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American Journal of  Applied 
Statistics and Economics (AJASE)

The Nexus Among Transformational Leadership and Firm Performance: 
Testing the Mediating Role of  Knowledge Sharing

Le Ba Phong1*, Do Thu Hang1

Volume 2 Issue 1, Year 2022
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Article Information ABSTRACT

Received: April 26, 2023

Accepted: May 18, 2023

Published: May 22, 2023

The purpose of  this paper is to clarify the mediating roles of  active and passive knowledge 
sharing (KS) in the relationship between transformational leadership (TL) and specific 
aspects of  firm performance namely operational and financial performance. The paper 
used Analysis of  Moment Structures (AMOS) and Structural Equation Modelling (SEM) to 
investigate the influence of  TL and knowledge sharing (KS) on aspects of  firm performance 
using data from a survey of  235 manufacturers and suppliers in Vietnam. The empirical 
findings show the significant and positive influence of  TL, and KS activities in supply chain 
networks on firm performance. It highlighted the key role of  active and passive KS in linking 
the effect of  TL on firm’s operational and financial performance. Especially, this study reveals 
that TL has a greater effect on financial performance compared to its effects on operational 
performance. Especially, active KS has a greater effect on both firm’s operational and 
financial performance compared to the influence of  passive KS. The paper has extended the 
theory of  leadership, knowledge management and organizational performance by clarifying 
the critical roles of  TL practice and processes of  KS in the supply chain in improving firm’s 
operational and financial performance.

Keywords

Transformational Leadership, 
Active Knowledge Sharing, Passive 
Knowledge Sharing, Financial 
Performance, Operational 
Performance, Supply Chain

1 Hanoi University of  Industry, Bactuliem, Hanoi, Vietnam
* Corresponding author’s e-mail: lebaphong.vn@gmail.com

INTRODUCTION
Under the increasing pressure in developing new 
products and services quickly and efficiently, firms have 
exerted to foster greater collaborative activities in supply 
chain networks to maintain and improve their long-term 
performance (Nguyen et al., 2019; Wang and Hu, 2020). 
Knowledge resources have been recognized as important 
strategic assets and have made remarkable contributions 
to firm performance and competitive advantage (Obeidat 
et al., 2016; Son et al., 2020; Nguyen et al., 2022). However, 
the major challenge for today’s organizations in building 
their knowledge capital is to be aware of  how they can 
better facilitate knowledge sharing (KS) activities among 
members in supply chain networks to contribute to the 
firm performance and success (Rajabion et al., 2019; 
Jen et al., 2020). Among the typical leadership styles, 
transformational leadership (TL) is regarded as one of  the 
most effective leadership styles with significant influences 
on KS activities and key organizational outcomes (Le 
and Lei, 2019; Phong and Son, 2020; Gui et al., 2022). 
Transformational leaders inspire and motivate KS 
behaviors among employees to obtain the greatest degree 
of  achievement for organizational performance and 
managerial performance (Ali et al., 2019; Ha et al., 2019; 
Son et al., 2020). To enrich the mechanisms and deepen 
our understanding of  the nature of  the relationship 
between KS and firm performance, this study introduces 
active and passive KS activities in supply chain as a 
new approach in explaining how TL and KS activities 
are transpired into firm performance. It is expected to 
bring deeper understanding of  the potent pathway and 
mechanism for fostering firm’s performance by following 
motives.

First, previous studies have found KS activities enable 
firms to successfully apply or replicate knowledge 
dispersed by interactive activities among individual firms 
and their supply chain networks (Mishra and Shah, 2009; 
Wang and Hu, 2020). These KS activities can not only 
enhance knowledge capital among different firms but 
also significantly contribute to increasing volume, variety, 
and engagement in improving innovation performance 
(Wang and Hu, 2020; Le and Do, 2023; Le and Le, 
2023). Even though, it is not easy for employees to share 
knowledge with others, especially in sharing knowledge 
with individuals in the other organization due to concerns 
of  information leakage and lacks of  trust (Wang and 
Noe, 2010; Nguyen et al., 2019; Le and Nguyen, 2023). 
Among the premise factors of  KS, TL is acknowledged 
as a key factor having decisive influence on KS activities 
of  individuals within an organization (Hui et al., 2018; Lei 
et al., 2019; Yin et al., 2019; Sheehan et al., 2020) or among 
members in supply chain (Birasnav, 2013; Ojha et al., 
2018). Given the important role of  TL on KS activities 
for improving firm performance, the first goal of  this 
study is to clarify TL’s effect on KS activities in supply 
chain by posing the first research question:

RQ1. Does TL positively affect KS activities in supply 
chain networks?
Second, KS is an important basis for improving firm 
performance because it provides a complete set of  
essential skills and knowledge for individuals to work or 
achieve goals more efficiently (Le and Lei, 2019; Singh et 
al., 2019). KS is often perceived a basic survival and least 
expensive strategy and a key source for firms to increase 
innovation competence (Le and Lei, 2019; Le, 2021; Le 

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and Le, 2021). Scholar pointed out that successful KS 
processes enable firms to expand knowledge capital and 
exploit and convert all available resources into dynamic 
competences for improving firm performance (Le, 2020; 
Son et al., 2020; Le, 2021). Moreover, in case of  supply 
chain, firms strive to achieve greater collaboration and 
effectiveness by leveraging their resources and knowledge 
through the process of  knowledge creating and sharing 
among members in supply chain such as suppliers and 
customers (Cao and Zhang, 2011; Jen et al., 2020; Wang 
and Hu, 2020). Firms in supply chain tend to look 
outside their organizations for developing collaboration 
opportunities with partners to successfully innovate and 
to ensure that  supply chain members are efficient and 
responsive to dynamic market needs (Cao and Zhang, 
2011; Nguyen et al., 2018). However, the relationship 
between KS in supply chain and firm performance 
seem to be ignored in the current literature (Jen et al., 
2020; Li, 2020). To fill the research gaps and investigate 
potential effects of  KS activities in supply chain on firm 
performance, the paper proposes the second research 
question as follows.

RQ2. Do KS activities in supply chain significantly 
affect firm performance?
Third, leadership and KS are widely considered the 
strategic resources for firms to foster organizational 
performance (Hassan and Hatmaker, 2015; Son et al., 
2020; Le and Le, 2021). Son et al. (2020) highlighted 
the decisive role of  leadership in creating a positive 
influence on firm performance by establishing a KS 

climate among employees. In particular note, KS climate 
is found as a significant mediator between leadership/TL 
and key outcomes of  an organization such as innovation 
performance (Zheng et al., 2017; Le and Do, 2023), and 
organizational performance (Son et al., 2020; Le and Le, 
2021). 
However, there have been few studies investigating 
the mediating role of  KS in supply chain especially 
in term of  active and passive KS between TL and key 
organizational outcomes such as specific forms of  firm 
performance (Son et al., 2020; Le, 2021). This limits firm’s 
understanding of  the different ways according to which 
leaders can apply and follow to achieve specific goals 
of  performance. To address this theoretical gap, third 
research question is proposed:

RQ3. Do active and passive KS in supply chain 
mediate the effects of  TL on firm’s operational and 
financial performance?
To address the above research questions, this study will 
develop a research model to investigate TL’s impacts on 
firm’s operational performance and financial performance 
through the mediating role of  active and passive KS 
activities in supply chain networks (see Figure 1).
This study will apply the structural equations modeling 
to examine the relationship among the latent factors in 
the proposal research model through a survey data of  
235 firms in Vietnam. The authors expect that, this study 
will provide valuable theoretical initiatives and specific 
practical guidance for directors/managers to improve the 
operational and financial performance in their firms.

Figure 1: Proposal research model

LITERATURE REVIEW 
Influence of  TL on Firm Performance
Transformational leadership is well known as one of  the 
higher-ranking leadership styles (Lei et al., 2017; Le et al., 
2018; Cao and Le, 2022). It describes the leaders who have 
capabilities of  inspiring the employees to get the highest 
degrees of  achievement and outcomes (Le, 2020; Phong 
and Son, 2020; Lathong et al., 2021). Literature defined 
TL with four characteristics namely idealized influence, 
intellectual stimulation, inspirational motivation, and 
individualized consideration (Bass, 1985; Bass, 1990; Le 
and Lei, 2019; Le et al., 2021; Le et al., 2022). Idealized 
influence reflects abilities of  leaders to provide a vision 
and perception of  mission, instilling pride, gaining 

respect and trust; intellectual stimulation involves leaders’ 
ability to promote intelligence, rationality and attentive 
problem-solving; inspirational motivation reflects 
leaders’ interest in communicating high expectations, 
using symbols to focus efforts, and expressing important 
purposes in simple ways; and individualized consideration 
refers leaders’ interest in coaching and advising, personal 
attention,  and treating each employee individually. 
Firm performance is understood in various meanings 
for different people since it contained many facets. 
According to Madella et al. (2005), firm performance 
reflects firm’s capability in obtaining and handling 
properly organizational resources relating to human, 
finance and material to attain the organization’s targets. 

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Lee (2008) viewed firm performance as the results/
output of  an organization that measured against its 
intended objectives. Richard et al. (2009) defined firm 
performance comprising three aspects of  organization’s 
outcomes namely financial performance, product market 
performance, and shareholder return. Tsai and Yen 
(2008) focus on financial and market performance to 
evaluate performance of  an organization. The current 
study uses operational and financial performance 
to evaluate firm performance as they are the crucial 
constituents of  organizational performance had critical 
impacts on organizational survival and competitiveness 
(Wang et al., 2016; Son et al., 2020). According to Wang 
et al. (2016), operational performance reflects the fruit 
in managing cost, developing quality, achieving of  
customer satisfaction, responsiveness and productivity; 
while financial performance manifests the success of  an 
organization in exerting its assets to bring about revenues 
that represented in its financial statements.
Current literature indicated that among different 
leadership style, TL plays a crucial role and servers as 
antecedents of  key outcomes and firm performance 
(Arif  and Akram, 2018; Cao and Le, 2022). Prior studies 
noted that practicing TL is one of  the best solutions to 
increase the firm performance at both individual and 
group levels (Bass 1985; Van et al., 2018; Sengphet et 
al., 2019). Many prior researches had explained for the 
positive relationship between TL and firm performance 
(e.g., Judge and Piccolo, 2004; Arif  and Akram, 2018; 
Son et al., 2020). Prior research showed that firm 
performance is fostered by transformational leaders’ 
capabilities of  motivating and inspiring individuals to 
work and attain outcomes beyond expectations (Bass, 
1985). They build systems with provision direction, 
vitality and enthusiasm to the organization, producing 
good chance for employees learning and innovating for 
boosting firm performance (Tushman and Nadler, 1986). 
Judge and Piccolo (2004) denoted that transformational 
leaders have positive relationship with job performance 
and organizational performance, they inspired followers 
toward the fulfillment of  the desired result, with or 
without the rewards in line with the fruit. Wang et al.’s 
(2011) meta-analytic study pointed out that TL is 
strongly and positively associated with firm performance. 
According to Birasnav (2013), transformational leaders 
significantly predict effectiveness of  supply chain 
management, and as a result, firms’ overall performance 
has been improved. In the same vein, Son et al. (2020) 
stated that TL influences firm performance by promoting 
gradual contributions of  followers through the striving 
efforts further than the call of  obligation. Their empirical 
findings show TL’s significant effects on operational and 
financial performance.
Based on above arguments, the following hypotheses are 
proposed:

H1a: TL positively affect firm’s operational 
performance.

H1b: TL positively affect firm’s financial performance.

Mediating effect of  KS in supply chain between TL 
and firm performance
Knowledge is widely accepted as a crucial resource for 
firms to develop competitive advantages before the 
changes of  environment (Lei et al., 2019; Lei et al., 2021; 
Cao et al., 2022; Ha et al., 2023). As a key component of  
knowledge management process, KS helps to maximize a 
firm’s ability to manage knowledge and allows employees 
in organization to work or achieve goals more efficiently 
(Le and Lei, 2017; Yang et al., 2018; Le and Le, 2023). 
KS is defined as the process of  interchanging data, 
information, know-how, and expertise among individuals 
to accomplish both personal and organizational goals 
(Wang et al., 2016; Yang et al., 2018; Le and Lei, 2019). This 
study uses the term of  KS to describe the exchange of  
knowledge between organizations rather than individuals. 
According to this approach, KS in supply chain refers to 
processes of  exchanging data, information, know-how, 
experience and new ideas among supply chain members 
to achieve the common goals. This study separates KS 
in supply chain into two sub-processes called active 
and passive KS. Active KS reflects the voluntary and 
proactive degree of  firms in communicating knowledge 
and information to supply chain members, whereas 
passive KS reflects the readiness level of  firms to provide 
knowledge and information to those in supply chain in 
need or request it. We use this classification because KS in 
supply chain is a two-way process. In addition, active and 
passive KS commonly represent two divergent behavior 
tendencies of  an organization toward KS activities. 
Literature stresses the important and significant impact 
of  leadership on KS processes (Lei et al., 2019; Nguyen 
et al., 2022; Ha and Le, 2023; Phong and Thanh, 2023). 
Specifically, according to Manafi and Subramaniam (2015), 
transformational leaders can encourage KS processes by 
transforming employees’ positive attitudes and behaviors 
toward KS in organization. Choi et al. (2016) and Xiao 
et al. (2017) indicated that transformational leaders can 
create an appropriate climate for cultivating employees’ 
knowledge and skills, and encouraging them to share 
a lot of  knowledge and expertise with the others like 
colleagues and partners in supply chain for common 
goals. Lei et al. (2019) showed that under leadership by 
TL, employees are more willing to share their personal 
knowledge and expertise with others due to collaborative 
motivation for a common goal and the belief  that 
leaders and colleagues are worth trusting. This is very 
important to develop a culture of  sharing knowledge 
among individuals of  organizations in supply chains. In 
particular note, scholars indicated significant influences 
of  TL on competence-based trust of  those they interact 
with (Le and Lei, 2018; Lei et al., 2019; Ha and Le, 2021). 
As a result, competence-based trust developed by TL will 
supports KS process among members in supply chain by 
reducing information loss from sender to receiver (Ajmal 
and Kristianto, 2012; Lei et al., 2019). Recent studies also 
demonstrated that TL is one of  the most appropriate 
leadership styles for creating an atmosphere of  trust 

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and developing the knowledge-centered culture which 
in turn significantly foster the willingness of  individuals 
for sharing knowledge within an organization (Lei et al., 
2019; Sheehan et al., 2020; Yin et al., 2019; Le et al., 2022) 
or sharing knowledge among supply chain members 
(Nguyen et al., 2019; Wang and Hu, 2020). These 
arguments support the positive influence of  TL on KS 
activities in supply chain, so the following hypotheses are 
posed:

H2a.b: TL positively influences active and passive KS 
in supply chain members.
With respect to the KS-firm performance relationship, 
despite lack of  study has evaluated the direct effect of  KS 
activities in supply chain on firm performance (Samuel 
et al., 2011; Sangari et al., 2015), prior studies have also 
shown the evidence supporting this relationship (Hult et 
al., 2004; Sangari et al., 2015; Wang and Hu, 2020). Shaw 
et al. (2003) have realized that firm performance can be 
improved by coupling knowledge management initiatives 
with in supply chain networks and argued that firms must 
possess and share knowledge about different facets of  
the supply chain to achieve success. In the similar vain, 
Hult et al. (2004) indicated that the knowledge acquisition 
activities result in reduced cycle time as a performance 
outcome at the supply chain level and the knowledge 
development process is an important antecedent to supply 
chain efficiency. Jansen et al. (2006) argue that the exchange 
of  knowledge and information helps firms avoid being 
constrained inside their knowledge boundaries, thereby 
creating opportunities for firms to renew knowledge and 
improve firm performance. According to Sangari et al. 
(2015), realized that KS activities in process of  knowledge 
management will enhance supply chain’s knowledge flows 
and ultimately will enhance supply chain performance. 
Recently, Wang and Hu (2020) pointed out that KS 
process among members in supply chain facilitates the 
creation of  new ideas and processes for improving the 
innovation performance. Their empirical findings showed 
that KS process in supply chain networks is positively 
associated with innovation performance. Based on the 
above discussion this study argued, the vital knowledge 
and information gained from the KS process in supply 
chain will help firms use their material, financial and other 
resources more effectively for improving operational 
and financial performance. So, following hypotheses are 
posed:

H3a.b: Active KS in supply chain significantly affects 
firm’s operational and financial performance.

H3c.d: Passive KS in supply chain significantly affects 
firm’s operational and financial performance.
Above argument supports the mediating roles of  KS in 
supply chain by indicating that TL significantly influences 
KS in supply chain, which in turn positively affects firm 
performance. In addition, the current literature has also 
verified the mediating role of  KS in the relationship 
between leadership and key organizational outcomes. 
For example, Uddin et al. (2017) justified that effective 
leadership plays a significant role in promoting a supportive 

climate for exposing knowledge into organization 
innovation. These scholars found that TL significantly 
predicts firm’s innovation capability and performance 
via fostering the KS activities of  individuals. Zheng et al. 
(2017) argued that KS activities contribute significantly 
to innovation efforts and help ameliorate organizational 
performance at the firm level. Their findings revealed that 
KS positively mediates the relationship between TL and 
project-based innovation performance. Recently, Ojha 
et al. (2018) pointed out that orientation of  learning and 
KS in supply chain significantly mediates the relationship 
between TL and supply chain ambidexterity. Although the 
mediating role of  KS in supply chain between leadership 
and firm performance is supported, empirical research on 
relationship between KS among supply chain members 
and firm performance is still lacking. We, therefore, 
propose the hypotheses:

H4a: KS in supply chain mediate TL effects on firm’s 
operational performance.

H4b: KS in supply chain mediate TL effects on firm’s 
financial performance.

METHODOLOGY
Sample and Data Collection
The paper used the data collected from August to 
November 2020 through a survey with the final sample 
size of  235 manufacturers and suppliers which are 
randomly selected from the initial list of  more than 
15,000 Vietnamese enterprises published in 2018 by 
Vietnam Yellow Pages. We then communicated with 
representatives of  800 firms by phone, e-mail and/or made 
personal visits in some cases to explain the purpose of  
our research and ask for their assistance and cooperation 
in gathering the data, among which 500 ones agreed to 
assist us in data collection. The respondents in this survey 
need to be managers or leaders of  firms with supply chain 
collaboration experience. This study used measurement 
items that are utilized and developed from prior works. 
We released 500 question sheets and received back 356 
ones, of  which 121 responses were excluded from the 
sample because of  missing data, and 235 are usable with 
the response rate of  47.0%. We used the Armstrong and 
Overton’s (1977) method to assess potential non-response 
bias. Chi-square and independent sample t-tests were used 
to compare the first 80 respondents and the last 80 ones 
via demographic variables namely age and gender. The 
results demonstrated there were no significant differences 
between the two groups of  responses (p > 0.05).

Variable Measurement
The paper used measures developed by previous studies 
to ensure the validity and reliability of  the measurements. 
All items are measured via five-point Likert-type scales 
ranging from “1” (strongly disagree) to “5” (strongly 
agree).

Transformational Leadership 
This study used 8 items From Le’s (2021) study to 

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assess the perceptions of  employees about the TL 
style of  their direct supervisors. These items were also 
adopted in previous studies of  TL in Chinese context, 
one of  the emerging countries (e.g., Le and Lei, 2019). 
A sample item is “my supervisor encourages me to think 
about problems from a new perspective”. Knowledge 
sharing in supply chain. We used three items adapted 
from the study of  Le (2021) to measure active and 
passive KS in supply chain networks. This study then 
splits KS behaviors into two versions: “active KS” and 
“passive KS”. Active KS was measured by three items 
reflecting the proactive level of  supply chain members in 
sharing new knowledge and information that they have. 
A sample item is “we proactively share our new work 
reports and technical documents we have to other supply 
chain members”. Passive was measured by three items 
reflecting the readiness level of  supply chain members 
to provide knowledge and information to those request 
it. A sample item is “we are willing to share new work 
reports and technical documents to other supply chain 
members when they ask for it”. Firm performance. 
This study used 11 items obtained from the Son et al. 
(2020) study to evaluate firm’s operational and financial 
performance, where operational performance is assessed 
by five items that describe the firms’ successful degree in 
obtaining the quality development, customer satisfaction, 
responsiveness, productivity, and cost management. A 
sample item is “customer satisfaction of  our firm is better 
than that of  key competitors”. Financial performance is 
assessed by six items to describe the firm’s capabilities in 
using its resources to result in revenues that exhibited in 
the financial statements of  an organization.  A sample 
item is “return on investment of  our company is better 
than that of  key competitors”. Control variable. Firm 

size serves as the control factor to explain for variations 
among organization and its potential effect on firm 
performance. 

Data Analysis Methods
This study utilized Structural Equation Modeling (SEM) 
to test proposal hypotheses in the research model by two 
reasons. First, SEM method has been widely used due to 
its ability to demonstrate versatile regression correlations 
on a single model and test (Kline, 2015). Second, it is also 
proper and practical to identify interaction and mediation 
effects (Lei et al., 2019). As a result, this study has used 
SEM though AMOS software for the test of  the structural 
model and hypotheses based on the data gathered from 
the 235 manufacturers and suppliers. Data analysis was 
conducted using SPSS and AMOS version 22.

RESULTS
Measurement model
We first tested the reliability of  the measures for the 
constructs by examining the private Cronbach’s alpha 
coefficients (Cα). The results of  statistics are range of  0.93 
- 0.97, which are all over than Nunnally and Bernstein’s 
(1994) recommended level of  0.7. We continuously 
analyze confirmatory factor (CFA) to evaluate the 
universal measurement model to check the discriminant 
and convergent validity. 

Convergent Validity
As shown in Table 1, all factor loadings are range of  
0.698 - 0.997; CR values are range of  0.93 - 0.97; and the 
AVE values are range of  0.73 - 0.94. According to Hair et 
al.’s (2006) criteria, these measurements meet the criteria 
on convergent validity. 

Table 1: Standardize loading and reliabilities for measurement model
Construct Item Standardize loading t-value AVE CR Cα
Transformational leadership (TL) 8 - - 0.76 0.96 0.96

TL1 0.839*** 17.9
TL2 0.891*** 20.5
TL3 0.875*** 25.8
TL4 0.857*** 18.7
TL5 0.870*** 19.4
TL6 0.895*** 20.7
TL7 0.888*** 20.3
TL8 0.864*** 19.1

Active knowledge sharing (AKS) 3 - - 0.85 0.94 0.94
AKS1 0.972*** 32.5
AKS2 0.940*** 31.1
AKS3 0.860*** 22.6

Passive knowledge sharing (PKS) 3 - - 0.94 0.97 0.97
PKS1 0.997*** 68.9
PKS2 0.982*** 68.7
PKS3 0.929*** 34.5

Operational performance (OP) 5 - - 0.74 0.93 0.93

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OP1 0.858*** 19.9
OP2 0.698*** 13.3
OP3 0.918*** 23.7
OP4 0.893*** 22.0
OP5 0.920*** 23.9

Financial performance (FP) 6 - - 0.73 0.94 0.94
FP1 0.805*** 17.7
FP2 0.918*** 24.9
FP3 0.758*** 15.6
FP4 0.930*** 25.5
FP5 0.922*** 25.3
FP6 0.808*** 17.8

Notes: Cα ≥ 0.7; CR ≥ 0.7; AVE ≥ 0.5; *** Significant at p<0.001.

Discriminant Validity 
Discriminant validity refers to the level of  the factors 
that assumed to assess a certain construct do not forecast 
conceptually independent criteria (Kline, 2015). This 

paper applies the method of  Fornell and Larcker (1981) 
relating to compare the AVE’s square root with the 
correlations among the dormant constructs (see Table 2).
Table 2 indicated that the AVE’s square root of  each 

Table 2: AVE’s square root and descriptive statistics from constructs
Constructs Mean SD TL AKS PKS OP FP
TL 3.23 0.59 0.87
AKS 3.67 0.62 0.54*** 0.92
PKS 3.71 0.70 0.32*** 0.38*** 0.96
OP 3.57 0.57 0.51*** 0.59*** 0.42*** 0.86
FP 3.71 0.61 0.54*** 0.62*** 0.43*** 0.63*** 0.85

Note: Diagonal components (in bold) are the AVE's square root; off-diagonal components are the constructs' correlation coefficients.

construct is higher than the correlation coefficients 
among variables of  research model. Overall, the above 
results show strong evidence for both the reliability of  
the constructs, and the discriminant validity of  scales.
Regarding the satisfactory of  measurement model, 
we estimated the fit of  measurement model based on 

examining: (1) absolute fit values (such as GFI; CMIN/
df, and RMSEA); and (2) incremental fit values (such as 
NFI, AGFI, and CFI). Table 3 shows that all fit indices 
of  the measurement model were satisfactory; thus, the 
model fit the data.

Table 3: The fit indices of  the CFA model
Fit index Scores Proposal threshold values
Absolute fit measures
CMIN/df  (Chi-square/df) 1.629 ≤ 2a;  ≤ 5b

GFI (goodness of  fit index) 0.877 ≥ 0.90a;  ≥ 0.80b 
RMSEA (root mean square error of  approximation) 0.052 ≤ 0.08a;  ≤ 0.10b

Incremental fit measures
NFI (incremental fit measures including normed fit index) 0.941 ≥ 0.90a; 
AGFI (adjusted goodness of  fit index) 0.847 ≥ 0.90a;  ≥ 0.80b

CFI (comparative fit index) 0.976 ≥ 0.90a; 
Notes: a: good fit; b: acceptable fit (Schermelleh-Engel et al., 2003; Le and Lei, 2019).

Structural Model
This study used structural equation model (SEM) with 
maximum likelihood estimation procedures to test the 
proposal hypotheses. The fit indices of  the structural 
model are satisfactory (χ2=463.05; df  = 264; RMSEA 
= 0.057; GFI = 0.867; CFI = 0.972; TLI = 0.968), 

suggesting that the relationships among latent constructs 
fit the data. 

Direct Effect Analysis
The results in Figure 2 and Table 4 demonstrate that 
effects of  independent variables on dependent ones 

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Figure 2: Path coefficients of  the structural model
Notes: ***p< 0.001; ----- Non-significant paths

ensure statistically significant. Hypotheses H1-H3 are, 
therefore, supported.  Specifically:
Hypothesis H1a.b relating to the effect of  TL on aspects 
of  firm performance. The results in Figure 2 and Table 

4 revealed that TL’s influences on financial performance 
(β= 0.267; p < 0.001) is larger than its influence on 
operational performance (β= 0.246; p < 0.001).
Regarding hypothesis H2a.b, the results showed that TL’s 

Table 4: Results of  the direct relationships
Hypothesis Relationship Beta Standard error t-value Results
H1a TL -->Operational performance 0.246*** 0.070 3.570 Supported
H1b TL --> Financial performance 0.267*** 0.073 4.023 Supported
H2a TL --> Active knowledge sharing 0.553*** 0.067 9.101 Supported
H2b TL --> Passive knowledge sharing 0.335*** 0.078 5.203 Supported
H3a AKS --> Operational performance 0.395*** 0.061 5.984 Supported
H3b AKS --> Financial performance 0.417*** 0.063 6.568 Supported
H3c PKS --> Operational performance 0.202*** 0.047 3.614 Supported
H3d PKS --> Financial performance 0.194*** 0.048 3.619 Supported

Notes: ***significant at the 0.001 level.

impact on aspects of  KS activities in supply chain is very 
considerable. The findings showed significant influences 
of  TL on active KS activity (β= 0.553; p < 0.001) is more 
significant than its effect on passive KS activity (β = 
0.335; p < 0.001) of  firms in supply chain networks.
For hypotheses H3a.b and H3c.d, the results showed 
that, active KS has greater impacts on both operational 
and financial performance compared with the effect 
of  passive KS. Specifically, the influences of  active 
KS on operational performance (β= 0.395; p < 0.001) 
and financial performance (β= 0.417; p < 0.001) are 
statistically significant. Similarly, the impacts of  passive 
KS on operational performance (β= 0.202; p < 0.001) 
and financial performance (β= 0.194; p < 0.001) are also 
statistically significant and supported.
The hypotheses assessments’ results are attained after 
investigating the effects of  control variable of  firm 
size. The findings did not support the control role of  

firm size because its effects on operational and financial 
performance are not statistically significant. 

Indirect and Total Effect Analysis
To provide evidence on the mediating roles of  active and 
passive KS in supply chain networks between TL and 
specific aspects of  firm performance namely operational 
and financial performance, this study used the bootstrap 
confidence intervals method with 5,000 iterations as the 
suggestion of  Preacher and Hayes (2008), to test the 
significance of  indirect effects (see Table 5).
The results in Table 5 pointed out that the indirect effect 
of  TL on operational performance (β= 0.286; p < 0.001) 
and financial performance (β= 0.295; p < 0.001) are 
significant within the range of  confidence intervals. In 
general, these findings provide the evidence to confirm 
the mediating role of  KS activities in supply chain 
between TL and firm performance.

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DISCUSSION AND IMPLICATIONS 
Supply chain management and KS practice represent 
alternative approaches that have generated a lot of  
interests among scholars and practitioners. Scholars 
and practitioners considered have made great efforts 
to understand and identify how leadership practice can 
facilitate KS activities for fostering firm performance. 
It is unfortunately there is little guidance in the extant 
literature on how TL and KS activities among supply chain 
members affect specific aspects of  firm performance 
namely operational and financial performance (Wang 
and Wang, 2012; Sangari et al., 2015; Arif  and Akram, 
2018). Accordingly, by investigating the mediating role of  
KS activities in supply chain networks between TL and 
aspects of  firm performance, the findings of  this study 
significantly contribute to developing and advancing 
theory of  leadership, knowledge management and 
performance management by some following important 
reasons.
First, the paper significantly contributes to filling the 
theoretical gaps and increasing the understanding of  
TL’s effects on specific aspects of  KS activities in supply 
chain networks. Indeed, knowledge is a pivotal resource 
of  organization that bring firms a sustainable competitive 
advantage to survive before the increasingly changes of  
business environment and fierce competition (Lei et al., 
2019; Son and Phong, 2023; Than et al., 2023). Many 
organizations have invested the huge time, efforts and 
money for improving their organizational knowledge 
capital through enhancing KS activities in their firm. 
However, they still fail to share knowledge and losing 
billions of  dollars each year (Babcock, 2004; Le and Tran, 
2020). An important reason for the failure of  improving 
KS is the lack of  understanding of  how leadership styles 
or specific leadership characteristics influence KS (Wang 
and Noe, 2010; Le and Tran, 2020), especially in the 
context of  supply chain networks (Birasnav, 2013; Ojha 
et al., 2018). To address these theoretical gaps, the paper 
has examined the effects of  TL on active and passive 
KS activities of  supply chain members. The findings 
have underlined the TL’s essential role in exhorting 
activity of  active KS in comparison with passive KS 
activity. The findings reveal that the positive effects of  
transformational leaders have prompted and encouraged 
employees to voluntary and proactively share their new 
knowledge and information with co-workers in supply 
chain networks for creating greater benefit and achieving 
their common goals. 
Second, active and passive KS represent two different 

forms of  KS activities of  organizations in supply chain 
networks. By investigating the effects of  active and 
passive KS on aspects of  firm performance, the paper 
has contributed to the expanse and arousal the new ideas 
of  improving firm performance. The findings show that, 
active and passive KS act as the significant predictors of  
operational and financial performance. The findings are 
consistent with Iqbal et al.’s (2019) findings on critical 
role of  KS and knowledge management processes in 
improving firm performance. The paper has indicated 
that active KS has greater impacts on two aspects of  firm 
performance in comparison with influence of  passive 
KS. In other words, these findings emphasize the critical 
role of  active KS activities in supply chain networks, and 
consider active KS activities as the main solution to foster 
firm performance. So, posing the right investment and 
great efforts on stimulating active KS among members 
in supply chain networks is right and possible solution 
for directors and managers to effectively improve firm 
performance. Moreover, transformational leaders also act 
as a driving force of  nurturing firm performance. The 
findings of  this study disclose the greater influence of  
TL on financial performance in comparison with TL’s 
effect on operational performance. From these findings, 
the paper implies that focusing on practicing the TL style 
might help directors/managers to follow and attain better 
financial goals in term of  the return on investment and 
sales, the growth of  profit and sales, and the average 
profitability. 
Third, previous studies have shown the positive 
influences of  TL and KS activities on some particular 
spheres of  firm performance such as operational and 
financial performance (Ojha et al., 2018; Son et al., 2020). 
However, there still exists a research gap in the literature 
that helps to explain potential mediating role of  KS 
processes in supply chain networks in the relationship 
between TL and firm performance in supply chain 
(Wong and Wong, 2011; Birasnav, 2013; Ojha et al., 2018; 
Son et al., 2020). This limits our understanding of  the 
mechanism by which TL can interact with KS activities 
in supply chain to produce significant influences on firm 
performance in certain forms. As a result, this paper 
contributes significantly to advancing the theory of  
leadership and performance management by evaluating 
the mediating effect of  active and passive KS activities 
among supply chain members in the relationship between 
TL and two specific types of  firm performance. The 
empirical findings have affirmed the mediators of  active 
and passive KS in supply chain networks and spotlighted 

Table 5: Confidence intervals of  the indirect effects
Hypothesis Path Direct 

effects
Indirect 
effects

Total 
effects

Bias-corrected confidence intervals
Lower confidence 
level

Upper confidence 
level

H4a TL-->KS-->OP 0.246*** 0.286*** 0.532*** 0.225 0.367
H4b TL-->KS-->FP 0.267*** 0.295*** 0.562*** 0.235 0.376

Note: *** p < 0.001.

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that TL practices can significantly affect operational and 
financial performance directly or indirectly by stimulating 
active and passive KS activities among supply chain 
members. From these findings, the paper might serve as 
the valuable reference that provides valuable insights into 
the needful conditions and the new pathway to promote 
firm’s operational and financial performance.
Finally, prior studies argued that in the context of  
developing and emerging countries like Vietnam, 
Vietnamese firms are still facing with many difficulties and 
quite sensitive to changes in technology and innovation 
(Nguyen et al., 2019; Son et al., 2020; Lei et al., 2020). The 
majority of  firms in developing countries like Vietnam are 
small and medium size, account for approximately 98.1%, 
and lack of  capital, resources, and R&D capabilities to 
innovate for improving firm performance (Lei et al., 2019; 
Son et al., 2019). Thus, improving firm performance by 
huge investments in technological innovation or physical 
resources is not feasible (Than et al., 2021; Than et al., 
2023; Tran et al., 2023). Leadership and knowledge 
resource of  supply chain members are well known as the 
main drive of  innovation performance and organizational 
performance (Birasnav, 2013; Ojha et al., 2018; Sengphet 
et al., 2019; Son et al., 2020). The findings of  this paper 
have, therefore, implied that focusing on TL practice to 
stimulate knowledge and intellectual resource of  firms 
in supply chain networks seems to be one of  the most 
optimal and effective strategies for firms in developing 
countries to follow operational and financial performance.

RECOMMENDATION AND RESEARCH 
LIMITATION
This study has also inevitably limitations. First, the 
paper employs cross-sectional design this may arise the 
circumstance according to which causal correlations 
might fluctuate in the long-run due to KS activities 
of  firms in supply chain networks may change over 
time. A longitudinal investigation will assist to control 
this limitation and affirm the result. Second, this study 
examined the correlation based on self-report data. This 
may lead to the limitation of  common method bias 
or single source bias. Future research should test the 
relationship among the constructs, especially in term 
of  measuring firm performance based on objective data 
to consolidate the findings. Third, the results and the 
benchmarks in this paper are more appropriate for the 
context of  Vietnamese firms. Future research should 
implement in other circumstances to show clearer 
picture/implication in terms of  the correlation among 
these factors. 
Finally, active and passive KS activities are found to 
have significant impacts on firm performance. So, it is 
needed to perform further studies in future for exposing 
deeper the effects of  TL and active and passive KS in 
supply chain on the other strategic components of  firm 
performance such as innovation performance and market 
performance or key outcome of  firms such as innovation 
capability and competitive advantage.

CONCLUSION
Generally, this study significantly contributes to filling 
the gaps on the relationship between TL, KS behaviors 
and organizational performance. Especially, the paper 
has enhanced understanding and pushed the theory 
of  leadership and organizational behaviors forward by 
clarifying the mediating roles of  KS processes in linking 
TL and specific forms of  organizational performance 
namely operational and financial performance.

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