Influence of Knowledge Management Processes on Organizational Performance in Service Sector: An Empirical Study

International Journal of Business and Management Invention
ISSN (Online): 2319 – 8028, ISSN (Print): 2319 – 801X
www.ijbmi.org || Volume 5 Issue 11 || November. 2016 || PP—28-35
www.ijbmi.org 28 | Page
Influence of Knowledge Management Processes on
Organizational Performance in Service Sector:
An Empirical Study
Ms. Ayesha Ahmad Iqbal Shaikh1
, Dr U Syed Aktharsha2
1, 2
(Jamal Institute of Management, Jamal Mohamed College, Tiruchirapalli, Tamil Nadu, 620020, India)
ABSTRACT: This research attempts to establish a link between knowledge management processes and
organizational performance in knowledge intensive service sectors. The key dimensions of knowledge
management processes have been identified which could influence the organizational performance. Metric has
been developed for the empirical investigation of the relationships between these research constructs. Structural
Equation Modelling (SEM) using partial least square techniques has been used to test these hypotheses with a
sample size of 491 knowledge workers (330 - Higher educational institutions and 161 from the IT companies) to
investigate the empirical relationships between the factors. All the four hypotheses were supported. The testing
of the hypotheses justified the identification of the key dimensions of KM as the critical success factors in terms
of the organizational performance. Implications of the study would enable the strategic planning managers to
make their knowledge management processes more effective so as to enhance the organizational performance.
Keywords: Knowledge Management Processes, Organizational Performance, IT Companies, Higher
Education, Structural Equation Modelling
I. INTRODUCTION
Service sectors in India contribute to a great deal to the national GDP in general, and the knowledge
intensive service sectors play a major role, which include education sector, IT sector and health maintenance
sector. Globalization has imposed a strict adherence to the international quality standards and Strategic Human
Resource Management is now acting as an enabler to the Knowledge Management (KM) which is considered to
be the driver of organizational performance.
In the context of knowledge intensive service organizations, it is believed that Knowledge Management
Processes (KMP) have a major role to play in the Organizational Performance (ORP). Many researchers have
undertaken qualitative studies on the direct impact of knowledge management contributing to the ORP ([1]-[4]),
but not many have considered empirical study of the influence of KMP on ORP. Knowledge intensive service
organizations in their group include: educational institutes, hospitals, software industries, legal consultants,
banks, marketing services, consultancy services etc. The major resources in such organizations will be
intellectual capital, or more specifically, the knowledge residing in the minds of the employees of such
organizations. While Knowledge Management (KM) is the enabler for tapping such knowledge which is stored
in the minds of the people in the organization and making it available for everyone to use for the organizational
growth, the influence of KMP on the ORP has not been studied in depth to the extent required. This study is
important because a lot of resources have been invested on the development of KMP so there is a need for its
worth justification and it can be only through the assessment of its contribution to the ORP. It is under this
backdrop of the importance gained by the knowledge as a „strategic asset‟ in the knowledge intensive service
organizations, this research has been conducted to assess in quantitative terms the impact made by the KMP on
ORP.
II. LITERATURE REVIEW
2.1.Knowledge Management Processes (KMP)
Learning is gaining an importance more than ever before because the World economy has shifted
towards the concept of knowledge as a strategic asset [5]. Ahmed et al., [6] have observed that the most efficient
strategic planners are on the lookout for the most recent knowledge in their area of business research so that they
can surpass the expectations of their customer. Disappointments connected with past administration choices has
inspired managers to look for the most modern information which can be transformed into knowledge.
Intangible assets mainly in the form of intellectual capital play a key role in helping organizations
achieve higher level of organizational performance [3]. Knowledge has already taken over other forms of
resources such as land, labour, and capital [7]. While it comes to the measurement issue of KMP the following
dimensions become important in terms of the service sectors in general and the IT and higher Education sectors
in particular ([8]-[11]). Through the process of Analytical Hierarch Process (AHP) a set of dimensions have
been identified for this research, which are presented below as used in the questionnaire development (Table 1).

Influence of Knowledge Management Processes on Organizational Performance in Service Sector…
Table 1: The Dimension, Meaning, Literature Support, and the Sample Item - KMP
Dimension Author, Year Description Sample Item
1. Knowledge
Acquisition
[11]-[14] Knowledge acquisition is the process the
company uses for obtaining new
information and knowledge
Organization provides multiple
sources of information to enable the
faculty and the students to acquire
knowledge.
2. Knowledge
Distribution
[9],[15] It comprises the dissemination of acquired
knowledge between different individuals
or units within a company. This process is
principally accomplished through
informal interactions among the
employees of the company.
Organization has formal mechanisms
to guarantee the sharing of best
practices among different fields of
the activity.
3. Knowledge
Interpretation
[16]-[18] Processes required in order that
information is understood and assimilated
by employees in order to transform it into
a new common knowledge.
All organization members share the
same aim to which they feel
committed.
4. Organizational
Memory
[8],[19] It is both the tacit and explicit knowledge
stored in the database of the organization
and made available for the future use.
The organization has databases to
stock its experiences and knowledge
so as to be able to use them later on.
2.2.Organizational Performance (ORP)
Measurement of ORP has now been dominating the management research in terms of gaining of the
competitive advantage in the market and it has become one of the most popular terms in today‟s management
terminology. The idea of managing organizational performance has emerged from the concept of enhancing the
efficiencies of the process which are important for business success particularly in the knowledge intensive
service sectors.
Performance is about doing the work as well as monitoring the results achieved. It is thus the outcome
of work because they provide the strongest linkage to the strategic goals of an organization, customer
satisfaction and economic contributions. The term “Performance Management and Measurement” refers to any
integrated, systematic approach to improving organizational performance to achieve strategic aims and promote
an organization‟s mission and values. In that sense organizational performance management is quite different
than individual performance management which specifically targets the personal performance of an employee
although the latter comprises an essential part of the overall organizational performance framework. In fact, a
Performance Management system aims at improving the results of people‟s efforts by linking these to the
organization‟s goals and objectives. It is, ideally, the means through which employees‟ performance can be
improved by ensuring appropriate recognition and reward for their efforts, and by improving communication,
learning and working arrangements. Table 2 provides the salient aspects of ORP as referred to this research.
Table 2: The Dimension, Meaning, Literature Support, and the Sample Item - ORP
Dimension Author, Year Description Sample Item
Organizational
Performance
[20]-[26] It is the indicators of performance of an
organization can be broadly categorized in to
Operational performance, Financial performance
and Non-financial performance factors
Our investments in R&D
are high.
III. RESEARCH OBJECTIVES
The aim of this research is to study the dynamics of knowledge management processes on the
organizational performance in knowledge intensive service organizations. To accomplish this aim, following
objectives have been developed.
1. Identify the dimensions which constitute the knowledge management processes as relevant to knowledge
intensive service organizations.
2. Develop a metric to measure above mentioned research constructs and validate it.
3. Develop a hypothetical model linking the various dimensions of the research constructs.
4. Obtain the empirical evidence for the inter-relationships between the dimensions of the research constructs.
5. Draw implications and make suggestions to the organizations to enhance the organizational performance
through the improvement in knowledge management processes.
IV. RESEARCH METHODS
4.1 The Hypothetical Research Model - Linkage between KMP and ORP
As per the objectives of the research, the hypothetical model to study the influence of KMP dimensions
on ORP is developed based on the literature available. If KM, as it claims, focuses on building the successful
link between knowledge and performance [27], then it is logical to assume KM activities will help to produce
valid organizational knowledge, which is justified by its ability to perform [28]. A performance measurement

framework is therefore required to determine how successful KM activities have been in attaining organizational
objectives [29]. Many organizations have used KM and organizational performance measurement for internal
management [30]. The EFQM Excellence Model is designed as an audit tool to facilitate excellent quality
management, hence focuses on auditing what has already happened, rather than addressing strategic issues such
as formulation and evaluation of strategy ([31], [32]). It is important to create insights into the value drivers,
which are the vital intangible assets that determine future success and form the basis for formulating resource
based strategies [33]. So, as a suitable measurement framework for KM, organizational performance is
important [30]. Many researchers have emphasized the importance of knowledge infrastructure and processes
for KM ([34]-[37]) for producing influence on organizational performance.
In general, it is considered as the process of developing new knowledge and insights derived from the
common experiences of people within the organization and it has the potential to influence behaviors and
improve a firm‟s capabilities [38]. Drawing upon these early research, Jimenez-Jimenez and Sanz-Valle [3]
have identified four processes of KM, namely, acquisition, distribution, interpretation and organizational
memory, which are of special interest in the context of organizational performance. It is important to find if the
KM processes have a bearing on organizational performance, and hence, the following hypotheses have been
postulated.
H1: Knowledge acquisition has a significant influence on organizational performance.
H2: Knowledge distribution has a significant influence on organizational performance.
H3: Knowledge interpretation has a significant influence on organizational performance.
H4: Organizational memory has a significant influence on organizational performance.
The hypothetical research model is depicted in fig.1.
Figure 1: Hypothetical Research Model
4.2 Development of the Questionnaire
The development of the questionnaire was through the standard procedure of skimming through the
available scales and metrics, consulting with the experts in the field, qualified respondents, and mainly through
the literature [39]. However, as the three research constructs were of research interest to both academics and
practitioners in service as well as product industries standard questionnaires which were available and some of
the items were readily usable with little or no modification to suit to the requirements of this research. The
dimensions used under each of the research constructs, the contributing authors, the descriptions and the sample
item of the questionnaire is given in the section Literature Review. The questionnaire had three distinct
components of primary data collection: the first was demographics, the second quantitative data through Likert
5-point scale, and the third qualitative data through open-ended questions.

4.3 Sample Description
Considering the intensive use of knowledge and its application for the achievement of business goals
and objectives the two sectors chosen were Higher Educational (HE) Institutions and the IT Companies. Ease of
access to the employees and data collection through electronic means was also the inclusion criterion.
In Tamil Nadu State there are 21 state government universities, 14 central government institutions, 08
state& central government joint support universities, 28 private universities (deemed to be universities) and
there are more than 100 colleges affiliated to these universities [40]. While obtaining the actual number of
faculty in these institutes is beyond the scope of this research as the number is ever growing an estimation was
made to define the population size and it was found that about 25,000 faculty members are working as Asst.
Professor, Associate Professor, and Professor in these institutes. There are 238 IT parks and 1683 software
exporting units in Tamil Nadu and about 2,70,000 employees in IT industries in Tamil Nadu [41].
As per the second generation statistical analysis of Structural Equation Modelling used in this research,
sample size is not an issue as long as the minimum sample size criterion of above 200 is satisfied. The software
has the technique of bootstrapping through which extrapolation into any sample size is possible. However, to be
sure of the minimum sample size, the approach of specifying the precision of estimation desired first, and then
determining the sample size necessary to ensure it [42] was adopted, according to which, the sample size
necessary is about 188, however, to get a better sample distribution the sample size chosen in this research is
491 (HE institutions = 330; IT companies = 161). The main objective of this research is not the inter-sector
comparison, instead the study of the impact of the knowledge management processes on organizational
performance and hence proportionate random sampling was not necessary and simple random sampling has
been adopted. The sample size estimation is based on the 2% defect in sample (based on pilot study) and an
acceptable error of 2%.The target respondents were the teaching faculty from HE institutions and managers
from the IT companies.
4.4 Method of Data Collection
Questionnaires were administered both on personal and electronic mode. Out of 600 questionnaires
distributed, 520 completed questionnaires were collected back after repeated pursuit (87% return rate). Informal
interviews have also been conducted with the employees during the field visits. The sample has been
randomized across the knowledge workers in the HE institutes and the IT companies in Tamil Nadu. The
quantitative analysis through the questionnaire survey has been substantiated with the views of the knowledge
workers both through the questionnaires as well as field visits.
4.5 Pilot Study and Analysis
The survey questionnaire is then subjected to a pilot test for a sample size of 32 for the content,
construct and criterion validity. It was also subjected to confirmatory factor analysis and the original 51 items
questionnaire is reduced to 30 items. This questionnaire is used for the primary data collection for the sample
size of 491. The data thus collected was subjected to analysis which included descriptive statistics & inferential
statistics using IBM SPSS 19, LISREL package for Goodness of Fit calculations, and SEM package SmartPLS
2.0 for constructing the measurement and structural model.
V. RESULTS AND DISCUSSION
5.1 Demographic details of Respondents
This section presents the demographic distribution for an understanding of the respondents‟
characteristics and their distinct features. As the descriptive provides an idea about the respondents details and
background it provides strength to the inferences which are drawn through the data. The respondents chosen for
this research are the knowledge workers of the Higher Education (HE) institutions and the IT sector. Table 3
gives the demographic details of the respondents.
The total sample size in this research was 491 (HE institutions = 330; IT companies = 161). The
majority of the respondents happen to be male (67.6%) in this research (Table 3). Majority of the respondents
are in the age group of 20-30 years (40.5%) followed by the age group of 30-40 years (27.5%). Educational
qualification wise, majority of the respondents were post-graduates (40.5%) followed by graduates (26.9%). In
terms of experience, majority had five to 10 years of experience (58%). The majority of the respondents were in
the salary range of Rs. 20,000/= to 40,000/= (57.2%). Thus, the respondents are by and large normally
distributed across each of their characteristics and majority are from a group which is competent enough to
provide the required information both in qualitative and quantitative forms and are mature enough, adequately
qualified, well-experienced, and are competent enough to provide data and information for this research.

Table 3: Demographic distribution of the Respondents (N = 491)
Attributes Frequency Percentage
Gender
Male 332 67.6
Female 159 32.4
Age
Less than 20 years 65 13.2
20 – 30 years 199 40.5
30 – 40 years 135 27.5
40 – 50 years 52 10.6
Great than 50 years 40 8.1
Educational qualification
Diploma 68 13.8
Graduate 132 26.9
Post graduate 199 40.5
PhD 52 10.6
Others 40 8.1
Work Experience
Less than two years 12 2.4
2 – 5 years 74 15.1
5 – 10 years 120 24.4
More than 10 years 285 58.0
Income per month(INR)
Less than 10,000 2.9 2.9
10,000 to 20,000 13.8 13.8
20,000 to 30,000 28.1 28.1
30,000 to 40,000 29.1 29.1
40,000 to 50,000 15.1 15.1
Above 50,000 11.0 11.0
5.2 Measurement Model
The maximum likelihood method of estimation was chosen for conducting SEM analysis. Table 4
represents the Goodness of Fit (GOF) indices for both the initial measurement models and final measurement
model for all constructs. The last two rows represent GOF results for the full measurement model and
recommended values for acceptable GOF. The overall GOF measures for some of the initial models did not
meet the acceptable criteria, so the models were revised based on assessment of factor loading (Table 6) and
suggestion from modification indices. This resulted in reduction of 26 item original a priori metric, into 15 item
scale which was subjected to GOF test criterion. GOF results for both individual measurement models and full
measurement models are within the acceptable range with non-significant χ2 (Chi-square) (>=0.05), goodness
fit index (GFI), adjusted goodness fit index (AGFI) and Tucker-Lewis Index (TLI) values greater than 0.9 and
Root Mean Square Error of Approximation (RMSEA) value < 0.10.
Table 4: Goodness of test results for measurement models
Construct No. Of Items χ2
Df p χ2
/Df GFI AGFI RMSEA TLI
KNA - Initial 5 123.2 4 0.04 30.6 0.94 0.84 0.13 0.84
KNA - Final 3 1.64 2 0.32 0.5 0.86 0.94 0.12 0.96
KND - Initial 5 121.3 4 0.04 20.4 0.92 0.94 0.06 0.93
KND - Final 3 2.6 2 0.68 0.8 0.84 0.94 0.03 0.92
KNI - Initial 5 85.5 4 0.004 10.8 0.92 0.97 0.04 0.97
KNI - Final 3 8.3 2 0.16 2.8 0.93 0.8 0.04 0.93
ORM - Initial 5 342.9 4 0.04 85.5 0.96 0.94 0.13 0.91
ORM- Final 3 6.4 2 0.66 1.8 0.95 0.87 0.08 0.92
ORP – Initial 6 171.3 5 0.04 24.4 0.93 0.96 0.08 0.93
ORP - Final 3 3.6 2 0.68 0.9 0.85 0.93 0.12 0.92
>=0.05 <=3.0 >=0.9 >=0.9 <=0.1 >=0.9
Legend:
Knowledge Acquisition (KNA)
Knowledge Distribution (KND)
Knowledge Interpretation (KNI)
Organizational Memory (ORM)
Organizational Performance (ORP)
To verify the reliability of the latent variables in the model, internal consistency reliability measure,
item reliability measure and composite reliability measures were calculated. Table 5 shows the Cronbach‟s
alpha coefficient and the composite reliability result for the final model. The alpha coefficient has the acceptable

value ranging from 0.6 to 0.9, indicating a moderate to high level of internal consistency. The composite
reliability estimate also ranges from 0.6 to 0.9 indicating moderate to high reliability values. The results of the
convergent validity assessed based on factor loading (> 0.6) indicate a strong effect of the factor on the variable
of study (Table 6). To test for discriminant validity, the square root of average variance extracted (AVE) for
each construct was compared with the correlation between the construct and the other constructs. Table 7 shows
acceptable discriminant validity between each pair of construct, with all AVE square roots greater than the
correlation between the constructs.
Table 5: The Reliability Measures of the Data
AVE Composite Reliability R Square Cronbach‟s Alpha Communality Redundancy
KNA 0.63 0.8283 0.6387 0.6654 0.63 0.1157
KND 0.7363 0.8924 0.668 0.8171 0.7363 0.4407
KNI 0.7451 0.8973 0.6849 0.8263 0.7451 0.2647
ORM 0.8004 0.9231 0.4041 0.8749 0.8004 0.0996
ORP 0.7913 0.919 0.9266 0.8664 0.7913 0.0523
Table 6: Factor Loading after Reduction
CMR KNA KND KNI ORM ORP PRA RCT TMW TRD
KNA1 0 0.5134 0 0 0 0 0 0 0 0
KNA3 0 0.9535 0 0 0 0 0 0 0 0
KNA4 0 0.8469 0 0 0 0 0 0 0 0
KND1 0 0 0.8964 0 0 0 0 0 0 0
KND2 0 0 0.9326 0 0 0 0 0 0 0
KND3 0 0 0.7319 0 0 0 0 0 0 0
KNI1 0 0 0 0.8652 0 0 0 0 0 0
KNI2 0 0 0 0.9273 0 0 0 0 0 0
KNI4 0 0 0 0.7918 0 0 0 0 0 0
ORM1 0 0 0 0 0.8579 0 0 0 0 0
ORM2 0 0 0 0 0.8881 0 0 0 0 0
ORM3 0 0 0 0 0.9362 0 0 0 0 0
ORP2 0 0 0 0 0 0.8596 0 0 0 0
ORP3 0 0 0 0 0 0.9556 0 0 0 0
ORP5 0 0 0 0 0 0.8496 0 0 0 0
Table 7: Inter-item Correlation and Discriminant Validity
CMR KNA KND KNI ORM ORP PRA RCT TMW TRD
KNA 0.6564 1 0 0 0 0 0 0 0 0
KND 0.5985 0.8376 1 0 0 0 0 0 0 0
KNI 0.7599 0.632 0.5408 1 0 0 0 0 0 0
ORM 0.3052 0.2518 0.354 0.377 1 0 0 0 0 0
ORP 0.7617 0.6293 0.6586 0.8606 0.4562 1 0 0 0 0
5.3 Structural Model
From the main hypotheses testing the causal relationship between the constructs of study was
established and a differential causation was observed for the research constructs. So, the next research question
that was to be addressed was dynamics between these constructs when these variables are interlinked through
the theoretical models. The obvious solution was the Structural Model of the SEM which has been described in
the previous chapter. Structural model of the analysis gives the inter-relationships between the exogenous and
the endogenous variables of study. This is used for the hypothesis testing at the macro level of the latent
variables. The factor loadings after reduction, path coefficients, and R2 are shown in Table 5 and the t-values
are shown in Table 8. For all the relationships established, the path coefficient values ranged from 0.03 to 0.6
and the R2 values were up to 0.9 which is quite adequate in comparison to the other research studies in this field
(cut off 0.1) ([33],[43]). The strength of the relation is moderate and the percentage influence of the exogenous
variables on the endogenous variables as expressed by R2 is acceptable.
The SEM indicated that the following hypotheses were supported:
H1: Knowledge acquisition has a significant influence on organizational performance.
H2: Knowledge distribution has a significant influence on organizational performance.
H3: Knowledge interpretation has a significant influence on organizational performance.
H4: Organizational memory has a significant influence on organizational performance.

Table 8: t-statistic of Hypothetical Research Model
Original
Sample (O)
Sample
Mean (M)
Standard Deviation
(STDEV)
Standard Error
(STERR)
T Statistics
(|O/STERR|) Hypothesis
KNA ->ORP -0.3129 -0.3124 0.0697 0.0697 4.4909* Supported
KND ->ORP 0.265 0.259 0.0763 0.0763 3.4724* Supported
KNI ->ORP 0.5314 0.5264 0.0501 0.0501 10.6118* Supported
ORM ->ORP 0.1224 0.1178 0.0407 0.0407 3.0081* Supported
*(1% significance; t>2.58).
VI. FINDINGS AND IMPLICATIONS TO THE STRATEGIC MANAGERS
Knowledge management has emerged out to be a dimension which has a significant influence on
Organizational performance. So, one of the most important implications to the policy makers and the managers
of knowledge intensive service organizations is that the most effective means to achieve better organizational
performance is through strengthening the KM processes.
It is implied that the managers of the service sectors need to consider the four major critical success
factors recommended by a group of researchers who have extensively worked on the KM models and arrived at:
the human based factors (leadership, culture, and people); organizational factors (processes and structures);
technology based factors (infrastructure and applications); management (strategy, goals, and metrics) ([44]-
[47]).
It is a general observation that KM processes heavily rely on technology and the service organizations
need to invest on these technologies particularly the Information Technology (IT). A set of organizational
changes are strongly recommended based on the information gathered through the field visits and the extensive
literature review in which the past experience of the service organizations have been analysed. To have a
positive impact on elements of knowledge, IT has to be introduced in a phased manner.
It is also necessary that IT is backed up by changes in people, organizational climate and organizational
processes. Organizational change helps an organization to optimize processes and define process oriented
structure. Further, a behavioural and cultural change has to be brought in the service providers to develop
flexibility to adapt to the changing situations. A strong culture, trust and transparency in all areas of the
organization may be necessary to make the KM processes to be effectively supported by the knowledge
workers.
VII. CONCLUSION
This research primarily focused on the study of the influence of the dimensions of KM processes:
knowledge acquisition, knowledge distribution, knowledge interpretation, and organizational memory on
organizational performance.
The revelation through hypothesis testing was that all the dimensions of KMP had a significant
influence on organizational performance. All these results were in accordance to the earlier findings in various
other contexts.
These revelations have led to the development of the strategic implications to the managers of the
knowledge intensive service industries which may be considered these organizations for enhancing their
organizational performance.
The research has a few limitations which provide scope for future research. First of all, this is mainly a
quantitative analysis and all the limitations of the second generation statistical analysis are applicable to this
research. Sample size has always been an issue in empirical studies and in this research even though the
standard formula has been adopted the assumptions in the formula act as the limitation for the possibility of
generalization of the results completely. Finally, this is a perceptions based study and has its own methodical
limitations. Future researchers may consider the possibility of studying the combined influence of knowledge
management as well as total quality management as the mediating variable. In this modern era based on
knowledge economy this research is quite timely and the implications to the managers drawn in this research
may be of immense use in enhancing the organizational performance.
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