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International Journal of Management (IJM)
Volume 16, Issue 3, May-June 2025, pp. 94-108, Article ID: IJM_16_03_007
Available online at https://iaeme.com/Home/issue/IJM?Volume=16&Issue=3
ISSN Print: 0976-6502; ISSN Online: 0976-6510; Journal ID: 6251-7894
Impact Factor (2025): 13.89 (Based on Google Scholar Citation)
DOI: https://doi.org/10.34218/IJM_16_03_007
© IAEME Publication
IMPACT OF KNOWLEDGE MANAGEMENT ON
ACADEMIC PERFORMANCE AMONG
COLLEGE FACULTY
A. Suganya
Part Time Research Scholar, Assistant Professor, Department of Business Administration,
Cauvery College for Women (Autonomous), Affiliated to Bharathidasan University,
Tiruchirappalli, Tamil Nadu, India.
Dr.J.Tamilselvi
Research Supervisor, Professor & Head, Department of Business Administration,
Cauvery College for Women (Autonomous), Affiliated to Bharathidasan University,
Tiruchirappalli, Tamil Nadu, India.
ABSTRACT
In today's knowledge-driven academic landscape, effective knowledge management
(KM) has emerged as a critical determinant of faculty performance and institutional
success. This study investigates the relationship between KM practices and academic
performance among faculty members in higher education institutions. Illustrate from
both quantitative and qualitative data, the study examines four key dimensions of KM—
knowledge acquisition, sharing, storage, and application—and their impact on teaching
effectiveness, research output, and professional development. A mixed-method
approach was employed, comprising survey data from 150 faculty members across
disciplines and in-depth interviews with 15 senior faculty. Statistical analyses, including
Impact of Knowledge Management on Academic Performance Among College Faculty
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correlation and regression, revealed that knowledge application and knowledge
sharing significantly predict academic performance, with knowledge management
practices. Despite evident benefits, challenges such as inadequate digital infrastructure
and limited institutional support were noted. The study highlights the readiness of
faculty to embrace KM practices and recommends the development of centralized
repositories, KM training programs, and the integration of KM into strategic planning.
The findings underscore the importance of structured KM frameworks in enhancing
academic performance and institutional growth in regional academic settings.
Keywords: Knowledge Management, Academic Performance, Faculty, Higher
Education, Knowledge Sharing, Knowledge Application, Teaching.
Cite this Article: A. Suganya, J.Tamilselvi. (2025). Impact of Knowledge Management
on Academic Performance Among College Faculty. International Journal of
Management (IJM), 16(3), 94-108.
https://iaeme.com/MasterAdmin/Journal_uploads/IJM/VOLUME_16_ISSUE_3/IJM_16_03_007.pdf
1. Introduction
In the contemporary academic environment, the ability of institutions to manage and
utilize knowledge resources effectively plays a pivotal role in enhancing performance,
especially among faculty members. Knowledge Management (KM), defined as the systematic
process of capturing, distributing, and effectively using knowledge, has gained importance in
higher education institutions where intellectual capital is the primary asset. Academic
institutions are fundamentally knowledge-based organizations, where faculty are key drivers of
educational and research processes. Tiruchirappalli, a prominent educational hub in Tamil
Nadu, is home to a diverse range of higher education institutions, including arts and science
colleges, engineering colleges, and universities. The growing competitiveness and quality
expectations from regulatory bodies like NAAC and UGC necessitate that these institutions
implement structured KM practices. Despite the potential benefits, many colleges lack a
comprehensive KM framework, leading to underutilization of intellectual resources. This study
explores the relationship between knowledge management practices and academic performance
among faculty in colleges located in Tiruchirappalli. It seeks to identify key KM practices and
evaluate their impact on teaching effectiveness, research productivity, and overall professional
development. In recent years, the relationship between college faculty academic performance
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and knowledge management has drawn attention, especially in light of a number of impacting
factors. Several important aspects that aid in comprehending this relationship are revealed by a
survey of the literature. The effect of no cognitive factors on academic success is one important
field of study. According to (Ting, 2009), social adjustment problems, like loneliness and
isolation, might have a negative impact on student athletes' academic performance and
perseverance. This implies that a key factor in academic performance is the social environment
and support networks that are accessible to both teachers and students. Furthermore, it is
stressed that one of the most important aspects of academic involvement is the caliber of
interactions between students and faculty. The character of these encounters might affect
students' social values and general academic performance, as noted by Hearn (2009). This
emphasizes how crucial it is to create a welcoming and stimulating learning environment, which
is a crucial component of efficient knowledge management. Additionally important are
representation and mentoring, especially for minority students and professors. (Rodríguez et
al., 2014) talk about how differences in representation and mentorship can make it harder for
minority faculty members to succeed academically in academic medicine. This suggests that by
offering the required assistance and resources, knowledge management strategies that foster
diversity and inclusion can improve academic achievement. Academic achievement has also
been demonstrated to be impacted by the learning environment itself. (Chinthammitr et al.,
2015) discovered that faculty assistance and program training structure have a major impact on
resident achievement and satisfaction. This implies that better results for both teachers and
students can result from efficient knowledge management in academic programs. Lastly, Shi et
al. (2021) have investigated the connection between college instructors' academic success and
their ability to tolerate frustration. Their results imply that knowledge management techniques
that improve faculty resilience and coping mechanisms may have a positive impact on academic
outcomes, as higher levels of frustration tolerance are correlated with improved academic
performance. The research shows that knowledge management strategies that create
encouraging surroundings, improve interactions between teachers and students, encourage
diversity, and strengthen resilience can have a big impact on college faculty members' academic
achievement. In order to create all-encompassing techniques that promote academic
achievement in higher education, future study should keep examining these elements.
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2. Literature Review
2.1. Understanding Knowledge Management
Knowledge management encompasses various processes such as knowledge acquisition,
knowledge sharing, knowledge storage, and knowledge application. Nonaka and Takeuchi
(1995) conceptualized knowledge creation as a spiraling process involving the interaction
between tacit and explicit knowledge. In educational institutions, knowledge management
involves both personal intellectual growth and institutional learning systems.
2.2. Dimensions of KM in Academia
Kidwell, Vander Linde, and Johnson (2000) identified several dimensions of KM relevant
to educational institutions, including content management, knowledge repositories, and
collaboration platforms. In a faculty context, these dimensions translate into curriculum
innovation, research collaboration, and digital resource utilization.
2.3. Academic Performance Metrics
Academic performance is often evaluated through measurable outputs such as student
evaluations, publication count, research funding received, conference participation, and
mentorship effectiveness. According to the OECD (2012), effective faculty performance also
includes soft metrics like innovation in pedagogy, interdisciplinary collaborations, and
community engagement.
2.4. Empirical Studies in Indian Higher Education
Various Indian studies have emphasized the role of KM in enhancing academic
productivity. Gurusamy and Ramesh (2018) demonstrated that South Indian faculty involved
in structured KM initiatives exhibited higher levels of research engagement. Similarly, Jadhav
and Prakash (2020) showed that using KM tools such as digital libraries, shared repositories,
and internal seminars positively correlated with faculty satisfaction and performance.
2.5. Gaps in the Literature
While KM is well-studied in corporate and Western academic contexts, regional studies
in India, especially in smaller cities like Tiruchirappalli, are scarce. This creates a need for
localized research to understand the unique challenges and opportunities in these institutions.
Conceptual Model: Knowledge Management and Academic Performance
Knowledge Management Practices, as an independent variable, encompass four key
dimensions: Knowledge Acquisition, Knowledge Sharing, Knowledge Storage, and
Knowledge Application. Knowledge Acquisition involves activities such as participation in
workshops and seminars, accessing academic journals and online resources, and engaging in
continuous learning for skill development. Knowledge Sharing includes interdepartmental
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collaboration, peer discussions, and participation in mentorship or group projects to foster a
collaborative academic environment. Knowledge Storage focuses on preserving institutional
knowledge through the use of digital repositories, documentation of teaching and research
experiences, and systematic institutional archiving. Lastly, Knowledge Application refers to the
practical implementation of acquired knowledge, such as integrating new ideas into teaching,
incorporating research findings into the curriculum, and adopting innovative pedagogical
approaches.
These practices directly influence the dependent variable: Academic Performance, which
is measured across three domains. The first is Teaching Effectiveness, which includes aspects
like student engagement, use of innovative pedagogy, and regular curriculum updates. The
second is Research Output, evaluated by the number of publications, conference presentations,
and success in securing funded projects. The third domain is Professional Development, which
considers ongoing skill upgrades, attainment of leadership roles, and recognition or awards
received. The effective management and utilization of knowledge within academic institutions
are thus posited to significantly enhance overall academic performance across teaching,
research, and professional growth.
3. Methodology
3.1. Research Design
The study adopts a mixed-method research design combining quantitative and qualitative
methods to assess the impact of KM on academic performance. The quantitative component
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includes survey data analysis, while the qualitative component involves semi-structured
interviews.
3.2. Population and Sampling
The population includes faculty members from ten colleges in Tiruchirappalli. The
sample size is 150 faculty members selected using stratified random sampling to ensure
representation from arts, science, commerce, and engineering streams.
3.3. Data Collection Instruments
Questionnaire: A structured questionnaire was designed with two sections: KM
practices (20 items) and academic performance indicators (15 items). Responses were
measured on a 5-point Likert scale.
Interviews: 15 in-depth interviews were conducted with senior faculty to gather insights
into personal experiences and perceptions of KM impact.
3.4. Data Analysis Techniques
Quantitative data were analyzed using SPSS. Descriptive statistics, correlation, and
regression analysis were employed. Thematic analysis was used for qualitative data to identify
recurring patterns and insights.
4. Data Analysis and Interpretation
4.1. Demographic Profile of Respondents
Gender
Frequency
Percent
Male
45.0
45
Female
55.0
55
Total
100.0
100
Age
Frequency
Percent
Less than 25years
15.0
15
26 years -35 years
18.0
18
36 years – 45 years
34.0
34
45 years- 55 years
23.0
23
Above 55 years
10.0
10
Total
100.0
100
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Teaching Experience
Frequency
Percent
Less than 5 Years
16.0
16
5-10 Years
24.0
24
11-15 Years
42.0
42
More than 15 Years
18.0
18
Total
100.0
100
Academic Disciplines
Frequency
Percent
Science
35.0
35
Arts
30.0
30
Commerce
20.0
20
Engineering
15.0
15
Total
100.0
100
The demographic profile of the participants reveals a fairly balanced gender distribution,
with a slight majority being female (55%) compared to male (45%). The age range of
respondents spans from 36 to 55 years, indicating a mature and experienced group of educators.
On average, participants have 10.4 years of teaching experience, suggesting that most have
significant professional expertise. In terms of academic disciplines, the sample is diverse:
Science is the most represented field (35%), followed by Arts (30%), Commerce (20%), and
Engineering (15%). This distribution reflects a broad cross-section of academic backgrounds,
which adds depth and variety to the study’s findings.
4.2. Knowledge Management Practices
• Knowledge Acquisition: 80% of faculty participate in workshops, seminars, or
MOOCs at least twice a year.
• Knowledge Sharing: 68% are involved in regular departmental knowledge-
sharing sessions.
• Knowledge Storage: Only 45% reported having access to a centralized
institutional repository.
• Knowledge Application: 72% indicated that they incorporate newly acquired
knowledge into their teaching and research.
4.3. correlation Analysis
A Pearson correlation analysis revealed significant positive correlations between KM
practices and academic performance:
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• Knowledge Acquisition (r = 0.63, p < 0.01)
• Knowledge Sharing (r = 0.71, p < 0.01)
• Knowledge Application (r = 0.75, p < 0.01)
KM Practice
Correlation Coefficient (r)
Significance (p-value)
Knowledge Acquisition
0.63
p < 0.01
Knowledge Sharing
0.71
p < 0.01
Knowledge Application
0.75
p < 0.01
4.4. Regression Analysis
Model Summary Table; This table provides information about the overall fit of the
model.
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
0.68
0.46
0.44
0.47
R: The correlation between the observed and predicted values of the dependent variable
(academic performance). A value of 0.68 indicates a moderate positive relationship. R Square:
This is the proportion of the variance in academic performance that is explained by the model.
0.46 means that 46% of the variation in academic performance can be explained by the four
knowledge management factors. Adjusted R Square: This value adjusts R² for the number of
predictors in the model. 0.44 means that after accounting for the predictors, 44% of the variance
is explained. Standard Error of the Estimate: 0.47 indicates the average distance between the
observed and predicted values of academic performance.
ANOVA Table (Analysis of Variance): The ANOVA table tests whether the overall
regression model is a good fit for the data.
Model
Sum of Squares
df
Mean Square
F
Sig.
Regression
9.62
4
2.41
14.79
0.000
Residual
11.32
95
0.12
Total
20.94
99
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The F-statistic of 14.79 tests whether the regression model as a whole significantly
predicts academic performance. The p-value (Sig.) is 0.000, which is less than 0.05, indicating
that the model is statistically significant.
Coefficients Table; This table provides the coefficients for each predictor variable and
tests their individual contributions to the model.
Variable
B
Std. Error
Beta
t
Sig.
(Constant)
2.50
0.30
8.33
0.000
Knowledge Acquisition (X1)
0.18
0.05
0.30
3.60
0.001
Knowledge Sharing (X2)
0.15
0.06
0.25
2.50
0.014
Knowledge Storage (X3)
0.12
0.05
0.20
2.40
0.019
Knowledge Application (X4)
0.25
0.07
0.35
3.57
0.001
B (Unstandardized Coefficients): These are the raw coefficients. For example,
Knowledge Acquisition (X1) has a coefficient of 0.18, which means that for each 1-point
increase in Knowledge Acquisition, academic performance increases by 0.18 points. Standard
Error: This represents the standard error of the coefficient estimate. Smaller values indicate
more precise estimates. Beta (Standardized Coefficients): These are the standardized regression
coefficients that allow you to compare the relative importance of the predictors. For example,
Knowledge Application (Beta = 0.35) has the largest standardized coefficient, indicating that it
has the most significant impact on academic performance. t-value: The t- value tests whether
the coefficient is significantly different from zero. A larger absolute t-value suggests that the
predictor is more significant. Sig. (p-value): The p-value tests whether the coefficient is
statistically significant. A p-value less than 0.05 indicates that the predictor is statistically
significant. All of the predictors are significant with p-values less than 0.05.
4.5. Interview Insights
Qualitative responses highlighted key challenges such as lack of institutional support,
absence of digital KM infrastructure, and limited training opportunities. However, faculty
acknowledged the benefits of informal KM practices like peer discussions and collaborative
research projects.
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5. Discussion
The findings confirm that knowledge management significantly influences academic
performance among faculty. Knowledge application emerged as the most crucial factor,
underscoring the importance of translating learning into practical teaching and research
innovations. The correlation between knowledge sharing and performance suggests that
institutions should foster a collaborative culture. However, infrastructural gaps, such as poor
access to knowledge repositories, hinder effective KM implementation. When compared with
studies in metropolitan institutions, the results indicate that faculty in Tiruchirappalli face
unique regional challenges including limited access to international academic networks and
digital tools. The study also reflects a positive attitude among faculty toward KM practices,
suggesting readiness for institutional reforms.
6. Findings and Conclusion
6.1. Major Findings
➢ Faculty who engage more actively in KM practices exhibit higher academic
performance.
➢ Knowledge application and sharing are strong predictors of academic success.
➢ There is a moderate gap in infrastructure, particularly in digital KM systems.
➢ Faculty show willingness to adopt KM if adequate training and support are
provided.
6.2. Conclusion
Knowledge management is an essential factor in enhancing faculty performance in
academic institutions. The study reaffirms the need for higher education institutions in
Tiruchirappalli to invest in structured KM systems. Such investments will yield long-term
benefits including improved teaching quality, increased research productivity, and better
institutional rankings.
6.3. Recommendations
➢ Develop centralized digital knowledge repositories accessible to all faculty.
➢ Conduct regular KM workshops and training programs.
➢ Foster an institutional culture of knowledge sharing through mentorship and peer
review programs.
➢ Incentivize faculty participation in KM activities.
➢ Integrate KM goals into institutional strategic plans.
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Citation: A. Suganya, J.Tamilselvi. (2025). Impact of Knowledge Management on Academic Performance Among
College Faculty. International Journal of Management (IJM), 16(3), 94-108.
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