Enhancing employee productivity and service quality using ERP and KM

DOI: 10.4018/IJKM.2020070106
International Journal of Knowledge Management
Volume 16 • Issue 3 • July-September 2020

Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Employee Productivity and Service
Quality Enhancement Using ERP
With Knowledge Management:
A Study in a Power Distribution
Company in the Global Context
Animesh Agrawal, National Institute of Technology, Raipur, India
https://orcid.org/0000-0001-8175-5410
Hemant Kumar Diwakar, National Institute of Technology, Raipur, India
Suraj Kumar Mukti, National Institute of Technology, Raipur, India
ABSTRACT
Many researchers have identified that implementation of knowledge management (KM) improves
the success rate of enterprise resource planning (ERP) in different sectors, but the support of KM in
post-ERP implementation in the service sector is yet to be analyzed. The aim of the study is to confirm
the support of KM and to identify the post-implementation effect of ERP on employees and service
quality. For the fulfillment of this research, data were gathered from the service sector’s employees
and consumers. The research has been processed in three stages: The first stage includes the variables
identification from the previously published literature and reduced to smaller groups with the help
of IBM SPSS software. Second stage explores the preparation of conceptual model. The third stage
involves the identification and substantiation of KM support with post-ERP implementation, which
has been accomplished through a short brainstorming session with employees and senior manager
of power distribution company. This research concludes that there is positive support of KM in post-
ERP implementation.
Keywords
Employee’s Productivity, End-User Satisfaction, Enterprise Resource Planning, Knowledge Management,
Organizational Impact, Service Quality, Success Factors
1. INTRODUCTION
Enterprise Resource Planning (ERP) systems are software system comprising various modules,
such as human resources, sales, finance, and production, providing cross-organizational mixing of

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transaction-based data management during embedded business processes support (Sousa, 2004). ERP
system is an integrated software bundle poised by a standard practical unit (manufacturing, Sales,
Human Capitals, Economics, etc.), established or integrated by the vendor, which can be altered to
the precise needs of each customer (Botta-Genoulaz & Millet, 2006). The main focus of coordinating
ERP is to enhance the working skill of business by improving business forms and thinning operational
costs (Mukti, 2017).
After a thorough review of the literature, it seems that the success of the ERP system may be
increased with the collaboration of KM. Eventually, it can be concluded that KM may be used as
an effective tool to enhance the overall performance of the ERP system. Presently, no literature is
depicting such type of alliance of two systems after post ERP implementation. This research article
will give a point-to-point roadmap (Model) to enhance employee productivity and service quality.
2. LITERATURE REVIEW
ERP system provides the flawless integration of all the information curving through the company
such as secretarial, finance, supply chain, human resource and client information (Mukti & Rawani,
2016). ERP systems can be distinct as assimilated software bundle composed of a set of distinctive
practical elements such as manufacturing, sales, human capitals, economics, etc., which can be adapted
to the specific needs of each organization (Botta-Genoulaz & Millet, 2006). The ERP thoroughly
integrates all the operational processes in the organizations (manufacturing and production, finance
and accounting, sales & marketing and human capitals) that have been implemented as scattered
systems into a solo software system. This system facilitates the integration of information by utilizing
a central data storehouse allowing effective use of information by different parts within an organization
(Laudon & Laudon, 2015) (Botta-Genoulaz & Millet, 2006).
Implementation of ERP systems in an organization gives the new opportunity to the managers
of the companies to connect with their counterparts i.e. employees and staffs working under
different departments. ERP system also helps to achieve many benefits including the accessibility of
incorporated information, a high responsiveness to customers’ and suppliers’ need and the stipulation of
timely information to decision-makers. As the majority of researchers have investigated ERP success.
Only a few studies have concentrated on users’ point of view (Po-An Hsieh & Wang, 2007). Assessing
the post-implementation success of ERP systems from the side of individual users is crucial as ERP
systems may be due in part to underutilization of the systems by the users. As studies have exposed,
a common cause for ERP failures can be credited to users’ disinclination or refusal to adopt and use
the recently implemented ERP system (F. F. Nah, Lau, & Jinghua, 2001).
Knowledge and its management were emphasized on the mid-1980s, the organizations started
to appreciate and focus on an individual’s knowledge and the importance of their knowledge. In this
era, organizations emphasized product quality, quality, and facilities, the receptiveness of products,
using the appropriate placement of an individual’s knowledge. Many of the literature has defined the
Knowledge Management (KM) but Davenport (1994) postulated KM as “the process of capturing,
developing, sharing, and effectively using organizational knowledge” (Davenport, 1994). Later Duhon
(1998) provided another definition: “KM is a discipline that promotes an integrated approach to
identifying, capturing, evaluating, retrieving, and sharing all of an enterprise’s information assets”
(Duhon, 1998). According to Gloet and Terziovski (2004), KM is the interpretation of experience and
how to access that knowledge and proficiency, that produce new competencies, empower superior
routine, boost innovation, and increase customer satisfaction. This study also described the KM act
as an umbrella for a variety of interconnecting terms, such as knowledge conception, knowledge
assessment, and metrics, knowledge aligning and indexing, knowledge conveyance, storing and
circulation and knowledge allocation (Gloet & Terziovski, 2004).
In today’s competitive environment organizations are facing the challenges of information sharing
from one department to other seamlessly. The key issue in ERP implementation synchronizes the

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system with it optimally. The ERP system with the support of KM incorporates, accomplish and
synchronize business procedures through the whole organization comprising all levels of management
allowing organizations to become more flexible and productive (Laudon & Laudon, 2015).
Knowledge management helps to increase the success rate of ERP implementation. Knowledge
transfer increases the competitive advantages and supports the successful implementation of ERP
in an organization. Lee & Lee (2000) have identified the types of knowledge transfer and the factors
affecting knowledge transfer for the successful implementation of ERP (Lee & Lee, 2000). Sedera et
al. (2003) have discussed the impact of KM on the success of an ERP system by empirical relations
(Sedera, Gable, & Chan, 2003). The integration of KM with ERP system has always a positive effect
on an organization. Knowledge can be reflected as an enterprise’s imperceptible assets (Xu, Wang,
Luo, & Shi, 2006). The emergence of KM with the ERP system always give a competitive advantage
to an organization by providing the e-business environments. Implementation of KM with ERP
facilitated different positive effects on the competitive era of an organization e.g. employee education
and learning, information, and data network, knowledge sharing process and knowledge scanning (Li
& Zhao, 2006). For the implementation of projects, organizations must have the capability of effective
knowledge sharing of enterprise-wide information and knowledge (Vandaie, 2008). Knowledge sharing
culture might be a catalyst by effective use of KM. The relationship between the organizational
culture and the four sets of KM (i.e. knowledge acquisition, knowledge sharing, knowledge storage
and application of knowledge) is necessary for the successful implementation of ERP (Palanisamy,
2008). Jeng & Dunk (2013) have discussed how KM enablers and knowledge acquisition increase
the success rate of ERP. In this project, the investigator has confirmed that knowledge acquisition has
a positive impact on the successful implementation of ERP implementation (Jeng & Dunk, 2013).
Different model based on ERP systems with the support of the KM system (engineer-to-order) for
different companies like manufacturing industries has been proposed (Kłos, 2016). Acar, Tarim,
Zaim, Zaim, & Delen (2017) have shown the relationship among ERP and KM and proved that KM
implementation gave success to the ERP system and association of both the systems is complimentary,
not contradictory (Acar, Tarim, Zaim, Zaim, & Delen, 2017). The different framework that targets
ERP with support of KM for different business models, selection, customization and evolution models
may be explored (Olson, Johansson, & De Carvalho, 2018). Jennex & Olfman (2005) proposed a
framework estimating KM system success models. The project will also discuss the KM system
success models based on Delone & McLean (2003). M. Jennex & Olfman (2005) proposed the KM
system success factors from previous studies and projects (M. Jennex & Olfman, 2005). KM plays
an important role in small and medium-sized enterprise as the steel sector and factors have been
analyzed (Animesh & Mukti, 2019).
2.1. ERP Successful Implementation: Meaning and Significance
According to F. F. H. Nah et al. (2003) and Loh & Koh (2004) and ERP system implementation
success integrates, employees productivity in an enterprise. It demands the effort and cooperation
of technical and business experts as well as end-users. Bhatti (2005) concluded that the successful
implementation of the ERP system is the capability to generate and communicate accurate and timely
information. The organizations which have successfully implemented the ERP systems are reaping
the benefits of having integration working environment, standardize the process and operational
benefits to the organization.
3. RESEARCH GAP
The motivation to perform the present study is the scarcity of literature in support of ERP system
with the KM. Very few literatures have discussed the variables affecting the post ERP implementation
and no literatures are present in the context of the power sector in a global context. No literatures

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have discussed the variables influencing the employee productivity and service quality after the
implementation of ERP system in an organization.
Rare literatures are available to justify the valid relationship between post ERP implementation
variables with its end users e.g. employees & consumers etc. No literature is present which discusses
the conceptual model/framework/roadmap to enhance the employee productivity and service quality
using ERP with KM. So it was advisable, to extend the research by implementing the ERP with KM
in any service industry. In the present research article, a power sector is chosen as a case study and
have been studied about the variables of ERP system with KM to enhance the employee productivity
and service quality in an organization.
4. RESEARCH OBJECTIVES
On the basis of above-mentioned gaps, the following objectives are outlined:
• To identify variables for employee’s productivity and service quality after ERP implementation
with KM;
• To perform an exploratory factor analysis among variables related to employee’s productivity
and service quality after ERP implementation;
• To develop a conceptual model/ framework to enhance employee’s productivity and service
quality after ERP implementation.
5. METHODOLOGY
This section of the research article shows, a roadmap of overall research work. A case study was
developed for the present research purpose. An electricity board was targeted for this study. Data
were collected in this organization, then a Kaiser-Meyer-Olkin (KMO) test was performed to check
adequacy of sampling size. A questionnaire was developed based on five point Likert scale. It was used
to measure the respondent’s attitude by measuring the extent to which they agree or disagree with a
particular statement or question. Likert scale used psychometric testing to measure opinion attitudes
and beliefs. Table 1 shows the description of techniques and sampling techniques used in this study.
Data are analyzed using Exploratory Factor Analysis (EFA) and grouping of the variables has
been done. After grouping the variables Structural Equation Modelling (SEM) has been performed.
A conceptual model is developed to identify the relationship among organizational impact, end-user
satisfaction, employee’s productivity and service quality. Based on this model hypothesises were
generated and tested using SPSS AMOS software.
Table 1. Description of the techniques and sampling scheme
Technique Description of the Study Sampling Scheme
Questionnaire Survey
To identify variables for employees
productivity and service quality after ERP
implementation with KM.
The questionnaire sent to the employees
and consumers of Chhattisgarh State Power
Distribution Company Limited (CSPDCL)
and 165 valid responses has been collected.
Framework
To develop a conceptual model/ framework
to enhance employees productivity and
service quality after ERP implementation.
Highly purposive for validation of
framework.

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6. DATA COLLECTION
Data collection can be classified in two categories, primary data collection technique and secondary
data collection technique. The primary data collection involves like personal interviews, group
discussions, telephonic conversation and through social networking. The secondary data collection
type can be defined as the collection of annual report of the company and information collected
by government departments etc. Both primary and secondary data were collected and analysed for
this research article. Data is collected from power distribution company in central region of India,
for study purpose. Authors have selected the organization which have already implemented ERP
system i.e. Chhattisgarh State Power Distribution Company Limited (CSPDCL), which is responsible
for generation, transmission and distribution of electricity in the central India. Some of the power
distribution companies in India are seeking to implement ERP system. This exercise provides an in-
depth acquaintance about the post-ERP implementation and necessary information to maintain the
existing information system.
Data has been collected through an online survey (Google forms), as well as offline survey
technique from both employees and consumers. Two sets of questionnaires were sprayed through
an online questionnaire using Google forms. Set-1 questionnaire has been sent to the employees or
end-users of CSPDCL, which was based on employee productivity measurement. There were total
of 250 respondents involved who were employees of CSPDCL and out of which 102 valid responses
were found. Consequently, Set-2 questionnaire based on service quality measurement and was sent to
consumers of CSPDCL. To take responses using sampling technique from the consumers, a random
sampling method including 63 valid responds out of 150 respondents was used. Overall valid response
percentage of both sets was 41.5% of the total population.
In addition, a brainstorming session with the senior employees of the CSPDCL, having experience
10 years or more was organized. Brainstorming is a group or individual creativity technique by which
efforts are made to find a conclusion for a specific problem by gathering a list of ideas spontaneously
contributed by its member(s). The term was introduced in 1953 by Alex Faickney Osborn later in 1957
it has been revised and again in 1963 (Jacobs, 1984). Osborn claimed that brainstorming was more
effective than individuals working alone in generating ideas (Mitchell 1991). Brainstorming session
arranged by comprising the team of eight managers and 94 employees of CSPDCL. In addition to
this an extensive literature survey was carried out. Based on brainstorming and literature review a
comprehensive list of variables were prepared and chosen for further analysis. Variables related to
post-ERP implementation were emphasised during this session. This study needs specific variables
focusing on employee’s productivity and service quality in an organization. After identification of the
variables, it was instructive to extend the research in the field of power sector companies and identify
the factors for employee productivity and service quality. Factor analysis has been done with the
help of SPSS software and result of the factor analysis has been taken for further analysis. In Table 2
variables related to employee productivity and service quality with respective authors are represented.
• Business Strategy: Positive ERP implementation involves reshaping business processes from a
rigid, mass-transaction direction to a nimble, lean, and knowledge-based process;
• Product Variety/ Process Improvement: Increasing product lines have a positive impact on
viable and for many firms (Kekre & Srinivasan, 1990). Firm capabilities donate to performance
outcomes because they symbolize dynamic routines that can be turned into a unique pattern to
drive product and service differences (Teece, Pisano, & Shuen, 1997). Firm performance can
be divided into two parts viz. return on assets or financial returns;
• Change Management-Effect of Company’s Decision on Employees: Basically change
management involves all the stakeholders, employees of the firm owners etc. who will able to
realize the benefits of the change in their respective firm. Firms develop their capabilities to
create competitive advantage by leveraging organizational resources such as information system

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Table 2. Variables of employee productivity and service quality
S. No. Variables Name Variable Code Author(s) and Year
1.
Business strategy-employee
feedbacks or ideas
VAR01BUSSISTRAT
Smith, Mills, & Dion
(2010), HassabElnaby,
Hwang, & Vonderembse
(2012)
2. Process improvements VAR02IMPROVE
Kekre & Srinivasan
(1990), HassabElnaby et
al. (2012)
3.
Change management-effect
of company’s decision on
employees
VAR03CHGMGMT
M. E. Jennex & Olfman
(2002), Campatelli,
Richter, & Stocker
(2016)
4. Return on assets VAR04RETURNS
Hunton, Lippincott,
& Reck (2003),
HassabElnaby et al.
(2012)
5. Training provided to employees VAR05TRAINING
Dezdar & Ainin (2011),
Hsu, Yen, & Chung
(2015)
6.
Information flow quality/quantity
within the company
VAR06INFOFLOW
Chien & Tsaur (2007),
Al-Busaidi (2010),
Chawla & Joshi (2011)
7.
Interdepartmental
communication
VAR07COMM
Ballard & Seibold
(2006), Laframboise,
Croteau, Beaudry, &
Manovas (2007), Usoro
& Kuofie (2006)
8.
Appraisal ratings/employees
performance rating
VAR08APPRAISAL
Pearce & Porter (1986),
Qutaishat, Khattab,
Khair, Abu, & Amer
(2012)
9. The expectation from the system VAR09EXPFULFIL
Hackett, Mirvis, & Sales
(1991), Al-Busaidi
(2010)
10. Manual work reduction VAR10MANUALWORK
Al-Busaidi (2010)
Tziritas, Xu,
Loukopoulos, Khan, &
Yu (2013)
11.
Overall benefits of ERP system
to employees
VAR11BENEFITS
Sabherwal & Sabherwal
(2005), Hsu et al. (2015)
12. Product quality VAR12PRODUCT
Yeh, Yang, & Lin
(2007), HassabElnaby et
al. (2012)
13. Response time VAR13RESPTIME
Yeh et al. (2007), Hsu et
al. (2015)
14. Staff availability VAR14STAFFAVA
Yeh et al. (2007), Hsu et
al. (2015)
15. Latest technology VAR15TECH
Siau & Tian (2004), Hsu
et al. (2015)

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to develop unique and change-oriented capabilities. These organizational capabilities enable them
to meet customer needs and respond to challenges from competitors (Qutaishat et al., 2012);
• Return on Assets or Financial Returns: Variable is the most important in term of reviewing
firms recent performances. ERP implementation helps to gain the profit from the market
(Hunton et al., 2003);
• Training Provided to Employees: KM system improves the satisfaction level of users in terms
of providing the training to the employees after implementation of the new system (M. E. Jennex
& Olfman, 2004);
• Information Flow Quality/Quantity: Can be considered as how much information is required
by the user and what information is relevant to the user (Chien & Tsaur, 2007). Information
access/flow mainly takes organizational memory as a storehouse of information created from
the organization’s history (M. E. Jennex & Olfman, 2002);
• Interdepartmental Communication/Relation: ERP implementation gives the advantage of
communication with employees and with consumers gives the new height to the organization.
Support of the top management for any system, there must be a proper information flow with
its end users (M. E. Jennex & Olfman, 2004);
• Appraisal Rating: One of the best variable used in various literature in the past to measure the
employee productivity factor (Pearce & Porter, 1986);
• The Expectation From the System: ERP helps to record and register the complaints regarding
power cuts or transformer breakdowns in any power distribution company;
• Manual Work Reduction: ERP helps to reduce manual handling work like information and
processes within an organization;
• Overall Benefits of ERP System to Employees: ERP helps the level of satisfaction for a different
group of people. ERP system provides the benefits to consumer as well as to the employees of
an organization (Jenatabadi & Noudoostbeni, 2014);
• Product Quality: One of the major concern for the organizations for shielding IS assets, networks,
data, information, computers, and applications by malicious network breach restricting access
to the assets and stopping illegal modification or destruction (M. Jennex & Durcikova, 2014);
• Response Time: Should be as minimum as possible to attend the complaints;
• Staff Availability: Variable can be explained as qualified employees are the necessity of an
organization. It is very much important to have the good working ambiance for the user as well
as employees. The availability of staff also depends on whether it’s comfortable and encouraging
working environment is present or not. Number of staffs or employees to handle a task is necessary
for the effective working culture in an organization. (Lotfy & Halawi, 2015);
• Latest Technology: Can be referred to as the advancements in the technology used by the end-
users or even consumers (Lotfy & Halawi, 2015);
• Organizational Impact: Refers to the awareness of business goals and improved venture
operating capabilities as a result of the ERP implementation. An individual’s use of a system
will create a force on that personal performance in the workplace, each individual impact will
sequentially have an effect on the performance of the entire organization (M. Jennex, 1998).
The alleged organizational impact variable covers both effectiveness and efficiency based
performance improvements in order to confine the business benefits of the ERP system (Stratman
& Roth, 2002). Service quality has been classically viewed as a main planned component of
competitive benefits and for the enrichment of service and product quality in an organization
(Soltani, Azadegan, Liao, & Phillips, 2011). A lot of factors cause poor product quality in small
industries related to manufacturing. These factors cannot afford superior management systems
such as allotment delays and intervention, deprived human resource distribution and deprived
inventory management (Bosscha, Coetzee, Terblanche, Gazendam, & Isaac, 2006);
• Employee Productivity: An evaluation of the effectiveness of a worker or group of workers.
Productivity may be evaluated in terms of the output of an employee in a specific period of time.

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The originator of David Caruso & Associates incorporated. M. E. Jennex & Olfman declared
that employees are the secret assets of the success of any business. Information Systems that
contain KM is more probable to improve the productivity of workers (M. E. Jennex & Olfman,
2002). IT in quality management play a vital role in the improvement of quality understanding
of the enhancement of services and products (Mjema, Victor, & Mwinuka, 2005).
7. DATA ANALYSIS
Collected data were analysed focusing on employee’s productivity and service quality after ERP
implementation in an organization. Respondents were selected form the departments which are directly
or indirectly influenced by ERP implementation. For this purpose, departments like regulatory affair
& power management, training, research & development, civil distribution department, vigilance
department, HR department, the office of executive engineer, department of maintenance, project
management department have been considered. Factor analysis was presented to ensure construct
validity of the instrument, which has obtained through various statistical operations. Table 3 shows
the Kaiser-Meyer-Olkin (KMO) test, which measures sampling adequacy for each variable in the
model. The value from 0 to 1 is obtained through the KMO test and value between 0.6 to 1 indicate
the sampling is adequate. The Bartlett’s Test of Sphericity deals the factor analysis and the values
lesser than 0.05 is taken as the good value.
7.1. Measurement Instrument
Variables were selected from literature and was supported by a brainstorming session. Table 2 shows
identified variables after this step. Five-point Likert scale ranging from 1 (strongly disagree) to 5
(strongly agree) was used to measure the attitude of respondents. Table 4 shows the demography of
respondents including gender position and experience.
A factor analysis was performed to group the identified variables. There are two techniques for
the analysis of the factors viz. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis
(CFA). Both techniques have the purpose of uncovering latent factors. EFA, as the name says, is a
more exploratory approach than CFA. The purpose of CFA is to confirm to what extent your model
fits the data. This is done with a different statistical approach (SEM/Measurement Model) and is
dependent on the covariation matrices. EFA is useful to determine the factor structure or model while
CFA is used to compare the number of factors. EFA is also used to explains a maximum amount of
variance, while CFA can be used to analyze which items load on each factor, hence EFA technique
was applied for this study.
Figure 1 shows the scree plot between the eigenvalues and number of components depicting
eigenvalues greater than 1.
Table 3. KMO and Bartlett’s test
Kaiser- Meyer-Olkin measure Value
sampling adequacy 0.639
Approx. chi-square 547.615
Bartlett’s Test of Sphere City Value
Degree of freedom (df) 120
Significance .000

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Table 5 shows the total variance obtained from the factor analysis, comprising component, initial
eigenvalues, extraction sum of squared loadings and rotated sums of squared loadings.
Rotated component matrix data is given below which provides the reduction of variables under
a common factor. Table 6 shows the value of the rotated component matrix, a total of 6 factors have
obtained from the survey responses, which is fed to the statistical tool. For the simplification of
Table 4. Demography of respondents
Category
Set 1
Frequency
Set1
Percentage
Set 2
Frequency
Set 2
Percentage
Gender
Male 85 83.33% 50 79.36%
Female 17 16.67% 13 20.64%
Position
Manager 8 7.8% consumer - -
Employee 94 92.2% - -
Experience
Less than 3
years
66 64.7% consumer - -
3 to 5 years 28 27.45% - -
5 to 10 years 7 6.8% - -
More than 10
years
1 .90% - -
Total 102 63
Figure 1. Eigenvalue vs. The number of components

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calculation, only two or more variables under a single factor are taken into account, the rest of the
variables may or may not be used for further calculations.
The output of the factor analysis is given in Table 6 which shows the rotated component matrix,
in which the variables are grouped into the factors. Total 6 factors have been identified, but due to
insufficient loading of the variable only four groups are considered. After identification of factors,
terms organizational impact, user satisfaction, employee productivity and service quality are
designated. Total 6 factors, out of which 4 factors have the majority of the selected variables. Other
2 factors viz. manual work reduction and new technology were not supported either because of the
low positive response given to them or purely due to the algorithms of principle component analysis.
Table 7 provides grouping of variables under the factors.
All fifteen variables are grouped through Exploratory Factor Analysis (EFA) using SPSS software.
These are conversed in four groups named as Organizational impact comprising of (business strategy-
employee feedbacks or ideas, process improvements, change management-effect of company’s decision
on employees, return on assets), User satisfaction comprising of (training provided to employees,
information flow quality/quantity within the company, interdepartmental communication), Employee
productivity comprising of (appraisal ratings/employees performance rating, the expectation from the
system, manual work reduction, overall benefits of ERP system to employees) and Service quality
comprising of (product quality, response time, staff availability, latest technology).
After grouping of variables, Structural Equation Modelling (SEM) has been performed. On the
basis of this grouping a conceptual model has been developed which is depicted in Figure 2 comprising
of organizational impact, end-user satisfaction, employee’s productivity, and service quality. This
model also depicts that organizational impact and end-user satisfaction are the independent factors
while employee’s productivity and service quality are the dependent factors. It also illustrates the
Table 5. Total variance explained
Component
Initial Eigenvalues Extraction Sums of Squared Loadings Rotated Sums of Squared Loadings
Total
Variance
%
Cumulative
%
Total
Variance
%
Cumulative
%
Total
Variance
%
Cumulative
%
1 3.950 24.690 24.690 3.950 24.690 24.690 2.749 17.183 17.183
2 1.982 12.386 37.077 1.982 12.386 37.077 1.920 11.998 29.181
3 1.836 11.477 48.554 1.836 11.477 48.554 1.919 11.993 41.174
4 1.446 9.038 57.592 1.446 9.038 57.592 1.738 10.863 52.037
5 1.163 7.271 64.863 1.163 7.271 64.863 1.681 10.505 62.541
6 1.033 6.456 71.318 1.033 6.456 71.318 1.404 8.777 71.318
7 .877 5.480 76.798
8 .750 4.686 81.484
9 .640 4.003 85.487
10 .577 3.608 89.096
11 .394 2.462 91.558
12 .365 2.279 93.837
13 .285 1.780 95.617
14 .274 1.711 97.328
15 .220 1.374 98.702
16 .20 1.298 100.0
Extraction Method: Principal Component Analysis.

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Table 6. Rotated Component Matrix
Variables
Component Factors
1 2 3 4 5 6
VAR02IMPROVE .813
VAR01BUSSISTRAT .781
VAR03CHGMGMT .723
VAR04RETURNS .707
VAR13RESPTIME .816
VAR12PRODUCT .686
VAR14STAFFAVA .615
VAR07COMM .843
VAR06INFOFLOW .670
VAR05TRAINING .558
VAR09EXPFULFIL .806
VAR11BENEFITS .711
VAR08APPRAISAL .561
VAR10MANUALWORK .615
VAR15TECH .866
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 11 iterations.
Table 7. Grouped variables
S. No. Variable Grouped Under
1 VAR01BUSSISTRAT
Organizational impact
(4 Variables)
2 VAR02IMPROVE
3 VAR03CHGMGMT
4 VAR04RETURNS
5 VAR05TRAINING
User satisfaction
(3 Variables)
6 VAR06INFOFLOW
7 VAR07COMM
8 VAR08APPRAISAL
Employee productivity
(4 Variables)
9 VAR09EXPFULFIL
10 VAR10MANUALWORK
11 VAR11BENEFITS
12 VAR12PRODUCT
Service quality
(4 Variables)
13 VAR13RESPTIME
14 VAR14STAFFAVA
15 VAR15TECH

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interdependency of the factors. Based on the conceptual model hypothesis were generated and tested
through SPSS AMOS software.
Dezdar and Ainin (2011) have confirmed the positive influence of organizational factors for the
success of ERP implementation (Dezdar & Ainin, 2011). Beheshti and Beheshti (2010) have examined
that the success of ERP and employees productivity are vice-versa related to each other (Beheshti
& Beheshti, 2010). User satisfaction plays a major role on successful ERP implementation (Dezdar,
2012). Calisir and Calisir (2004) and Somers et al. (2003) have hypothesised the positive relationship
between end-user satisfaction with ERP systems and its application software (Calisir & Calisir, 2004)
(Somers et al., 2003). Tsai et al. (2007) examined the users’ service quality satisfaction in the ERP
consultant and found service quality enriches the performance of ERP systems (Tsai et al., 2007).
The four grouped factors play an important role to ensure the success of post-ERP implementation
system in an organization. Hence the proposed model is validated through various authors as mentioned
above. Hypotheses are framed to test the model qualitatively. Based on the conceptual model, following
hypothesises are generated and tested for its significance:
Hypothesis H1: There is a positive effect of the organizational impact with employee’s productivity.
Hypothesis H2: There is a positive effect of the organizational impact on service quality.
Hypothesis H3: There is a positive effect of end-user satisfaction with employee’s productivity.
Hypothesis H4: There is a positive effect on end-user satisfaction with service quality.
Hypothesis H5: There is a positive relationship between organizational impact and end-
user satisfaction.
7.2. Structural Equation Modelling (SEM)
SEM is fit for the performing exploratory factor analysis as well as multiple regressions (Ullman
& Bentler, 2003). In this research, variables have been established for the measurement model and
factors are derived. This combination of derived variables is considered as a structured model. Table
8 shows the regression weights and comprises the dependent entities, independent entities, estimate,
S.E., C.R., and P-value. While, Table 9 shows the standardized regression weights which comprise of
dependent entities, independent entities, and estimate. Critical ratio (C.R.) is the ratio of estimate and
standard error (S.E.). The magnitude of the C.R. value greater than 1.9 is considered as the relevant
value. In other words, it can be said that the dependent variable has a relation with the independent
Figure 2. Conceptual model

101
variable between the variables as well as factors. Table 10 shows the covariance of factor 1 and
factor 2. Table 11 shows the correlation between factor 1 (ENDUSER_SATISFACTION) and factor
2 (ORGANIZATIONAL_IMPACT). This table also concludes that factor 1 and 2 are interconnected
with each other and equally proportional to each other. Table 12 and Table 13 show the variances
and squared multiple correlations respectively.
Table 8 provides information about the hypothesis. From the result, it is proved that the conceptual
model can be considered relevant. The goodness of model fit can be determined with Absolute model
fit i.e. the ratio of chi-square/degree of freedom must be less than or equal to 3. Probability level
must be less than 0.05. The output of the SEM provides Chi-square (ᵡ2
) value as 180.741, Degrees
of freedom (DF) is 73. The ratio of these two (Chi-square and Degree of freedom) are obtained as
ᵡ2
/DF is 180.741/73 =2.47, which is highly acceptable and satisfactory. This research satisfies the
probability level as 0.00, the obtained value is lower than the limit probability value.
Correlation in Table 11 shows that the increment or decrement in one factor, then there will be
a change in the related factor with the same magnitude.
Table 8. Regression weights
Dependent Entities Independent Entities Estimate S.E. C.R. P-Value
EMPLOYEE_
PRODUCTIVITY
<---
ORGANIZATIONAL_
IMPACT
.211 .108 1.95 .050
SERVICE_QUALITY <---
ORGANIZATIONAL_
IMPACT
-.152 .138 -1.10 .271
SERVICE_QUALITY <--- ENDUSER_SATISFACTION .736 .283 2.600 .008
EMPLOYEE_
PRODUCTIVITY
<--- ENDUSER_SATISFACTION .742 .285 2.60 .009
EP2EXPFULFIL <---
EMPLOYEE_
PRODUCTIVITY
.911 .180 5.07 ***
EP4BENEFITS <---
EMPLOYEE_
PRODUCTIVITY
.833 .171 4.88 ***
EP1APPRAISAL <---
EMPLOYEE_
PRODUCTIVITY
1.000
SQ2RESPTIME <--- SERVICE_QUALITY 1.190 .311 3.82 ***
SQ1PRODUCT <--- SERVICE_QUALITY .706 .196 3.60 ***
SQ4STAFFAVA <--- SERVICE_QUALITY 1.000
OI2IMPROVE <---
ORGANIZATIONAL_
IMPACT
.725 .125 5.77 ***
OI1BUSSISTRAT <---
ORGANIZATIONAL_
IMPACT
.631 .104 6.06 ***
OI3CHGMGMT <---
ORGANIZATIONAL_
IMPACT
.752 .116 6.48 ***
OI4RETURNS <---
ORGANIZATIONAL_
IMPACT
1.000
USAT3COMM <--- ENDUSER_SATISFACTION 1.000
USAT2INFOFLOW <--- ENDUSER_SATISFACTION 1.048 .300 3.49 ***
USAT1TRAINING <--- ENDUSER_SATISFACTION 1.602 .398 4.02 ***
MANWORK <--- ENDUSER_SATISFACTION .802 .267 3.00 .003

102
From the path analysis, one can say that the factors like organizational impact and user satisfaction
are independent factors. Now, the path co-efficient between these two factors (organization impact
and user satisfaction) is obtained as 0.56 which is significant enough. Similarly, organizational impact
and employee’s productivity is having a path coefficient as 0.31. User satisfaction with employee’s
productivity has a path coefficient as 0.5. User satisfaction with service quality has a path coefficient
as 0.49. Also, the organizational impact is not having any significant effect on the service quality as a
negative path coefficient of -0.16 confirms it. There are 4 paths which have a higher coefficient than
0.3 and that means that those relations are valid. Table 14 shows the validity of Figure 3.
Table 9. Standardized regression weights
Dependent Entities Independent Entities Estimate
EMPLOYEE_PRODUCTIVITY <--- ORGANIZATIONAL_IMPACT .312
SERVICE_QUALITY <--- ORGANIZATIONAL_IMPACT -.161
SERVICE_QUALITY <--- ENDUSER_SATISFACTION .488
EMPLOYEE_PRODUCTIVITY <--- ENDUSER_SATISFACTION .503
EP2EXPFULFIL <--- EMPLOYEE_PRODUCTIVITY .630
EP4BENEFITS <--- EMPLOYEE_PRODUCTIVITY .597
EP1APPRAISAL <--- EMPLOYEE_PRODUCTIVITY .765
SQ2RESPTIME <--- SERVICE_QUALITY .708
SQ1PRODUCT <--- SERVICE_QUALITY .505
SQ4STAFFAVA <--- SERVICE_QUALITY .637
OI2IMPROVE <--- ORGANIZATIONAL_IMPACT .639
OI1BUSSISTRAT <--- ORGANIZATIONAL_IMPACT .673
OI3CHGMGMT <--- ORGANIZATIONAL_IMPACT .730
OI4RETURNS <--- ORGANIZATIONAL_IMPACT .762
USAT3COMM <--- ENDUSER_SATISFACTION .494
USAT2INFOFLOW <--- ENDUSER_SATISFACTION .503
USAT1TRAINING <--- ENDUSER_SATISFACTION .685
MANWORK <--- ENDUSER_SATISFACTION .405
Table 10. Covariance
Factor 1 Factor 2 Estimate S.E. C.R. P-Value
ENDUSER_SATISFACTION <-->
ORGANIZATIONAL_
IMPACT
.117 .040 2.955 .003
Table 11. Correlations
Factor 1 Factor 2 Estimate
ENDUSER_SATISFACTION <--> ORGANIZATIONAL_IMPACT .558

103
Table 12. Variances
Independent Entities Estimate S.E. C.R. P
ORGANIZATIONAL_IMPACT .458 .112 4.071 ***
ENDUSER_SATISFACTION .097 .039 2.473 .013
U15 .100 .038 2.672 .008
U16 .335 .133 2.508 .012
U1 .266 .048 5.603 ***
U2 .264 .045 5.852 ***
U3 .150 .038 3.919 ***
U4 .573 .165 3.481 ***
U5 .591 .100 5.889 ***
U6 .595 .132 4.516 ***
U7 .349 .058 5.989 ***
U8 .220 .038 5.756 ***
U9 .226 .043 5.223 ***
U10 .330 .068 4.826 ***
U11 .299 .048 6.258 ***
U12 .314 .051 6.151 ***
U13 .281 .062 4.555 ***
U17 .318 .048 6.560 ***
Table 13. Squared multiple correlations
Dependent Entities Estimate
SERVICE_QUALITY .176
EMPLOYEE_PRODUCTIVITY .525
MANWORK .164
USAT1TRAINING .469
USAT2INFOFLOW .253
USAT3COMM .244
OI4RETURNS .581
OI3CHGMGMT .534
OI1BUSSISTRAT .453
OI2IMPROVE .408
SQ4STAFFAVA .406
SQ1PRODUCT .255
SQ2RESPTIME .501
EP1APPRAISAL .585
EP4BENEFITS .357
EP2EXPFULFIL .397

104
The result obtained from path analysis are depicted in Figure 3 and concluded below in a
generalised form:
• The better organizational environment has led to improve an employee’s working capacity or
motivation towards work;
• The organization may or may not have a direct relation to the overall service provided to
the consumer;
• Primary users (employees) or end user’s satisfaction can be directly linked to the user’s
productivity it can be explained as that if a worker is not satisfied with the work or working
environment he or she is not going to perform well;
• In case of a power distribution company, it can be concluded that a satisfied primary user provides
better service to the secondary users that are consumers;
• The organizational impact is directly proportional to user satisfaction; it can be said focus on
either of the factor can provide the benefits to the other factor.
Hypotheses are concluded as: Hypothesis 1 – productivity of an employee depends heavily
upon the organizational rules, policies and the way of carrying out their operations. To become
more efficient, an employee must follow and adopt the company’s policies. In this way, one can
understand the relationship between the employee’s productivity and the organizational impact. The
results obtained from the hypothesis shows that there is an existing relationship between these two
variables/factors and the regression weight just confirms it mathematically. Hypothesis 2 – service
quality is a variable more concerned about the services provided to the consumers rather than the
services available within the company for the staffs, employees, and managers. In this regards, one
Table 14. Hypothesis result (unstandardized)
Hypothesis Support Estimate S.E. C.R. P
1 YES
EMPLOYEE_
PRODUCTIVITY
<---
ORGANIZATIONAL_
IMPACT
.211 .108 1.95 .050
2 NO SERVICE_QUALITY <---
ORGANIZATIONAL_
IMPACT
-.152 .138 -1.10 .271
3 YES SERVICE_QUALITY <--- ENDUSER_SATISFACTION .736 .283 2.600 .008
4 YES
EMPLOYEE_
PRODUCTIVITY
<--- ENDUSER_SATISFACTION .742 .285 2.60 .009
5 YES ENDUSER_SATISFACTION <-->
ORGANIZATIONAL_
IMPACT
.117 .040 2.955 .003
Figure 3. SEM output from AMOS (standardized)

105
can identify that there is no relation between organizational impact and service quality. The study also
proves non-existence mathematically through regression weights. Hypothesis 3 – User satisfaction can
be linked to the expectation of the consumers or employees from the organization. If an organization
is more concerned about their end-users than they always try to provide better product or service
quality. So it can be concluded that the user will get satisfied with the products and services provided
by an organization, if they are in line with their desired expectation. Regression weights show that the
relationship is quite positive between the two variables/factors. Hypothesis 4 – Employee’s productivity
may be linked to user satisfaction by the fact that end-users include a portion of employees from the
company. If an end-user is more satisfied, it will be reflected in his performance and eventually, the
productivity will be enhanced. So this relation is also proved positive in the case mentioned as can
be seen from the regression weights. Hypothesis 5 – organizations have more emphasis on the user
satisfaction i.e. satisfaction of employees and the consumers.
8. RESULTS
Six factors were identified from rotated component matrix using factor analysis. After group formation
of factors named as organizational impact, user satisfaction, employee productivity and service quality.
Four factors out of six factors were selected for the study purpose, remaining two factors viz. manual
work reduction and new technology were not supported either because of the low positive response
given to them or due to the algorithms of principle component analysis.
The result of hypothesis proved that the conceptual model is relevant and validated. The goodness
of model fit can be determined with absolute model fit, hence will be acceptable for all industries.
The result of the path analysis depicts the relationship among grouped factors. Emphasising on
these factors an industry can ensure the success of ERP system after its implementation. Relationship
between organization impact and user satisfaction is significant. User satisfaction with employee’s
productivity has also significant relationship. Organizational impact is not having any significant
relationship with service quality, though this statement does not follow a common tendency. Probably
improper response of respondents may be the reason of this relationship. The same may be explored
further study.
New policies within an organization can have both a positive or negative impact on the end-
users. If the organization tries to provide better services to its end-users, then it would reflect on
its productivity and this could be seen from their satisfaction. As the satisfaction level of end-user
increases the productivity of an organization proportionally increases and wise-versa. Hence it may
be stated that two variables/factors are interlinked, relationship has been proven mathematically.
9. CONCLUSION
The result of this research work will emphasize towards the variables identified mainly influencing
post-ERP implementation phase. In this research article, variables for employee’s productivity and
service quality after ERP implementation with KM have been identified, focusing on which employee’s
productivity and service quality may be enhanced.
An exploratory factor analysis among variables related to employee’s productivity and service
quality after ERP implementation is performed which will be used to reduce the data to a smaller set
of summery variables and to explore the underlying theoretical structure of the phenomena. It will
also be used to identify the relationship between the variables and respondents.
A conceptual model/ framework to enhance employee’s productivity and service quality after
ERP implementation which will support the industries, it provides a graphical representation of
cause and effect relationship within an organization. It will be useful to evaluate the organizations
performance, eventually it will support/assist/help to enhance the employee’s productivity and service
quality after ERP implementation.

106
The proposed model may be applicable in all sectors with proper identification of variables related
to employee’s productivity and service quality after ERP implementation with KM in an organization.
An exhaustive list of factors comprising important variables are illustrated in this article; it
will contribute to academia. Focusing on these four vital factors an industry may ensure the success
of ERP implementation. Hence, the article contributes to industrial people. The study confirmed
that organizational impact, user satisfaction, employee productivity, and service quality delivers a
positive impact on post-ERP implementation. This research will be useful for developing countries
with similar or dissimilar cultural environment, economic and political environments. Data are
collected and analyzed from the power distribution company of central India which has already
implemented ERP systems. The outcome of the study may be used in a generalized form for all
sectors of developing countries.
KM supports the post-ERP implementation by acquiring the data/problem from the consumers,
after acquisition of the data the problem can be stored in database. Knowledge management is the
systematic management of an organization’s knowledge assets for the purpose of creating value and
meeting tactical & strategic requirements; it consists of the initiatives, processes, strategies, and systems
that sustain and enhance the storage, assessment, sharing, refinement, and creation of knowledge.
The data or problem can easily transfer to the concern department and rectification/modification/
alteration can be done. KM also supports the future rectification if the same problem raised. Through
the use of KM, the consumers or the employees can easily access the previously raised problems and
easily rectify the same problem.
The research was performed among the employees and consumers of power distribution companies
in central India which are either directly or indirectly associated with the use of the IT system like
ERP system. The data were collected from power distribution companies for study purpose, the
framework prepared in this study will be equally applicable for other sectors also, hence, the result
may be generalized.
The study provides a comprehensive list of variables to ensure the success of ERP implementation.
Researchers of the field may directly use this list to enrich the work. Factors comprising of variables
are prepared, emphasising on these factors only the industries which are seeking to implement or
have already been implemented the ERP system will be benefited.
In the future, the same study may be done in other sectors like the manufacturing sector and
service sectors. Larger sample size will provide a more precise and generalized result, hence the
model will be acceptable for all sectors.

107
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Animesh Agrawal is a research scholar (Ph.D. Scholar) in Mechanical Engineering Department, NIT Raipur,
India. His main area of interest is knowledge management, optimization, and ERP. Currently he is working in the
collaboration of knowledge management with different fields like artificial intelligence, artificial neural networks
etc. He has presented more than 6 papers in highly indexed journal attended more than 14 international and
national conferences.
Hemant Kumar Diwakar is a full-time M.Tech. research scholar in the Department of Mechanical Engineering,
National Institute of Technology Raipur, India. His area of interest is optimization technique, Enterprise Resource
Planning, knowledge management, business management, etc.
Suraj Kumar Mukti (PhD) is working as assistant professor in the department of mechanical engineering, NIT
Raipur. His area of interest is Industrial Engineering & Management, Enterprise Resource Planning, Management
Information System, Knowledge Management, Change Management, Customer Relationship Management,
Production Engineering & Technology. He has presented more than 14 papers in reputed journal and attended
more than 15 international and national conferences.
APPENDIX
This study is a part of large research project to learn about post ERP implementation with support of
KM. You are requested to give your individual perceptions against the following questions. Needless
to say we would be very happy to share with you, the result of this study without disclosing the details
of other participating individuals or organizations.
Table 15. Questionnaire for the identification of factors
Please Indicate the Extent to Which You Agree With the Following Statements
Responses
Strongly
Disagree
Disagree Neutral Agree
Strongly
Agree
Q.1 Rate the timely information provided by the IT system/software you use?
Q. 2 Rate what rating did you receive from your supervisor on your most recent
performance appraisal on your current job this year?
Q.3 Rate the fulfilment of your expectation from the IT system you use?
Q.4 Rate how much the manual work has been reduced in the past three years.
Q.5 At your current designation does the management tend to seek some feedback or
ideas in decision making for the company? Rate how often.
Q.6 Rate up to what extent the company’s recent decisions affect your work.
Q.7 Rate the scope for future of product variety/process improvement in the
upcoming years within the company?
Q.8 Rate the revenue growth/ return on assets for the company in the last few years?
Q.9 Rate the satisfaction of the information flow within the organization?
Q.10 Rate the training/instructions provided on the ERP system
Q. 11 Rate the overall satisfaction with the IT system installed within the company
helping in interdepartmental communication.
Q.12 Rate the quality of power supply (in terms of voltage fluctuations) in your
Local area?
Q.13 Rate the responsiveness provided in case of transformer problem.
Q.14 Rate the availability of staff for registering complaints & queries?
Q.15 Rate How comfortable is the new billing (online) system to you?

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