute of Education
International Islamic University Malaysia
Gombak 50728, kuala Lumpur, Malaysia
E-mail address: hairuddin@iab.edu.my
and
Mohammed Borhandden Musah
Institute of Education
International Islamic University Malaysia
Gombak 50728, kuala Lumpur, Malaysia
E-mail address:mbmusaus@yahoo.com
Abstract
This study aims at testing psychometric properties of the 32-item of Romance of
Leadership Scale (RLS). The study is equally aims at investigating factors
influencing faculty followership’s perception of institutional leader in selected
Malaysian institutions of higher learning. Online survey questionnaires were
conveniently sent to 2560 faculties of 16 public universities sampled, using
guidelines of 95% confidence interval and margin of error at ± 02% to collect the
data. A confirmatory factor analysis was performed to test factorial validity and
psychometric properties of the three constructs underlie the faculty followership’s
perception of institutional leader. It is anticipated that findings of the study will
provide leaders of the institutions of higher learning with proper understanding of
factors influencing faculty followership’s perceptions of institutional leaders, which is
critical to organizational performance and faculty retention. Furthermore, it is
expected that the findings will contribute to the initiatives of empirical studies on
followership especially in higher education sector. The study as being the first of its
kind in Malaysian context to empirically evaluate psychometric properties of the
scales underlie faculty followership’s perception of institutional leader and investigate
factors influencing faculty followership’s perception of institutional leader using well
experienced participants rather than inexperience participants of the previous studies
which sampled university students, high school and company employees would be a
remarkable contribution to the followership literature.
Keywords: faculty, romance of leadership scale, followership, Malaysia,
psychometric properties, CFA, institutions of higher learning.
INTRODUCTION
Organisational research has witnessed predominantly studies on leadership in
isolation of followership. This narrow focus has been overturned when Kelley (1992)
discloses the missing element in organisational research. This is due to the fact that
according to social constructive approach leadership is defined as an experience
undergone by followers (Meindl, 1993:97). In other words, individuals are actively
involved in constructing leadership rather than leadership being simply what a leader
does.

2. 2
Practically, followers represent if not all, about 99% of the institutional human
capital. It is empirically proven that while leaders in most of the organisations
contribute to organisational success not more than 20 percent on average, followers
are instrumental to the remaining 80 percent (Kelley, 1992). Unfortunately, for
decades, organisational literature has been tightly concentrated on investigating
leadership theory, traits, styles, leader’s viewpoint and related issues rather than
taking into equal significant scale, the follower perspective (Kelley, 1992; Vartanian,
Seeley and Trouyet, 2003; Rusher, 2005; Kelley, 2008; Vrba, 2008). Even in a
situation where research does investigate the concept of followers as a subject
matter, the main purpose is to understand leadership better (Kelley, 2008). In other
words, followership is traditionally ignored in the early organisational literature, while
the two components are inseparable. Two independent components (Kelley, 1992)
but intertwine with common focus “followers and leaders both orbit around the
purpose; followers do not orbit around the leader” (Chaleff, 2009:13). This study
focuses on evaluating psychometric properties of the romance of leadership scales
(RLS) in the context of Malaysian culture. It is also investigates factors influence
faculty followership’s perception of institutional leader in selected Malaysian
institutions of higher learning.
LITERATURE REVIEW
The Development of Romance of Leadership Scale
This particular RLS was established by Meindl and Ehrlich (1988) with the aim of
predicting leadership influence from the followers’ perceptions and experiences.
More specifically, RLS defines leadership as the fulfilment of followers’ preconceived
ideas as to the qualities and behaviours a leader should exhibit (Eden and Leviatan,
1975). Instruments of RLC were meant to measure the degree to which an individual
holds a romantic view of leadership. Recently, RLC is widely used in organisational
literature after the wakeup turn from heavily dependency on leadership in the
organisational literature. Since the establishment of RLC, researchers have been
using the instrument to control for follower bias when studying follower attributions.
Furthermore, the validity and reliability of RLC is also cross-culturally tested and
evaluated across a dichotomous cultures. An experimental study which sampled
304 undergraduate students found two factor structures and one factor contained
two items (Awamleh and Gardner, 1999). Similar findings (three factor-structure)
were reached in Chong and Wolf’s (2009) study which sampled 452 management
students in New Zealand. In addition, Cho and Meindl (n.d.) found a three factor
structure of the same version of the instruments.
However, the findings of a study which used RLS on election in the state of
California found a two-factor solution (with only 11 items) for the RLS, with the
reverse coded items loading on a separate factor (Bligh, Kohles and Pillai, 2005).
Given the forgoing findings, it would be imperative to test the factor structure
of the RLS in the context of Malaysian culture within the settings of institutions of
higher learning. As such, this study tests the following hypotheses:

3. 3
Hypothesis 1:Components of RLS are best represented by three-factor
model in the context of Malaysian institutions of higher
learning.
Hypothesis 2: Factors of RLS are valid and reliable.
Followers’ Perceptions of Organisational Leader
Back to four decades or so, organisational literature has started to pay considerable
attention to the vital role the followers play in the organisational performance. This
approach has led to the emergence of studies of followership styles, factors
influencing followership styles, followers’ perceptions of organisational leaders,
followers work experience, followers traits and values and so on to mention a few
(Kelley, 1992; Vartanian, Seeley and Trouyet, 2003; Rusher, 2005; Kelley, Chaleff
and Blumen, 2008; Vrba, 2008; Chaleff, 2009).
This study investigates followers’ perceptions with particular reference to
organisational leader. It is commonly known that followers judge organisational
leaders based on performance reports rather than leaders’ potentials. This is often
take place within the context of discussion between followers about their
organisational achievements (Calder, 1977). Empirical studies suggest existence of
differences in terms of followers’ personal values and work experience associated
with a relationship with a leader in a given context, which is deemed to have an
impact on followers’ work performance and behaviour (Ehrhart and Klein, 2001;
Miller, Butler and Cosentino, 2004; Bjugstad, 2006). Chong et al. (2009) however
argued that work performance and behaviour are outcomes. Given a stand as such,
researchers suggested constructive values, work experience and age formed
followers’ behaviours in relation to their leaders (Chong et al., 2009; Ehrhart et al.,
2001). This study however tests only work experience and age for whether or not
these variables influence followers’ perception with the organisational leader
interchangeability. As such, the study posed the following hypothesises:
Hypothesis 3: Older followers perceive greater influence of organisational
leader compared to the perception of younger followers of the
universities sampled.
Hypothesis 4: Experienced followers perceive organisational leader as
having less influence as compared with the perception of less
experienced followers in the context of the universities
sampled.
Method
Instrumentation
The process of investigating followers’ perception of organisation leader in the
contect of institutions of higher learning used an online survey questionnaire, which
sampled 16 universities across Malaysia. Apart from demographical information of
the respondents, the questionnaire comprised 32 items of RLS (Meindl and Ehrlich,
1988; Chong et al., 2009). The study adapted the instruments and slight
modifications were made to suit the context of this study especially to the Item 27,

4. 4
which in the original version states “the president of US can do very little to shape
the course of our country” and was modified to “the Prime Minister of Malaysia can
do very little to shape the course of our country”.
The pool of the questionnaire of the 32 items is built-up of close-ended
statements to determine the phenomenon that the researchers aimed to investigate.
Furthermore, it comprised two major sections. Section one, formed 7 items, which
requested respondents to indicate general demographic information. Thus, the
items asked and collected characteristic information such as gender, age group, race
group, marital status, work experience, monthly income and university affiliated to.
Section two, comprised 32 items of RLS used in the study. A 7-point Likert type
scale was employed to collect the data. The scale is interpreted as: 1=very strongly
agree, 2=strongly agree, 3=agree, 4=neither agree nor disagree, 5=disagree,
6=strongly disagree and 7=very strongly agree. Since items of RLS contained 15
negatively worded statements, reverse coding procedure was performed. During the
reverse coding, a value of 7 =very strongly disagree, 6 =strongly disagree, 5
=disagree, 4 =neither disagree nor agree, 3 = agree 2=strongly agree and 1 =very
strongly agree were selected. This recoding was used instead of 1 =very strongly
agree, 2 =strongly agree, 3 =agree, 4 =neither agree nor disagree, 5 =disagree,
6=strongly disagree and 7 =very strongly disagree. This reverse coding procedure
covered all 15 negative statements of RLS scale.
Participants
A total of 16 institutions of higher learning were sampled to represent the population
of this study. 150 respondents were drawn from each to represent population of
academic staff in their respective universities. Participants were drawn from
universities such as International Islamic University Malaysia (IIUM), University
Malaya (UM), University Putra Malaysia (UPM), University Kebangsaan Malaysia
(UKM), Universiti Teknologi Malaysia (UTM), Universiti Utara Malaysia (UUM),
Universiti Sains Malaysia (USM), Univerisiti Teknologi Mara (UiTM), Universiti
Pendidkan Sultan Idris (UPSI), Universiti Malaysia Sabah(UMS), Universiti Tun
Hussein Onn Malaysia (UTHOM), Universiti Pertahanan Nasional Malaysia (UPNM),
Universiti Teknikal Malaysia Melaka (UTMM) and Universiti Malaysia Sarawak
(UNIMAS) academic staff. The sample size (2400) was determined through the
confidence interval of 95% and margin of error at ± 3.5% as suggested by
(Ferguson, 1981; Vockell and Asher, 1998). Respondents were randomly selected
and voluntarily participated through online in answering the survey questionnaires.
Surveys questionnaires were e-mailed to the respondents through their staff
directory e-mail addresses. They were asked to express their level of agreement or
disagreement to the survey instruments. Of the 2400 questionnaires e-mailed, 436
questionnaires were successfully returned. However, two incomplete questionnaires
were discarded, which resulted into a total of 434 questionnaires being retained and
analysed. This indicated a ratio of (18.08%) of response rate which is acceptable in
online survey.
Data Analysis
Data screening processes
Since instruments RLS were adopted and used in different cultural context, two
statistical analyses were performed. First, principal component analysis (PCA) using
SPSS version 17.0 software to test factors underlie the constructs understudy. This

5. 5
was then followed by application of structural equation modelling (SEM) through
AMOS version 18.0 software, using confirmatory factor analysis (CFA) to test
confirm constructs (Arbuckle, 2008). Finally, assessment of construct validity through
average variance extracted (AVE) and construct reliability through composite
reliability index (CRI) were evaluated.
Results of the Study
Respondents’ demographic information
The accuracy of the dataset is often assessed through code and value cleaning
using descriptive frequencies. The results of the frequencies indicated that (n
=168, 38.5%) of the respondents were male academic staff, while (n =266, 61.3%)
were female academic staff.
With reference to respondents’ age group, the analysis showed that the
majority of respondents were as young academic staff (age between 29-39 years) n
=296, 68.2 %). In addition, respondents who were between 40-69 years who
identified as old academic staff were small in terms of academic staff
participation (n = 138, 31.8%).
The analysis demonstrated that the majority (n= 366, 84.3%) of the
respondents, who participated in the study were identified as experienced academic
staff (6-10 years teaching experience). Followed by participants, who identified
themselves as inexperience academic staff (1-5 years) n =68, 15.7%).
Pertaining to academic staff monthly income, the results of Table 1 showed
that the majority (n=227, 52.3%) of academic staff surveyed were earning a monthly
income above RM 6000. Table 1 also identified n=67, 15.4% of the respondents as
academic staff who earn a monthly income ranged between RM 3001 – RM 4000.
Furthermore, n=56, 12.9% of the respondents were identified as academic staff who
earn RM 4001 – 5000 and RM 5001 respectively. In addition, the least category of
respondents according to the scale of respondents’ monthly income, was the
category of academic staff who earn between RM 2000-3000 (n=28, 6.5%).
Regarding respondents’ race group, the analysis showed that the majority of
the participants (n = 363, 83.6 %) were Malay academic staff. Followed by foreign
academic staff (n =32, 7.4 %). Chinese academic staff (n =21, 4.8%) participated in
the study. In addition only (n=18, 4.1%) Indian academic staff have participated in
this study. Regarding respondents’ marital status, the data yielded that majority (n =
364, 83.9 %) of academic staff surveyed were married. This was followed by
academic staff who were classified themselves as unmarried (n =51, 11.8%). Some
(n =10, 2.3 %) of the participants were classified as themselves as divorcees and
only (n =9, 2.1%) of them were identified as widowed.
Finally, responses of academic staff surveyed indicate that the majority (n=60,
13.8%) of the respondents participated in this study were from UiTM. Table 1
identifies n=47, 10.8% the percentages of academic staff participated in the study
from IIUM. Furthermore, n=41, 9.4% was accounted for UKM academic staff
participated in the study. Similarly, n=33, 7.6% of the respondents were accounted
for academic staff from UPM. Interestingly, moderate percentages (n=31, 7.1%) and
(n=31, 7.1%) of respondent participations were captured from both UTMM and
UMSR academic staff population respectively. Moreover, the table revealed that
(n=30, 6.9%) of the respondents participated in the study were academic staff of
UPSI. The table also identified that (n=26, 6.0) of respondents were from USIM. In
addition, both (n=22, 5.1%) and (22, 5.1%) were identified as academic staff from

6. 6
UPNM and UTM respectively. Nonetheless, (n=20, 4.6%) of academic staff affiliated
themselves to USM. The analysis further presented both (n=18, 4.1%) and (n=18,
4.1%) as academic staff from UTHOM and UM. Also, (n=12, 2.8) were identified as
academic staff from UMS who responded to the survey. Finally, academic staff of
UMK were classified as the least on the participation scale (n=11, 2.5%). Table 1
depicts the detailed results.
Table 4 Frequency and Percentages of Respondents' Demographic Variables
Variable Frequency (N) %
Gender
Male 168 38.5
Female 266 61.3
Total 434 100
Age Group
Young 296 68.2
Old 138 31.8
Total 434 100
Race Group
Malay 363 83.6
Chinese 21 4.8
Indian 18 4.1
Others 32 7.4
Total 434 100
Marital Status
Unmarried 51 11.8
Married 364 83.9
Divorcee 10 2.3
Widowed 9 2.1
Widowed 7 1.2
Total 434 100
Working Experience
Less experienced 68 15.7
experienced 366 84.3
Total 434 100
Monthly Income
RM 2000-3000 28 6.5
RM 3001-4000 67 15.4
RM 4001-5000 56 12.9
RM 5001-6000 56 12.9
RM above 6001 227 52.3
Total 434 100
University
UM 18 4.1
UKM 41 9.4
UPM 33 7.6
UiTM 60 13.8
UUM 12 2.8
USM 20 4.6
UTM 22 5.1
IIUM 47 10.8
UPSI 30 6.9
UMS 12 2.8
UTHOM 18 4.1
UMK 11 2.5
UPNM 22 5.1
UTMM 31 7.1
UMSR 31 7.1
Total 434 100

7. 7
Principal Components Analysis (PCA)
Prior to PCA, reliability analysis was performed, to assess internal consistency of the
instruments. The results of reliability analysis revealed overall Cronbach’s alpha of
.86. This indicated a substantial internal consistency between individual items, thus
the items have positive covariance meanwhile the alpha is very close to desired
cutoff. Furthermore, this finding yielded that there is a very high reliability level of
internal consistency of the items, which indicated that the instruments were suitable
therefore; its results would be reliable in association with internal consistency build
up.
A PCA was conducted for the constructs. The analyses involved an iterative
process to reach the final solution, whereby items that did not contribute significantly
and practically to the extracted factors were automatically discarded. Furthermore,
factors with eigenvalues of 1 or greater were considered as good factors, and
therefore retained. Given a rule of thumb as such, a number of factors were
extracted from the pool of items. The correlation matrix table yielded more than 2
correlations were greater than 0.30. The measures of sampling adequacy (MSA)
requirement of (0.50 or greater) were also satisfied. Thus, the anti-image correlation
ranged between .59 and .96. Furthermore, all communalities were greater than .50
(ranged between .53 to .76), which indicates fulfilment of this requirement.
Moreover, the analysis revealed 3 interpretable factors with eigenvalues
greater than one. The extracted factors accounted for 51.17% of variance explained
in the constructs analysed. Interestingly, the degree of inter-correlation among the
items also reached satisfied level. Bartlett’s test of Sphericity was statistically
significant χ2
(496) = 9020.179, ρ≤.001, KMO = .80. Table 2 displays results the
details.
Table 2 Interchangeability and Influence Scale; Factor Loading, Measures of Sampling
Adequacy and reliability.
Construct Eigenval
ues
%Varianc
e
Indicator Loadi
ng
MSA α
Interchangeabilit
y of Leader
8.27 25.84
20. When an organisation is performing poorly, the first
place one should look to is its leaders.
.87 .71
.87
21. The process by which leaders are selected is
extremely important.
.86 .68
19. It’s probably a good idea to find something out about
the quality of top level leaders before investing in an
organisation.
.86 .72
28. Leadership qualities are among the most highly
prized personal traits I can think of.
.77 .72
6. Sooner or later, unqualified leadership at the top will
show up in decreased organisational performance.
.76 .69
8. High-versus low quality leadership has a bigger impact
on a organisation than a favorable versus unfavorable
organisational environment.
.74 .61
5.The great amount of time and energy devoted to
choosing a leader is justified, because of the important
influence that person is likely to have.
.73 53
1. When it comes right down to it, the quality of
leadership is the single most important influence on the
functioning of an organization.
.71 .63
24. When the top leaders are good, the organisation does
well; when the top leaders are bad, the organisation does
poorly.
.70 52
4. Anybody who occupies the top level leadership
positions in an organisation has the power to make or
break the organisation.
.67 51

8. 8
9. It is impossible for an organisation to do well unless it
has high-quality leadership at the top.
.62 .70
17. The connection between leadership and overall
organisational performance is often a weak one.
.61 .53
14. With a truly excellent leader, there is almost nothing
that an organisation can’t accomplish.
.56 53
12. An organisation is only as successful or as
unsuccessful as its leaders.
.53 65
25. There’s nothing as critical to the “bottom line”
performance of an organisation as the quality of its top
level leaders.
.52 .66
Influence of a
leader 5.65 17.65
31. One leader is as good or as otherwise as the next. .75 .79
.82
27. The Prime Minister of the Malaysia can do very little
to shape the course of our country.
.74 .70
16. Top level leaders make life and death decisions about
their organisations.
.72 .73
11. Many times, it doesn’t matter who is making decisions
at the top, the fate of an organisational is not in the hands
of its leaders.
.71 .75
26. In many cases, candidates for a given leadership
position are pretty much interchangeable with one
another.
.66 .66
22. So what if the organisation is doing well; people who
occupy the top level leadership positions rarely deserve
their high salaries.
.65 .75
23. In comparison to external forces such as the
economy, government regulations, etc., an organisation’s
leaders can have only a small impact on its performance.
.63 .73
18. Many times, organisational leaders are nothing more
than figureheads like the King and Queen of England.
.61 .70
2. The majority of business failures and poor
organisational performances are due to factors that are
beyond the control of even the best leaders.
.60 .69
30. There are many factors influencing an organisation’s
performance that simply cannot be controlled by even the
best of leaders.
.58 .71
10. When faced with the same situation, even different
top-level leaders would end up making the same
decisions.
.54 .70
7. Luck has a lot to do with whether or not organisational
leaders are successful in making their organisations
successful.
.53 .65
13. You might as well toss a coin when trying to choose a
leader.
.52 .60
Parallel Analysis (PA)
A further critical analysis on how many factors are accurately extracted through the
application of PCA was performed. This analysis is critical especially when the
analysis involves composing multiple scale items into a single score. This is because
determination of the number factors to retain is the most crucial issue researchers
encounter when using PCA. One of the effective methods proposed in determining
the significance of principal components, is the parallel analysis (PA), which
consistently proven the accuracy of determining the threshold for significant
components, variable loadings and analytical statistics when decomposing a
correlation matrix. PA involves comparing the size of eigenvalues obtained from PCA
analysis with those obtained from a randomly generated data of the sample size
(Horn, 1965; Pallant, 2007; Franklin, Gibson, Robertson, Pohlmann and Fralish,
1995). The PA threshold denotes if eigenvalues of extracted factors through
application of PCA are greater than the corresponding eigenvalues of PA, the factors
are retained. However, if the otherwise is true, then the factors fail to fulfil the criteria
are rejected (Franklin et al., 2007; Horn, 1965; Pallant, 2007).

9. 9
A Monte Carlo PCA for PA software was used to perform PA analysis. As
such, the number of variables (in this case 32), the number of subjects (in this case
434) and the number of replications (specifically 100) were calculated. The analysis
revealed that the eigenvalues of the first two factors extracted through PCA were
greater than their corresponding eigenvalues of the PA, and were thus significant at
p = 0.05. Therefore, these factors were retained for further interpretation. However,
the remaining third factor was rejected, since its eigenvalues of PA was greater than
the initially eigenvalue extracted through PCA (Franklin et al., 2007; Horn, 1965;
Pallant, 2007). These results did not support Hypothesis 1 with the finding that
components of RLS are best represented by three-factor model in the context of
Malaysian institutions of higher learning Table 3 depicts the details.
Table 3 Comparison of PCA and PA Eigenvalues for Data from RLS Scale
Component
no.
Actual eigenvalue from
PCA
Criterion eigenvalue
from PA
Decision
1 8.25 1.54 Accept
2 5.65 1.47 Accept
3 1.40 1.41 Rejected
Construct Reliability and Validity of the Measures
A construct validity analysis was performed to assess psychometric properties of the
items indexed factors that constituted interchangeability of leader and influence of a
leader. Two rigorous tests (composite reliability index (CRI) and average variance
extracted (AVE)) were conducted to assess construct validity, convergent validity
and composite reliability of the instruments.
The first test conducted was a CRI, which indicates how well each structure
has been described by the observed variables was performed to establish more
accurate reliability values. This test was conducted for the fact that researchers
(Raykov, 1998; Zinbarg, Revelle, Yovel and Li, 2006; Gordon, 2008) cautioned
heavily dependency on Cronbach’s Alpha to proclaim reliability of a construct due to
its limitations. According to Ranganathan, Dhaliwal and Teo (2004), while the
traditional reliability measure of Cronbach’s Alpha assumes equal weight for the
items measuring the construct in which it is influenced by the number of items in the
construct, the CRI estimates rests on the actual readings to compute the factor
scores which is better indicator of internal consistency reliability. Among issues of
which researchers pinpointed in relying on coefficient alpha per se are (1) “it does
not make allowances for correlated error of measurements nor does it treat
indicators influenced by more than one latent variable” Bollen (1989: 221), (2) it may
over or underestimate reliability Raykov (1997, 1998) and (3) it is somehow regarded
as a lower bound estimate of reliability.
Furthermore, Fornell and Larcker (1981) argue that the CRI is a more
advanced criterion of estimation compared to coefficient alpha. As such, according to
(Raykov and Shrout, 2002), a method that would allow researchers to evaluate factor
indicator scores more accurately is needed.
Given the foregoing reasons, the CRI test was performed to rigorously
examine the reliability of factor loadings extracted through PCA applications. The
conventional cutoff value for CRI is 0.70 or greater (Fornell and Larcker, 1981;
Bagozzi and Burnkrant, 1985). According to Fornell and Larcker (1981) and Hair et

10. 10
al. (2010), the evidence of construct validity is established if the CRI of each factor is
0.70 or greater and AVE of each factor is 0.50 or greater.
The second test performed is AVE, which measures the amount of variance
that captured by the construct in association with the amount of variance due to the
measurement error (Fornell and Larcker, 1981). In other words, AVE represents the
percentage of the total variance of a measure represented or extracted by the
variance due to the construct, as opposed to being due to error. The AVE is a
method that evaluates convergent validity and discriminant validity of a given
construct (Fornell and Larcker, 1981; Bagozzi, 1981). It is according Fornell and
Larcker (1981), calculated as the square root of the average communality. As such,
it is proposed that a benchmark of equal to or greater than .50 should be attained in
order to establish evidence of construct and convergent validity of a given construct
(Fornell and Larcker, 1981; Bagozzi, 1981; Hair et al., 2010). It is worth noting that
only convergent and construct validity were calculated in this section.
Interchangeability of Leader and the Influence of a Leader factors
The AVE analysis of interchangeability and influence factors revealed an adequate
estimation of AVE for interchangeability 0.51 and Influence 0.50). The estimates
(0.51 and 0.50) had fulfilled the recommended value of AVE (Fornell and Larcker,
1981). This finding demonstrates evidence of convergent validity for the two factors.
Moreover, the construct validity of the factors was further evaluated through
application of CRI method. Results of CRI revealed substantial evidence of construct
validity (0.93 through 0.89). These results fulfilled Hair et al.’s (2010) guidelines
where CRI .70 percent. Therefore, evidence of construct and convergent validity
were established. These results confirmed Hypothesis 2 with the finding that factors
of RLS are valid and reliable. Table 4 shows the details.
Table 4 Construct Reliability and Validity of Interchangeability and Influence
Factors
Construct α AVE CRI
Interchangeability of Leader 0.87 0.51 0.93
Influence of a Leader 0.82 0.50 0.89
Note: Composite reliability Index (CRI) formula = )²
/( )²+ Average variance extracted (AVE) formula =
)²/( )² +
Construct Validity
Since the test of psychometric properties of the constructs was one of the principal
aim this of study, a more rigorous structural equation modelling-based approach of
CFA is needed to validate the two factors extracted through application of PCA.
According to Byrne (2010), CFA would be the best choice to validate instruments
that have been fully developed and their factor structures were validated. RLS
inventory as widely used to meet this criterion.
To assess the validity of the 28-item measurement models, the analysis relied
on a number of inferential fit indices, which included the (a) Chi-square (χ2
) which is
a minimum value of the discrepancy between the observed data and the
hypothesised model, thus it determines whether a relationship exists between two
categorical variables (CMIN), (b) Degrees of Freedom (df), measures the amount for
mathematical information available to estimate model parameters, (c) Comparative

11. 11
Fit Index (CFI), which conceptualises goodness of fit by comparing an existing model
with a null model, which assumes that the latent variables in the model are not
correlated, (d) Turker-Lewis Index (TLI) compares a proposed model against a null
model and (e) Root Mean Square Error of Approximation (RMSEA), which corrects
the tendency of the model χ2
statistics to reject any specified model with a sufficient
large sample.
Byrne (2010) argues that the appropriate values for CFI range from 0-1.
Conventionally, CFI should be .90 or greater as criterion value indicative of
satisfactory model fit (Chueng and Rensvold, 2002). The value of RMSEA of .08 or
less shows a reasonable error of estimation. According to Brown and Cudeck (1993),
RMSEA ≤0.05 indicates close fit, RMSEA ≥0.05 to ≤0.08 indicates fair fit and
RMSEA ≥0.08 to 0.10 shows poor fit.
This study uses RMSEA with its point estimate and associated confidence
interval (CI) through method of lower and upper bound of the CIs in combination with
0.05 and 0.10 as the cut-off values used to validate the extent to which the
hypothesised models fit the data. When the RMSEA with its CIs are used, a given
model is rejected if the lower bound of the CI is greater than the value of 0.05.
Similarly, a given model is rejected if the upper bound of the CI is greater than the
value of 0.10 (Chen, Curran, Bollen, Kirby and Paxton, 2008).
More recently, Hu and Bentler as cited in Byrne (2010) suggested that values
below .06 as indicative of good fit.
Validity of the Measures of Interchangeability of Leader
A confirmatory factor analysis (CFA) with maximum likelihood was used to assess
interchangeability factor. CFA allows an overall assessment of the validity of
indicators of the factor. The following measures; Chi-square (χ2
), Comparative Fit
Index (CFI), Turker-Lewin Index (TLI) and Root Mean Square Error of Approximation
(RMSEA) as the cutoff values used to validate the item measured this factor. Also,
RMSEA RMSEA with its point estimate and associated confidence interval (CI)
through method of lower and upper bound were checked.
The results of the analysis demonstrated poor fit statistics; χ2
(90) = 626.480,
CFI = .86, TLT = .84, RMSEA = 0.117 and CMIN/DF = 6.96. Since the study sought
a better fit, post hoc model modifications were checked to develop a better fit and a
more parsimonious model. The model was re-estimated and three inter-correlations
among 6 errors were freed based on the suggestions of modification indices (MIs).
More specifically, the following connections were established; error 3 (v14) and error
4 (v17), error 4 (v17) and error 10 (v8) and error 6 (v4) and error 11 (v6). These
connections were allowed to co-vary to reduce the total amount to 626.480 χ2
, and
therefore ameliorate the fit indices. These connections were supported
methodologically through the use of AMOS and theoretically owning to the fact that
the two elements of the measurement errors were correlated showing commonalities
among pairs of observed variables. Prior to that three items were eliminated doe low
and cross loading issues. As such the model improved χ2
(51) = 197.773, CFI = .95,
TLT = .94, RMSEA = 0.08 and CMIN/DF = 3.87. Moreover, the RMSEA with its CI of
the lower and upper bound also fell within the desired zone; LO .04 and HI .09
providing additional evidence of model acceptance (Chen et al., 2008). Figure 1,
depicts the details.

12. 12
Figure 1: Revised Model of Interchangeability factor
The parameter estimates of the interchangeability factor were free from
offending values. All path coefficients were statistically significant at .005 levels, and
were of practical important, since the smallest value of the standardised path
coefficient was 0.50. Moreover, the squared multiple correlations (SMC), which
indicate how well the observed variables serve as measures of the latent variables,
and provide evidence of the reliability of the indicators were also investigated. It is
worth noting that the values of SMC of the interchangeability model had fulfilled the
requirement (.25) or greater for all indicators. The values ranged from .26 (v14) to
.80 (v20). This provided substantive values to explain the variance in the 12
indicator variables of interchangeability construct.
Influence of a leader
A second CFA was performed for influence of a leader factor. The results showed
that the overall fit of the model was χ2
(65) = 654.839, p = 0.001, which was
statistically significant, indicating an inadequate fit between the covariance matrix of
the observed data and the implied covariance matrix of the model. The RMSEA fell
beyond the range of acceptable value (RMSEA .05 to .08). other fit indices also did

13. 13
not satisfy the requirements of good fit CFI = .73, TLI = .68, L0 = .13, HI = .15 and
CMIN/DF =10.07. All these indices indicate poor model fit. Since the study sought a
better fit, post hoc model modifications were checked to develop a better fit and a
more parsimonious model. As such, the model was re-estimated and involved two
modifications. First, three items were eliminated due to low loading issues. Second,
six inter-correlations among 12 errors were freed based on the suggestions of
modification indices (MIs). More specifically, the following connections were
established; error 2 (v7) and error 4 (v30), error 2 (v7) and error 7 (v23), error 2 (v7)
and error 9 (v26), error 6 (v18) and error 11 (v16), error 7 (v23) and error 11 (v16)
and error 10 (v11) and error 11 (v15). These connections were allowed to co-vary to
reduce the total amount to 654.839 χ2
, and therefore ameliorate the fit indices.
These connections were supported methodologically through the use of AMOS and
theoretically owning to the fact that the two elements of the measurement errors
were correlated showing commonalities among pairs of observed variables. As such
the model improved χ2
(29) = 126.793, CFI = .93, TLT = .90, RMSEA = 0.08 and
CMIN/DF = 4.37. Moreover, the RMSEA with its CI of the lower and upper bound
also fell within the desired zone; LO .05 and HI .08 providing additional evidence of
model acceptance (Chen et al., 2008). Figure 2 presents detailed results.
Figure 2: The Revised Model of Influence of a Leader
In addition to this, the parameter estimates were also examined and were
found to be statistically significant and practically important as shown in Figure 4.5.
The loadings ranged from .41 (v7) to .74 (v31). They were free from any offending
estimates and showed logical direction. Also, the squared multiple correlations

14. 14
(SMC), which indicate how well the observed variables serve as measures of the
latent variables, and provide evidence of the reliability of the indicators were also
investigated. The values of SMC scaled from 0 to 1 (Schumacker and Lomax,
2004). It is worth noting that the values of SMC of the influence of a leader model
(other than v7) had fulfilled the requirement of (.25) or greater for all indicators. The
values ranged from .25 (v181) to .55 (v31). This provided reasonable values to
explain the variance in the 10 observed variables of influence of a leader model.
A t-test analysis was conducted to investigate the perceptions of followers
towards their organisational leader across their work experience. The results indicate
that the experienced followers hold a stand that interchageability of organisational
leader will not have much effect on organisational performance, as compared with
the perceptions of less experienced followers in the institutions of higher learning
sampled M=49.29, D=9.97 for experienced followers and M=47.73, D=11.27 for
inexperienced followers respectively. However, this difference in followers’
perceptions is hardly claimed in isolation of performing independent samples t-test,
which either confirm or otherwise the degree of significance in the difference. In
other words, independent samples t-test table must be examined.
The F statistics which determines whether the equality of variance assumption
is satisfied or otherwise was statistically insignificant F = 0.064, p ≥ 0.05, therefore,
as the variances are not significantly different, we can accept the equal variances
assumption and use the bottom line values, since Hypothesis 4 is directional.
The test of t statistic revealed that the difference obtained in the mean scores
t (407) = 3.454, p ≤ 0.001 is significant. This result confirmed Hypothesis 4 with the
finding that experienced followers perceive organisational leader as having less
influence as compared with the perception of less experienced followers in the
context of the universities sampled.
Finally, the study tested the level of influence of a leader as perceived by
followers according to their age. The results indicate that younger followers in the
institutions higher learning sampled believe that organisational leader exhibits great
influence on organisational performance compared to the perceptions of older
followers M=32.2, D=17.61 for young followers and M=27.42, D=10.46 for old
followers respectively. However, this difference in followers’ perceptions is hardly
claimed unless independent samples t-test, which either confirm or otherwise the
degree of significance in the difference is tested. In other words, independent
samples t-test table must be examined.
The F statistics which determines whether the equality of variance assumption
is satisfied or otherwise was statistically insignificant F = 0.26.29, p ≥ 0.05, therefore,
as the variances are not significantly different, we can accept the equal variances
assumption and use the bottom line values, since Hypothesis 3 is directional.
The test of t statistic revealed that the difference obtained in the mean scores
t (407) = 4.46, p ≤ 0.001 is significant. This result rejected Hypothesis 3 with the
finding that younger followers perceived greater influence of organisational leader
compared to the perception of older followers of the universities sampled.

15. 15
Discussion
The findings of the present study have contributed to the literature of leadership in
several ways. First, the results replicated the psychometric adequacy of RLS
instruments, especially this is the first study that sample academic staff in validating
the scales. The measures seem sufficient to represent the ongoing concerns of RLS
instruments in different cultural context. As well as providing and evidence that
influence and interchangeability of leader are two distinct factors. This finding
corresponded with the findings of (Awamleh and Gardner, 1999; Felfe, 2005; Shyns
et al., 2007; Chong, 2009).
Second, the results confirmed that the two factor structure of the RLS
(Awamleh and Gardner, 1999; Bligh, Kohles and Pillai, 2005). The results also
revealed adequacy of two factor model, which can be used as a means to predict
followers’ perceptions of organisational leader on ameliorating academic staff
performance. However, this two factor solution contradicts with the study of Chong
and Wolf (2009) and Cho and Meindl (n.d.), which found three factor solution in
relation to RLS underlying factors.
Thirdly, the results of the study suggest that with age, older followers feel that
organisational leader has less influence on the organisational performance. This
finding suggests more friendly leader-follower relations that leads to organisational
stability.
Finally, the findings of the study indicate the existence of relationship between
the influence of a leader and the work experience in the organisational context. This
finding is consistent with findings of Miller et al. (2004) and Chong and Wolf (2009)
who found that performance of more experienced followers was independent of their
perceived relationship with their leaders.
Limitation and the Suggestions for Future Study
This study used only online survey questionnaire to sample universities involved in
this study, future studies should consider self-administered questionnaire as well.
The study sampled only academic staff, but future study should involve
administrative staff as well. It also could be a good idea for a mixed methods to be
employed in future studies.

16. 16
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