El papel del liderazgo de servicio y del liderazgo del conocimiento en el
análisis de grandes datos: Perspectivas de ociales militares en la Escuela
Superior de Guerra del Ejército del Perú.
Enviado: 10 de Marzo 2024 ● Evaluado: 27 de Marzo 2025 ● Aprobado: 03 de Abril 2025
ISSN: 2520 - 7628 (Impreso), 2789-2514 (En línea)
https://doi.org/10.60029/rcesge
Diego Noreña-Chávez
1
https://orcid.org/0000-0001-5292-2152
Universidad de Lima, Escuela de Posgrado Universidad de Lima, Lima, Perú
5
Grado académico:
Maestro en Gestión Pública
Correo electrónico: diego.norena1@gmail.com
Citar como:
Noreña-Chávez, D. (2025). El papel del liderazgo de servicio y del liderazgo del
conocimiento en el análisis de grandes datos: Perspectivas de ociales militares en la
Escuela Superior de Guerra del Ejército del Perú. Revista Científica De La Escuela Superior
De Guerra Del Ejército, 4(1), 77-94.
https://doi.org/10.60029/rcesge.v4i1ar5
Resumen
Determinar la inuencia del liderazgo servicial y del liderazgo del conocimiento en la adopción de la analítica
de grandes datos dentro de la Escuela Superior de Guerra del Ejército. En particular, se analiza cómo el
liderazgo impacta la toma de decisiones basada en datos y si dicha relación es mediada por el capital
intelectual. El estudio adopta un enfoque cuantitativo, utilizando el modelado de ecuaciones estructurales
mediante mínimos cuadrados parciales para analizar datos recopilados de 187 ociales del Curso de
Comando y Estado Mayor de la Escuela Superior de Guerra del Ejército del Perú. Se evalúan siete hipótesis
relacionadas con los efectos directos y mediadores del liderazgo sobre la adopción de la analítica de grandes
datos. Los hallazgos conrman que el liderazgo servicial promueve signicativamente la adopción de la
analítica de grandes datos al fomentar una cultura organizacional empoderada y orientada a los datos. El
liderazgo del conocimiento también cumple un rol mediador clave, facilitando la implementación exitosa de
dicha analítica mediante mecanismos de intercambio de conocimiento. No obstante, el capital intelectual no
inuye directamente en la adopción de la analítica de grandes datos, lo que indica que su impacto depende
de otros factores estratégicos como el compromiso del liderazgo, la transformación digital y los marcos de
inteligencia operativa. Los resultados ofrecen orientaciones concretas para instituciones militares
interesadas en fortalecer sus capacidades analíticas. El estudio destaca la importancia de los programas de
formación en liderazgo para fomentar la toma de decisiones basada en datos, el intercambio de
conocimientos y el desarrollo de estrategias de inteligencia colaborativa. Asimismo, se resalta la necesidad de
institucionalizar el liderazgo del conocimiento como parte integral de la modernización militar para
optimizar operaciones de inteligencia, planicación de misiones y gestión estratégica de la logística. Este
estudio contribuye al campo de la investigación sobre liderazgo y analítica en contextos de defensa, al
proporcionar evidencia empírica sobre cómo los modelos de liderazgo inuyen en la adopción de la analítica
de grandes datos en instituciones militares. La integración del liderazgo servicial y del liderazgo del
conocimiento en la estrategia militar representa un enfoque novedoso para mejorar la eciencia operativa
mediante sistemas de inteligencia basados en datos. Esta investigación es especialmente valiosa para líderes
militares, responsables de políticas de defensa y planicadores estratégicos que buscan modernizar los
procesos de toma de decisiones dentro del Ejército del Perú y otras organizaciones castrenses.
Palabras clave: Capital intelectual, analítica de grandes datos, Escuela Superior de Guerra del Ejército del
Perú
Revista Cientíca de la Escuela
Superior de Guerra del Ejército
Volumen IV, Numero I, Mayo 2025
Diego Noreña-Chávez
1
https://orcid.org/0000-0001-5292-2152
Universidad de Lima, Escuela de Posgrado Universidad de Lima, Lima, Perú
5
Maestro en Gestión Pública
Email: diego.norena1@gmail.com
Cite as:
Noreña-Chávez, D. (2025). El papel del liderazgo de servicio y del liderazgo del
conocimiento en el análisis de grandes datos: Perspectivas de ociales militares en la
Escuela Superior de Guerra del Ejército del Perú. Revista Científica De La Escuela Superior
De Guerra Del Ejército, 4(1), 77-94.
https://doi.org/10.60029/rcesge.v4i1ar5
Abstract
This study explores the impact of Servant Leadership (SL) and Knowledge Leadership (KL) on the adoption
of Big Data Analytics (BDA) within military institutions. Specically, it analyzes how leadership inuences
data-driven decision-making and whether Intellectual Capital (IC) mediates this relationship. The study
employs a quantitative research design, using Partial Least Squares Structural Equation Modeling (PLS-SEM)
to analyze data from 187 officers enrolled in the Command and General Staff Course at the Peruvian Army
War College (Escuela Superior de Guerra del Ejército del Perú). The model tests seven hypotheses regarding
leadership's direct and mediating effects on BDA adoption. The results conrm that Servant Leadership (SL)
signicantly enhances BDA adoption by fostering an empowered, data-driven organizational culture.
Knowledge Leadership (KL) also serves as a key mediator, facilitating the successful implementation of BDA
through knowledge-sharing mechanisms. However, Intellectual Capital (IC) does not directly inuence BDA
adoption, suggesting that its role depends on additional strategic enablers, such as leadership commitment,
digital transformation, and operational intelligence frameworks. These ndings provide practical insights for
military institutions seeking to enhance their analytics capabilities. The study highlights the importance of
leadership development programs in promoting data-driven decision-making, knowledge-sharing, and
collaborative intelligence strategies. In addition, it emphasizes the need to institutionalize knowledge
leadership within the military to optimize intelligence operations, mission planning, and strategic logistics
management. This study contributes to the growing body of research on leadership and military analytics by
providing empirical evidence on how leadership models impact BDA adoption in defense institutions.
Integrating Servant Leadership and Knowledge Leadership into military strategy introduces a novel
framework for enhancing operational efficiency through data-driven intelligence systems. This study is
particularly valuable for military leaders, defense policymakers, and strategic planners looking to modernize
decision-making processes within the Peruvian Army and other military organizations.
Keywords: Intellectual capital, big data analytics, Army War College
Sent: Mach 10, 2025 ● Evaluated: March 27, 2025 ● Aprobed: April 03, 2025
ISSN: 2520 - 7628 (Impreso), 2789-2514 (En línea)
https://doi.org/10.60029/rcesge
The Role of Servant Leadership and Knowledge Leadership in Big Data
Analysis: Perspectives of Military Ocers at the Peruvian Army War College.
Revista Cientíca de la Escuela
Superior de Guerra del Ejército
Volumen IV, Numero I, Mayo 2025
El papel del liderazgo de servicio y del liderazgo del conocimiento en el análisis de grandes datos: Perspectivas de oficiales
militares en la Escuela Superior de Guerra del Ejército del Perú.
1. Introducción
In defense, particularly in the Army, strategic decision-making must be
guided by data. According to Chen and Zhang, in 2023, adopting Big Data Analytics
(BDA) has become essential for improving operations and organizational
performance. However, effectively implementing BDA in military institutions
depends on several organizational and leadership factors (Rettore et al., 2024). In
this context, leadership fosters an organizational culture that values data-driven
decision-making (Kokkinou et al., 2024).
This study explores how Servant Leadership (SL) and Knowledge
Leadership (KL) inuence the adoption of Big Data Analytics (BDA) in military
institutions. It determines how these leadership styles support the integration of
analytical tools and whether Intellectual Capital (IC) acts as a mediator in this
relationship. The relevance of this research lies in the need to understand how
leadership capabilities can strengthen the digitalization and modernization of
military operations in a strategic defense context (Liwång et al., 2023). In various
organizations, BDA enhances planning, supply chain management, and data-driven
decision-making (Roßmann et al., 2017). Recent research from indexed databases
such as Web of Science and Scopus underscores that the successful
implementation of BDA in dynamic institutions depends not only on technological
advancements but also on leadership’s ability to cultivate an analytical culture (Bag
et al., 2021; Schmidt et al., 2023). In particular, Servant Leadership fosters team
autonomy and encourages the adoption of new technologies (Hamyeme et al.,
2024), while Knowledge Leadership facilitates the efficient transfer and application
of information in strategic decision-making processes (Mahdi, 2021).
Despite the growing interest in studying BDA across various settings,
signicant gaps in the global literature, particularly in the military domain, require
further exploration. First, the combined impact of Servant Leadership and
Knowledge Leadership on adopting BDA in the military sector has not been
analyzed, limiting the understanding of how these leadership styles can enhance
data-driven decision-making. Second, the role of Intellectual Capital as a potential
mediator in the relationship between these leadership styles and BDA adoption
remains unexplored, highlighting the need for studies that delve deeper into its
inuence within this strategic context. This study contributes to the literature by
addressing these gaps through an empirical analysis that evaluates the impact of
different leadership styles on BDA adoption in the military eld. Unlike previous
studies, this research examines the direct relationship between leadership and
BDA. It explores the mediating role of Intellectual Capital, providing a more
comprehensive model of how analytical capabilities develop within defense
institutions.
This research is grounded in two key theories: the Resource-Based View
(RBV) and the Dynamic Capabilities Theory (DCT). RBV emphasizes the importance
of resources, skills, competencies, and capabilities in generating sustainable
competitive advantage. Over time, this theory has evolved by incorporating
approaches such as the knowledge-based view and dynamic capabilities, which
have expanded its conceptual framework (Barney, 1991).
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Diego Noreña-Chávez
Correo electrónico: diego.norena1@gmail.com
Meanwhile, dynamic capabilities represent the organizational and strategic
processes that enable companies to adapt and evolve in dynamic environments.
These capabilities involve creating, reconguring, and renewing resources,
highlighting interaction and continuous learning within the organization as key
factors for achieving sustainable competitive advantage (Teece et al., 1997).
Figure 1 presents the proposed research model.
2. Literature Review and Hypothesis Development
2.1 Servant Leadership
Servant leadership is a leadership philosophy rooted in values,
emphasizing the well-being of others and advocating for social justice, as Eva et al.
(2019) highlighted. This leadership style is dened by its focus on empowering and
nurturing individuals, demonstrating humility, authenticity, interpersonal
acceptance, and stewardship while also providing clear guidance, according to Van
Dierendonck (2011). Servant leaders strongly emphasize fostering their followers'
growth and development, creating an environment where individuals can ourish,
as noted by Lee et al. (2019). Their role encompasses strategic and operational
responsibilities, which are reinforced by specic servant leadership traits and
competencies, as discussed by Coetzer et al. (2017). In comparison to other
leadership approaches, servant leadership plays a crucial role in strengthening
trust within groups, which enhances organizational commitment and employee
engagement, ultimately boosting overall work performance, as observed by Ling et
al. (2017).
2.2 The Relationship Between Servant Leadership and Big Data
Analytics
Big Data Analytics examines large and varied data sets to uncover hidden
patterns, unknown correlations, market trends, customer preferences, and other
Figure 1:
Proposed Model
80
ISSN: 2520 - 7628 (Impreso), 2789-2514 (En línea)
Volumen IV, Numero I, Mayo 2025
El papel del liderazgo de servicio y del liderazgo del conocimiento en el análisis de grandes datos: Perspectivas de oficiales
militares en la Escuela Superior de Guerra del Ejército del Perú.
helpful information (Hariri et al., 2019). Big data is characterized by its large volume,
high velocity of data generation, and variety of data types, including structured and
unstructured data (Lee, 2017). Servant leadership positively inuences Big Data
Analytics by empowering employees to embrace data-driven decision-making
(Kumar & Chauhan, 2024). Servant leadership positively inuences Big Data
Analytics by fostering a team-oriented environment; servant leaders encourage
open discussions about data insights. Servant leaders remove bureaucratic barriers,
allowing data scientists and analysts to experiment with new BDA techniques
(Oratis, 2022).
H1: Servant leadership has a positive inuence on Big Data Analytics.
2.3 Mediating Effect of Knowledge Leadership
Knowledge leadership involves creating a climate that supports learning
and innovation. Leaders act as role models and facilitate learning processes, crucial
for enhancing organizational capabilities and innovation performance (Viitala,
2004). Key characteristics of knowledge leadership include intellectual, open,
multi-dimensional, innovative, transformative, and strategic characteristics, which
play different roles in the knowledge management process (Qiong, 2010). Servant
leadership promotes a knowledge-sharing culture by building trust and
encouraging open communication among employees (Zaher, 2015). Servant
leadership positively inuences knowledge leadership through public service
motivation and corporate social responsibility (Tuan, 2016). Servant leadership
positively correlates with knowledge management, which has a signicant positive
relationship with cost-saving innovation (Bazyar et al., 2024). Moreover, servant
leadership positively inuences employee creativity and work role performance,
with knowledge sharing partially mediating this relationship (Zada et al., 2023).
Based on this literature review, the following hypothesis is developed:
H2: Servant leadership positively inuences knowledge leadership.
Leadership centered on knowledge is crucial in strengthening innovation
capabilities by fostering a culture that prioritizes data analytics maturity, as
Kadarsah et al. (2023) emphasized. Leadership focusing on knowledge management
can facilitate integrating and effectively using data analytics, resulting in improved
decision-making and a more decisive competitive edge, as Ferraris et al. (2019)
noted. Knowledge Leadership is essential for building key BDA competencies,
including data analytical skills and problem-solving abilities, which are critical for
enhancing security, privacy, and innovation, ultimately leading to improved
organizational performance, as Koohang et al. (2023) highlighted. Based on this
literature review, the following hypothesis is proposed:
H3: Knowledge leadership positively inuences big data analytics.
H4: Knowledge leadership mediates the relationship between servant
leadership and big data analytics
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2.3 Mediating Effect of Intellectual Capital
Intellectual capital refers to an organization's intangible assets and
resources that contribute to its value and competitive advantage, encompassing the
workforce's knowledge, skills, competencies, and other intangible organizational
factors (Singhania et al., 2025). Intellectual capital components include human
capital, structural capital, relational capital, and innovation capital (Kweh et al.,
2024). Human capital includes employees' knowledge, skills, experience, and
competencies, often considered the most crucial component as it directly
inuences innovation and organizational performance (Pigola et al., 2021).
Structural capital refers to the supportive infrastructure, processes, databases,
organizational culture, and intellectual property that enable human capital to
function effectively, including business models and organizational routines (Castro
& Sáez, 2008). Relational capital encompasses the relationships and networks a
company maintains with external stakeholders, such as customers, suppliers, and
partners, playing a vital role in maintaining competitive advantage and fostering
innovation (Ali et al., 2021). Servant leadership positively inuences intellectual
capital by fostering an environment that enhances employees' innovative
capabilities and organizational performance (Alasmari et al., 2025). Servant
leadership, particularly in academic settings, enhances the intrapreneurial ability
of working professionals by boosting their self-efficacy, which augments the
organization's intellectual capital through continuous innovation and effective
change management (Khatri et al., 2023). Based on this literature review, the
following hypothesis is developed:
H5: Servant leadership positively inuences Intellectual capital
Intellectual capital (IC) signicantly enhances big data analytics (BDA) by
providing the necessary intangible resources and capabilities that drive effective
data utilization and innovation (Alkhatib & Valeri, 2024). Intellectual capital,
comprising human, structural, and relational capital, positively correlates with
developing big data analytical capabilities. These capabilities, in turn, improve
internal integration and operational performance within organizations (Chen &
Chen, 2021). The human capital within a business equips employees to evaluate and
interpret big data insights prociently, resulting in enhanced decision-making
(Ferraris et al., 2019). Based on this literature review, the following hypothesis is
developed:
H6: Intellectual capital positively inuences big data analytics
3. Methodology
The research followed a post-positivist epistemological paradigm
grounded in a critical realism ontology, which assumes that reality exists
independently of human perception but can only be understood imperfectly and
probabilistically through empirical observation (Guba & Lincoln, 1994). The study
adopts an objectivist axiology, acknowledging that while values may inuence
research, their impact is mitigated through systematic methodological rigor,
triangulation, and falsiability (Creswell & Creswell, 2018). The research employed
a quantitative approach, utilizing Partial Least Squares Structural Equation
Diego Noreña-Chávez
Correo electrónico: diego.norena1@gmail.com
ISSN: 2520 - 7628 (Impreso), 2789-2514 (En línea)
82
Volumen IV, Numero I, Mayo 2025
El papel del liderazgo de servicio y del liderazgo del conocimiento en el análisis de grandes datos: Perspectivas de oficiales
militares en la Escuela Superior de Guerra del Ejército del Perú.
Modeling (PLS-SEM) as the primary method for data analysis, ensuring robust
statistical inference in the evaluation of complex relationships. A back-translation
process was conducted to enhance the validity of the measurement instruments,
following established guidelines to ensure linguistic and conceptual equivalence.
Additionally, all participants were informed about the research objectives and
consented before participating, aligning with ethical research practices (Blaikie,
2007). Furthermore, the praxeological approach is explanatory and applied, seeking
not only to identify underlying causal mechanisms but also to generate actionable
insights that inform decision-making and improve practices in the eld of study.
Big Data Analytics (BDA) was assessed using a four-item scale developed by
Lin et al. (2022). Intellectual Capital (IC) was measured based on three key
components: Human Capital, Relational Capital, and Structural Capital, following
the scale developed by Hsu and Fang (2009). Specically, Human Capital was
evaluated with four items, Structural Capital with seven items, and Relational
Capital with four items. Knowledge Leadership was measured using a ve-item
scale developed by Donate and Pablo (2015). This study employed well-established
and validated scales to ensure the reliability and validity of the constructs in the
research model
4. Results
The measurement model assessment demonstrates acceptable reliability
and validity across the examined constructs, including Big Data Analytics (BDA),
Human Capital (HC), Knowledge Leadership (KL), Relational Capital (RC), Structural
Capital (SC), and Servant Leadership (SL). The outer loadings indicate that most
items meet the recommended threshold (≥ 0.708) (Hair et al., 2022), with powerful
indicators in BDA3 (0.840), HC3 (0.971), KL2 (0.861), and SC7 (0.915). However, some
items exhibit weaker loadings, such as SL4 (0.589), SL5 (0.545), and RC2 (0.598),
which may require further review, as indicators with values below 0.70 can reduce
construct reliability and validity (Henseler, Ringle, & Sarstedt, 2015).
Regarding internal consistency, both Cronbach’s alpha and composite reliability
(rho_c) are above the acceptable threshold (≥ 0.7) (Henseler et al., 2015), conrming
construct reliability. However, Relational Capital (RC) and Servant Leadership (SL)
show relatively lower Cronbach’s alpha values (0.702 and 0.760, respectively),
suggesting that these constructs might benet from renement to improve their
reliability. The Average Variance Extracted (AVE) values conrm convergent
validity, as all constructs surpass the 0.50 threshold (Fornell & Larcker, 1981), except
for Servant Leadership (0.500), which is borderline acceptable. According to Fornell
and Larcker (1981), an AVE above 0.50 indicates that the construct explains more
than half of the variance in its indicators, strengthening convergent validity. These
ndings suggest that while the measurement model is generally robust. Table 1
provides an overview of the model’s reliability and validity assessments.
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The Fornell and Larcker criterion (1981) is a widely used method for
assessing discriminant validity in Partial Least Squares Structural Equation
Modeling (PLS-SEM) by comparing the square root of the Average Variance
Extracted (AVE) with construct correlations. Table 1 presents the discriminant
validity analysis for six constructs: Big Data Analytics (BDA), Human Capital (HC),
Knowledge Leadership (KL), Relational Capital (RC), Structural Capital (SC), and
Servant Leadership (SL). The diagonal values represent the square root of the AVE
for each construct, while the off-diagonal values denote the correlations between
constructs. According to the Fornell and Larcker criterion, discriminant validity is
established if the square root of the AVE for each construct (diagonal values) is
greater than its highest correlation with any other construct. In this analysis, BDA
(0.781) exhibits stronger self-association compared to its correlations with HC
(0.005), KL (0.601), RC (-0.098), SC (-0.144), and SL (0.564). Similarly, HC (0.799), KL
(0.766), RC (0.710), SC (0.842), and SL (0.705) all have square root AVE values
exceeding their respective inter-construct correlations, conrming the
discriminant validity of the model. This ensures that each construct is empirically
Table 1:
Reliability and validity
Construct
Item
Outer Cronbach's
Composite reliability
Average
variance
extracted
(AVE)
loadings
alpha
(rho_c)
Big Data Analytics
BDA2
0.735
0.700 0.824 0.610
BDA3
0.840
BDA4
0.763
Human Capital
HC1
0.644
0.794 0.837 0.639
HC2
0.748
HC3
0.971
Knowledge
Leadership
KL1
0.754
0.828 0.876 0.587
KL2
0.861
KL3
0.822
KL4
0.702
KL5
0.674
Relational Capital
RC1
0.659
0.702 0.799 0.504
RC2
0.598
RC3
0.669
RC4
0.882
Structural Capital
SC5
0.837
0.820 0.879 0.708
SC6
0.767
SC7
0.915
Servant leadership
SL1
0.743
0.760 0.828 0.500
SL2
0.817
SL3
0.786
SL4
0.589
SL5
0.545
Diego Noreña-Chávez
Correo electrónico: diego.norena1@gmail.com
ISSN: 2520 - 7628 (Impreso), 2789-2514 (En línea)
84
Volumen IV, Numero I, Mayo 2025
El papel del liderazgo de servicio y del liderazgo del conocimiento en el análisis de grandes datos: Perspectivas de oficiales
militares en la Escuela Superior de Guerra del Ejército del Perú.
distinct and measures a unique concept within the research framework. Therefore,
the model meets the necessary validity conditions for further hypothesis testing.
Table 2 shows the Fornell-Larcker criterion.
The Heterotrait-Monotrait (HTMT) Ratio of Correlations is a statistical
method used to assess discriminant validity in Partial Least Squares Structural
Equation Modeling (PLS-SEM), as recommended by Henseler et al. (2015). Table 3
presents the HTMT values for six constructs: Big Data Analytics (BDA), Human
Capital (HC), Knowledge Leadership (KL), Relational Capital (RC), Structural Capital
(SC), and Servant Leadership (SL). The HTMT criterion suggests that discriminant
validity is achieved when the HTMT values are below 0.90 (or a more conservative
threshold of 0.85) for conceptually distinct constructs. In this table, all HTMT
values fall below the 0.90 threshold, indicating sufficient discriminant validity
among the constructs. The highest HTMT value is 0.868 (SC and RC), which remains
within the acceptable range. Lower HTMT values, such as 0.052 (BDA and HC) and
0.08 (HC and SL), further conrm that the constructs are not highly correlated,
reinforcing their distinctiveness. Since none of the construct pairs exceed the
established HTMT thresholds, the measurement model demonstrates strong
discriminant validity, ensuring that each construct captures a unique theoretical
concept. Table 3 shows the HTMT criterion.
Table 2:
Fornell-Larcker criterion
BDA
HC
KL
RC
SC
SL
BDA
0.781
HC
0.005
0.799
KL
0.601
0.097
0.766
RC
-0.098
0.311
-0.124
0.71
SC
-0.144
0.321
-0.171
0.619
0.842
SL
0.564
0.05
0.474
-0.058
0.015
0.705
BDA
HC
KL
RC
SC
SL
BDA
HC
0.052
KL
0.743
0.135
RC
0.123
0.431
0.182
SC
0.157
0.455
0.214
0.868
SL
0.671
0.08
0.548
0.136
0.093
Table 3:
HTMT criterion
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Volumen IV, Numero I, Mayo 2025
Next, the structural model is assessed to substantiate the proposed
relationships. The structural model reects the paths hypothesized in the research
framework. Higher-order construct validation (IC) was conducted, where IC is
formed from human capital (HC), relational capital (RC), and structural capital (SC).
The Variance Ination Factor (VIF) was used to check for multicollinearity.
According to Hair et al. (2021), VIF values below 5 indicate no multicollinearity.
Since all VIF values were less than 5 (Table 4), collinearity did not threaten this
investigation. Finally, outer weights' statistical signicance and relevance were
analyzed (Sarstedt et al., 2019). The results indicate that outer weights mattered,
and each IC indicator had strong outer loadings, further validating IC (Sarstedt et
al., 2019). Table 5 presents the results for the higher-order constructs. Table 4
presents the higher-order construct for intellectual capital.
The results of this study provide a comprehensive understanding of the
relationships among Servant Leadership (SL), Knowledge Leadership (KL), Big Data
Analytics (BDA), and Intellectual Capital (IC) by testing seven hypotheses. The
ndings strongly support H1 (SL BDA), indicating that SL has a signicant and
positive impact on BDA (β = 0.335, T = 5.042, p = 0.000), suggesting that organizations
with strong servant leadership are more likely to foster data-driven
decision-making and analytics adoption. Similarly, H2 (SL KL) is supported (β =
0.445, T = 5.388, p = 0.000), reinforcing that SL is crucial in enhancing knowledge
leadership. Additionally, H3 (KL BDA) shows a strong and positive effect (β = 0.442,
T = 6.494, p = 0.000), conrming that knowledge-driven leadership signicantly
contributes to implementing and utilizing BDA. However, the results do not support
H5 (SL IC), as the relationship was non-signicant (β = -0.020, T = 0.178, p = 0.430),
suggesting that SL does not directly inuence IC within this model. Furthermore,
H6 (IC BDA) is also non-signicant (β = -0.075, T = 1.272, p = 0.102), indicating that
IC does not play a direct role in the adoption or effectiveness of BDA. These
ndings suggest that while SL and KL are essential drivers of BDA adoption, IC does
not exhibit a signicant direct inuence on BDA, which may imply that its impact is
indirect or mediated by other organizational factors. This highlights the need for
further research to explore potential moderating or mediating variables that could
clarify the role of IC in data-driven organizations. From a managerial perspective,
companies seeking to enhance their BDA capabilities should focus on
strengthening Servant Leadership and Knowledge Leadership, as they have been
empirically validated as key enablers of analytics adoption. Meanwhile, the
non-signicant role of IC suggests that its contribution to BDA may be more
context-dependent, requiring a more nuanced understanding of its interplay with
Table 4:
Higher-order construct for intellectual capital
VIF
Outer Weights
T
statistics
P-
values
Outer
loadings
P-
values
HC->IC
1.219
0.069
0.189
0.425
0.468
0.050
RC->IC
1.515
0.464
1.610
0.050
0.846
0.001
SC->IC
1.674
0.624
2.100
0.018
0.922
0.000
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militares en la Escuela Superior de Guerra del Ejército del Perú.
other strategic variables. Table 5 presents the hypotheses results.
The mediation analysis reveals that Knowledge Leadership (KL) acts as a partial
mediator in the relationship between Servant Leadership (SL) and Big Data
Analytics (BDA), as indicated by the signicant indirect effect (β = 0.197, T = 4.549, p
= 0.000). This suggests that SL enhances KL, which facilitates the adoption and
utilization of BDA, though SL still directly inuences BDA. Therefore, organizations
aiming to strengthen their BDA capabilities should focus on developing knowledge
leadership practices alongside servant leadership. In contrast, Intellectual Capital
(IC) does not mediate the relationship between SL and BDA, as evidenced by the
non-signicant indirect effect (β = 0.001, T = 0.148, p = 0.441), reinforcing the
ndings from the previous table where neither SL IC nor IC BDA showed
statistical signicance. This indicates that IC does not serve as a conduct between
SL and BDA, suggesting that its role in data-driven decision-making may be indirect
or dependent on additional moderating variables. Table 6 presents the mediation
analysis results.
Table 5:
Hypotheses results
Original
sample (O)
Standard deviation
(STDEV)
T statistics
P values
H1:SL -> BDA
0.335
0.066
5.042
0.000
H2: SL -> KL
0.445
0.083
5.388
0.000
H3:KL -> BDA
0.442
0.068
6.494
0.000
H5:SL -> IC
-0.020
0.112
0.178
0.430
H6:IC -> BDA
-0.075
0.059
1.272
0.102
Table 5:
Hypotheses results
Original
sample (O)
Standard deviation
(STDEV)
T statistics
P values
H4: SL -> KL -> BDA
0.197
0.043
4.549
0.000
H7: SL -> IC -> BDA
0.001
0.010
0.148
0.441
5. Discussion
The ndings strongly support H1, indicating that Servant Leadership (SL)
has a signicant and positive impact on Big Data Analytics (BDA) (β = 0.335, T =
5.042, p = 0.000). These results align with existing literature, highlighting that
servant leaders empower employees, foster a collaborative environment, and
remove bureaucratic barriers, all enhancing data-driven decision-making and
analytics adoption (Kumar & Chauhan, 2024; Oratis, 2022). By focusing on employee
development, ethical leadership, and open communication, servant leaders create
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an organizational culture that encourages experimentation with new BDA
techniques (Eva et al., 2019; Van Dierendonck, 2011). This reinforces the notion that
leadership plays a fundamental role in driving data analytics capabilities in
organizations.
The results conrm the mediating role of Knowledge Leadership (KL) in the
relationship between SL and BDA, supporting H2 (SL KL) and H3 (KL BDA). The
mediation effect is statistically signicant (β = 0.197, T = 4.549, p = 0.000),
demonstrating that KL enhances BDA adoption by fostering a knowledge-sharing
culture, data literacy, and analytics capabilities (Kadarsah et al., 2023; Viitala, 2004).
This nding reinforces previous research showing that servant leadership
promotes knowledge-sharing behaviors and creates a collaborative learning
environment that facilitates analytics-driven innovation (Tuan, 2016; Zaher, 2015).
The strategic role of knowledge leadership in BDA adoption suggests that
organizations should invest in KM systems, analytics training, and cross-functional
collaboration initiatives to maximize their data-driven decision-making
capabilities.
Contrary to expectations, H5 (SL IC) was not supported (β = -0.020, T =
0.178, p = 0.430), and H6 (IC BDA) was also non-signicant (β = -0.075, T = 1.272, p
= 0.102). These ndings suggest that Intellectual Capital (IC) does not play a direct
role in adopting BDA within this model. Previous studies emphasized that IC,
particularly human and structural capital, is a key driver of innovation and
technological capabilities (Ferraris et al., 2019; Chen & Chen, 2021). However, the
lack of signicance in this study implies that IC’s impact on BDA might be
contingent on other moderating variables, such as organizational culture,
technological readiness, or leadership inuence.
The mediation analysis for H7 (SL IC BDA) found no signicant indirect
effect (β = 0.001, T = 0.148, p = 0.441), conrming that Intellectual Capital does not
mediate the relationship between SL and BDA. This suggests that IC is not a primary
conduit through which SL enhances BDA capabilities. A potential explanation for
this result is that while IC provides essential intangible assets, its role in data
analytics adoption is likely indirect, requiring strong leadership and strategic
alignment. For instance, while human capital equips employees with analytical
skills, it may not automatically translate into improved BDA adoption without
adequate leadership and a supportive knowledge-sharing environment.
6. Theoretical Implications
This study contributes to the theory of leadership and organizational
analytics by demonstrating the strategic role of Servant Leadership (SL) and
Knowledge Leadership (KL) in enhancing Big Data Analytics (BDA) adoption. The
ndings reinforce that leadership is crucial to data-driven decision-making in
military and defense organizations.This study aligns with contemporary theories
emphasizing ethical leadership, team empowerment, and data-driven strategies in
complex environments.The Peruvian Army, as a knowledge-driven organization,
can leverage SL and KL to integrate intelligence, logistics, and operational data into
strategic decision-making.
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militares en la Escuela Superior de Guerra del Ejército del Perú.
The conrmed mediation effect of Knowledge Leadership (KL) in the SL BDA
relationship highlights the importance of knowledge-sharing mechanisms within
military institutions.This supports the argument that knowledge-driven leadership
models should be institutionalized to improve intelligence gathering, mission
planning, and operational efficiency.The study challenges traditional views on
Intellectual Capital (IC) as a direct enabler of BDA, suggesting that its impact may
depend on additional factors such as strategic alliances, digital infrastructure, and
leadership frameworks. This calls for future research on how military institutions
can maximize their intellectual assets to improve defense analytics and strategic
capabilities.
7. Practical Implications
The ndings provide actionable insights on how leadership can enhance
military intelligence, strategic planning, and operational efficiency through BDA
adoption for military institutions such as the Peruvian Army and Escuela Superior
de Guerra.Military leaders should embrace Servant Leadership principles to create
an adaptive, intelligence-driven command structure that promotes teamwork and
strategic thinking.Training programs should emphasize data literacy,
analytics-based leadership, and operational intelligence applications.Given the
conrmed role of Knowledge Leadership (KL) in BDA adoption, the Peruvian Army
should integrate knowledge-sharing frameworks to facilitate: Real-time intelligence
exchange across units.Data-driven mission planning and logistics optimization.
Integration of military and geopolitical analytics in high-stakes decision-making.
Military academies and war colleges should adopt a knowledge-oriented leadership
model, ensuring officers are trained to manage, interpret, and apply complex data in
military scenarios. The non-signicant impact of Intellectual Capital (IC) on BDA
suggests that military organizations should not rely solely on human and structural
capital for digital transformation. Instead, they should strengthen leadership
frameworks and technological infrastructure to create a comprehensive defense
analytics ecosystem. The ndings indicate the need for enhanced collaboration
between military institutions, academia, and the private sector to develop advanced
analytics capabilities.
8. Conclusion
This study provides critical insights into how leadership inuences the
integration of Big Data Analytics (BDA) in military and defense institutions. The ndings
conrm that Servant Leadership (SL) directly enhances BDA adoption, as leaders who
empower teams, reduce bureaucracy, and promote ethical decision-making are more
effective in integrating data-driven intelligence frameworks. Knowledge Leadership
(KL) also plays a key mediating role, demonstrating that leadership models emphasizing
knowledge-sharing and collaborative intelligence signicantly enhance military
analytics capabilities. In contrast, Intellectual Capital (IC) does not directly inuence
BDA adoption, suggesting that its impact depends on additional strategic enablers such
as leadership commitment, digital transformation, and geopolitical intelligence
structures. These results highlight the crucial role of leadership in shaping military
analytics capabilities and underscore the need for leadership-driven strategies to
maximize the effectiveness of BDA in defense operations.
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Acknowledgments
-To the certied translator María Luz Salas Roca for her valuable contribution in
improving the writing, rening the style, and reformulating the ideas into English.
-I sincerely thank the Peruvian Army and the Superior War College for allowing me to
collect data in 2024, and General Valverde for his support.
Conict of Interest
There are no conicts of interest.
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