THE IMPACT OF ACCOUNTING SOFTWARE ON BUSINESS PERFORMANCE

Abstract

The objective of this paper is to investigate and explore the impact of Accounting Software on business performance of Malaysian firms. The study discusses and explores the effects of using AIS on the firm's performance. The result of this study is expected to help the firm's owners and manager in understanding the importance of using Accounting Information System (AIS) derived from Accounting Software to achieve performance. Several characteristics such as: efficiency, reliability, ease of use, data quality and accuracy influenced the use of AIS, thereby affecting the performance of firms. Modern literature and the result of this study shows that these AIS characteristics possessed by the accounting information such as: efficiency, reliability, ease of use, data quality and accuracy have significant effects on the use of AIS and firm's performance. Previous researches have shown that it is crucial for firms to use AIS to ensure the survival and sustainability of business in the increasingly competitive environment besides enhancing their business operations competency and efficiency. This study is one of few that could shed light on how the use of AIS affects the performance of firms. In this study, the authors propose that dimensions of using AIS are important for improve the performance of business organizations.

Full text

ISSN: 22897615
Page 1
International Journal of Information System and Engineering
www.ftms.edu.my/journals/index.php/journals/ijise
Vol. 6 (No.1), April, 2018
ISSN: 2289-7615
DOI: 10.24924/ijise/2018.04/v6.iss1/01.26
This work is licensed under a
Creative Commons Attribution 4.0 International License.
Research Paper
THE IMPACT OF ACCOUNTING SOFTWARE ON BUSINESS PERFORMANCE
Yvonne Chong
FTMS College Malaysia
yvonc80@gmail.com
Ismail Nizam, PhD
Senior Lecturer
FTMS College Malaysia
nizam@ftms.edu.my
Abstract
The objective of this paper is to investigate and explore the impact of Accounting Software on
business performance of Malaysian firms. The study discusses and explores the effects of
using AIS on the firm’s performance. The result of this study is expected to help the firm’s
owners and manager in understanding the importance of using Accounting Information
System (AIS) derived from Accounting Software to achieve performance. Several
characteristics such as: efficiency, reliability, ease of use, data quality and accuracy
influenced the use of AIS, thereby affecting the performance of firms. Modern literature and
the result of this study shows that these AIS characteristics possessed by the accounting
information such as: efficiency, reliability, ease of use, data quality and accuracy have
significant effects on the use of AIS and firm’s performance. Previous researches have shown
that it is crucial for firms to use AIS to ensure the survival and sustainability of business in
the increasingly competitive environment besides enhancing their business operations
competency and efficiency. This study is one of few that could shed light on how the use of AIS
affects the performance of firms. In this study, the authors propose that dimensions of using
AIS are important for improve the performance of business organizations.
Keywords: Accounting Software, Accounting Information System, and
Business Performance

ISSN: 22897615
Page 2
1.0 INTRODUCTION
1.1 Background of the Research
Today, accounting has come to seize a significant position in its functions in the
business world. Accounting plays a critical role in the operation of an organization.
For every business, it is important that the financial information of the business
activities is being kept up-to-date and monitored by the organization. Accounting
involves various processes, ranging from simple to complex and even burdensome.
Companies need to keep pace with constant changes in information technology in
order to maintain a highly accurate and up-to-date accounting, statutory records
and inventory (Igbaria, et al., 1997). Due to complexity of the accounting system
increasing vulnerability to errors and the swelling volume of accounting
transactions, a system that could process and store accounting data with increased
speed, vast storage and processing capacity was necessitated. To satisfy the
increase demand for up-to-date and accurate information, accounting software, an
integration of accounting and information technology was introduced to the world
(Wickramsainghe, et al., 2017).
Many past researches have shown that accounting information system adoption
does increased firm’s performance, profitability and operations efficiency in
Malaysia, Finland, Spain, Iran and Pakistan (Saira et al., 2010; Gullkvist, 2002;
Sajady et al.,2008; Kouser et al., 2011). New computer tools and information
society enabled the companies in Turkey to effectively use their accounting system
in dealing with customers and suppliers (Esmeray, 2016). Cragg et al. (2002) and
Chan et al. (1997) found that alignment of IT strategy with business strategy
increased firm’s performance. In an earlier study, Raymond et al.(1995) found that
firms perform better with alignment of organisational structure and IT structure.
There has no research been conducted in Malaysia to evaluate the impacts of using
accounting software on the business performance of firms in Malaysia? Sam et al.
(2012) did a research on the adoption of computerized accounting system in small
medium enterprises in Melaka. However, there was no published research to
explore the impacts of accounting software characteristic on the business
performance.
1.2 Research Rationale
Accounting information systems (AIS) coupled by improved speed, accuracy and
reliability and with good corporate governance practices lead to good firm
performance (Ahmad, 2016; Hla and Teru, 2015). Literature has studied the firm
performance and AIS alignment but few studies have looked at the relationship
between characteristic of accounting software and firm performance. While the
impact of corporate governance and strategic decision making on firm
performance has been widely studied, the role of AIS in this relationship is remain
unclear. Since AIS serves as the firm’s business information infrastructure
backbone, both transparency of corporate governance and strategic decision-
making process depend on information obtained from the system. AIS with
features error-free, user-friendly, easy to learn, technically sound, well
documented and flexible are indirectly related to organization performance.

ISSN: 22897615
Page 3
Accounting information system is of immense significance to both businesses and
organization according to Hla and Teru (2015) as it facilitates management
decision making, quality of the financial report, internal controls, the company’s
transaction besides playing a crucial role in economic system. The study asserts
that businesses and organization should implement AIS because an efficient AIS
yield sufficient accounting information which is critical for effective decision
making and also ensures adequate, pertinent and true information are available to
all levels of management to plan and control business activities. IS application
impacted the organization in form of the benefits received. Through business
performance, the impact of IT on organization is discerned (Brynjolfsson and Hitt,
1996 & 2000; Osei-Bryson and Ko, 2004) leading to business value (Lee, 2001).
An optimal implementation of AIS by firms means adapting more successfully to a
changing environment and shows a high degree of competitiveness, thus
enhancing the dynamic character of a company through assimilation use of AIS. In
other words, there are improvements in administrative management regarding
accountancy and finance. By using AIS, it is possible to gauge the risk of some
operations or predict future earnings with sophisticated statistical software
applications.
The purpose of this research paper is to analyse the influence of the accounting
software characteristic on the firm’s performance. The results of this paper will
assist software developer to come up with new software that suit with user’s need
and also benefits the firms in acquiring the appropriate accounting software (Musa
and Ahmad, 2004).
1.3 Research Aim and Objectives
The major purpose of this study is to assess the impact of using accounting
software system on business performance of firms in Malaysia.
The specific objectives therefore are:
(1) To investigate the impact of Software Efficiency on the Performance of
Business
(2) To investigate the impact of Software Reliability on the Performance of
Business
(3) To investigate the impact of Software Ease of Use on the Performance of
Business
(4) To investigate the impact of Software Data Quality on the Performance of
Business
(5) To investigate the impact of Software Accuracy on the Performance of
Business

ISSN: 22897615
Page 4
1.4 Research Questions
In order to address the research problem, the following questions would be
administered:
(1) What is the impact of Software Efficiency on the Performance of Business?
(2) What is the impact of Software Reliability on the Performance of Business?
(3) What is the impact of Software Ease of Use on the Performance of
Business?
(4) What is the impact of Software Data Quality on the Performance of
Business?
(5) What is the impact of Software Accuracy on the Performance of Business?
2.0 LITERETURE REVIEW
Urquía Grande, E., Pérez Estébanez, R. and Muñoz Colomina, C., (2011) researched
on the relationship between the use of the Accounting Information Systems (AIS)
by the Small and Medium Sized Enterprises (SMEs) in Spain on variables Economic
Returns, Financial Profitability and Productivity based on empirical evidence. The
study conducted using survey carried out among small and medium-sized firms to
determine the extent that development and implementation of accounting
information systems had taken place, and subsequently analysed using ANOVA
analysis to ascertain the impact of introduction of AIS on firm’s performance and
productivity. The findings provided evidence that there is a positive relationship
among the SMEs that use AIS for fiscal and bank management and better
performance measures and subsequently provides value added in accounting
literature given the scarcity of works dealing with the relationship between the
application and use of AIS and performance and productivity indicators in SMEs in
Spain.
Ali, Omar and Bakar (2016) investigated the effect of Accounting Information
System (AIS) on organizational performance and the moderating effect of
organizational culture in the relationship between AIS success factors and
organizational performance in Jordanian banking sector. The study used four types
of AIS success factors as the determinants performance namely service quality;
information quality, data quality and system quality. Data were collected with a
structured questionnaire survey and analysed with PLS SEM technique. The
findings revealed that service quality, information quality and system quality are
the significant AIS success factors for increasing organizational performance. This
study also evidenced that organizational culture helps increase performance by
interacting with information quality, data quality and system quality. It can be
inferred from this study that organizations involved in banking sectors can
increase their performance by adopting and implementing AIS success factors
along with practicing favourable organizational culture. Therefore, firms should
cultivate a favourable environment so that employees feel happy which motivates
them to work more devotedly with the organizations.
The study by Ismail and King (2005) focused on measuring the alignment of
accounting information systems (AIS) requirements with AIS capacity and then
investigating whether this AIS alignment is linked to firm performance. The study
was conducted using a mail questionnaire on nineteen accounting information

ISSN: 22897615
Page 5
characteristics, data from 310 Small and Medium Sized Enterprises (SMEs) firms in
Malaysia was collected and analysed using principal component analysis and
varimax rotation with Kaiser normalization. The results indicated that a significant
proportion of Malaysian SMEs had achieved high AIS alignment.
Furthermore, the group of SMEs with high AIS alignment had achieved better
organisational performance than firms with low AIS alignment. The findings
provided evidence of the importance of AIS alignment and deepened current
understanding of the requirements for accounting information and te use of IT as
an important information processing mechanism. More importantly, it opens up
possibilities for further study of AIS alignment in SMEs, both in Malaysia and on a
global basis.
Ismail and King (2014) researched on the factors that affect the use of accounting
information systems in factories, small and medium-sized Malaysian
manufacturing firms with sample consisted of 214 companies that have accounting
systems. The study also found out that the information systems of accounting work
smoothly as they connect information from the top and bottom that help workers
in companies to achieve their goals, in addition using these systems will enable
companies to give accurate information to the relevant government agencies.
2.1 Literature Gap
Prior researches have shown that accounting information system adoption does
increased firm’s performance, profitability and operations efficiency in Malaysia,
Spain, Finland, Pakistan and Iran (Gullkvist, B., 2002; Kouser et al., 2011; Sajady et
al., 2008). In United Arab Emirates (UAE) information society and the new
computer tools have allowed the companies to make better use of their accounting
system in their relations with suppliers and customers. In the same way the
development of the AIS and electronic banking allows the companies to save a lot
of time in their transaction (www.ameinfo.com). Thus, the present study attempts
to provide some clarification of the relationship between AIS design,
organizational strategy and performance especially on financial performance and
performance management.
From the literature reviewed above, many studies have been conducted on the
impact of computerized accounting systems on efficiency, effectiveness and
performance of accounting functions. However, but there are little research
conducted on the impact of computerized accounting system on business
performance of firms in Malaysia. Therefore, this study aims to fill in that
knowledge gap.

ISSN: 22897615
Page 6
2.2 Conceptual Framework
Characteristics of
Accounting
Software
Efficiency
Reliability
Ease of Use
Data Quality
Accuracy
Independent Variables
Dependent Variable
Business
Performance
Figure 1: Conceptual Framework by the researcher
2.2.1 Efficiency and Business Performance
Accounting information system as a vital organizational mechanism is critical for
effectiveness of decision management and control in organizations. An effective
accounting information system as noted by Wilkinson et al., (2000) performs
several key functions such as data collection and maintenance, accounting systems
and knowledge management, data control and information generation. There are
many factors that affect the efficiency and effectiveness of accounting information
systems. Thus, the accounting information systems combined the factors qualified
human resources, best software and hardware and data base quality to be
effective.
Every business has pre-dominant goals to improve on performance and to
maximize shareholder wealth. In order to achieve these objectives, right plans
together with necessary resources are needed and used for implementation.
Efficiency becomes very important considering the fact that resources are scarce.
Efficiency in business context refers to ability of firm to maximize firm value by
using the least inputs to achieve higher outputs. Empirical studies have reinforced
the need for efficiency in the operations of the firm. Similarly, Greene and Segal
(2004) show that efficiency increase profitability of the firms in terms of return on
equity in the insurance industry. There is a strong positive link between the firm’s
value and the utilisation of the firm’s resources.
Therefore, we formulate the following hypothesis (see also Figure 1);
H1: There is a significant positive impact of Software Efficiency on Business
Performance

ISSN: 22897615
Page 7
2.2.2 Reliability and Business Performance
Accounting software produced reliability data that are critically used to plan,
identify, and control business operations. As an essential characteristic for
accounting information, reliability represents the extent to which the information
is unbiased, free from error, and representationally faithful making it useful for
decision making. Reliability is a complex and elusive construct of accounting
information despite its central role (Maines and Wahlen, 2006). To achieve
accounting standards, firms are required to provide more unabridged revelations
associated to the underlying economic constructs represented by accounting
information to help users’ better determine the reliability of accounting
information.
Reliability is ingrained in the information itself, and not in the manipulation of the
information. The relevance of measurement attributes and economic constructs
depicted by accounting information is a prerequisite for reliability to matter.
Hence, reliability is an imperative but inadequate for accounting information to be
functional. Reliability of accounting information determined by the requirement of
accounting standards and facilitates firms to render economic constructs with
pertinent informative accounting measurements and classifications. Thus, the
usefulness of accounting information in predicting future cash flows depends on a
number of factors such as accounting information reliability, the extent to which
accounting constructs and measured values depict economic constructs without
error or bias (Cho, Kim and Lim, 2006).
Chandar and Bricker (2002) in closed-end mutual funds predict and find that
returns to market-wide portfolios S&P 500 Index and the Russell 2000 Index
provide useful information to enable users to gauge the reliability of these funds’
estimates of fair value gains and losses. Similarly, prior studies by Sunder and
Waymire (1983), Casler and Hall (1985) and Shriver (1986, 1987) test reliability
across measurement attributes under SFAS 33 using different industry-wide and
economy-wide price-level indexes and baskets of assets. Various measures of
historic stock return volatility used by Alford and Boatsman (1995) in testing the
reliability of estimates of expected future return volatility to estimate fair values of
firms’ stock option-based compensation. The differences in these expected return
volatility estimates triggering differences in the degree of reliability in estimates of
options-based compensation expense. These studies imply that by revealing
independent and verifiable benchmark data for underlying economic constructs
make reliability more transparent and enhanced, thus enable financial statement
users to gauge the reliability of reported accounting estimates for stock options,
fair values, pension and other post-employment benefit obligations, loss reserves,
and others.
Therefore, we formulate the following hypothesis (see also Figure 1);
H2: There is a significant positive impact of Software Reliability on Business
Performance
2.2.3 Ease of Use and Business Performance
The quality of software which is ease of use and understandable, would be
beneficial to its user. Therefore, the success of the system used depends on the

ISSN: 22897615
Page 8
level of ease-of-use of the system. In general, an increase in ease of use positively
influence several aspects of a company's output quality such as increased sales and
revenues , productivity and customer satisfaction; reduced training and support
cost, development time and costs and maintenance costs (Bias and Mayhew, 2005).
Landauer (1996) asserts that increasing ease of use in the workplace raises several
responses from employees such as "Workers who enjoy their work do it better,
stay longer in the face of temptation, and contribute ideas and enthusiasm to the
evolution of enhanced productivity”. Companies create standards by implementing
experimental design techniques that create baseline levels. In an office
environment, concerned areas include working posture, design of workstations,
office environment and organization issues, input devices, screen displays, and
software interface (Celine, 2008). By improving the said factors, firms can attain
their goals with increased output at lower costs besides potentially produce
optimum customer satisfaction level. There are numerous bases for correlation of
these factors to overall improvement. For instance, easy to understand user
interfaces software reduces the requirement for extensive training, improved
interface tends to reduce the time needed to accomplish tasks, increase the
productivity levels for employees and reduces development time and costs. Each
factor is not mutually exclusive but rather they should be working in conjunction
to form the overall workplace environment.
Usability classifies by Gould (1988) relating to the system performance, system
functions and user interface (Dubey et al, 2010). System quality do not directly
impact toward organisation performance but if system is technically sound, error-
free, easy to learn, user-friendly, well documented and flexible those features are
indirectly related to organization performance.
Therefore, we formulate the following hypothesis (see also Figure 1);
H3: There is a significant positive impact of Software Ease of Use on Business
Performance
2.2.4 Data Quality and Business Performance
Output of accounting information system (AIS) very much depends on the data
quality as poor data quality will result in garbage in garbage out (Xu, 2003).
Through evaluation of accounting information quality using four attribute namely
accuracy, timeliness, completeness, and consistency, Xu (2003) concluded that
data quality is critical AIS, thus influencing the firm’s performance. To achieve
high data quality, the process of data production such as data collection, data
utilization, and data storage must work satisfactorily (Lee and Strong 2003).
According to (Xu, 2009), the competitiveness of firms would be damaged by
incomplete and inaccurate data as input control and employee competencies are
important to data quality of accounting information system. Huang, Lee, and
Wang (1999) found that data of inferior quality may cause adverse effect on
decision making and consequently affect firm’s performance.
Several researches focus on success factors and critical process factors that
frequently influence the information systems as well as AIS. Wixom & Watson
(2001), data quality as one of the critical factor is relevant directly to effort for

ISSN: 22897615
Page 9
decision making. In this information era, proactive approach to data quality
management is required to enhance reliance of the organization on (AIS) to
achieve their mission (Al-Hakim, 2007). Information and data are described as
critical components for all the activities of every human venture (Emeka-Nwokeji,
2012). Literature has been made for determining and handling critical success
factors at data quality management level as noticed by Xu (2003). Nevertheless,
there have been few attempts to identify the critical data quality measures in the
context of AIS causing data quality in AIS to remain largely unexplored. Emeka-
Nwokeji (2012) concluded that data quality is significant for the success of AIS as
the quality of data is assured for enhancing firm’s performance. Data quality
management policies aid companies in proactively responding and supplying
services and products required by the customers, and appropriate processes of
decision and operation. Few studies have scrutinized the effect of the AIS’ data
quality as critical success factor on organisational performance. Al_Qudah and
Shukeri (2014) noted data quality is strongly related and positively impacts the
perception of company internal auditors. More scientific studies on found data
quality and AIS performance are strongly related (Emeka-Nwokeji, 2012).
Therefore, we formulate the following hypothesis (see also Figure 1);
H4: There is a significant positive impact of Software Data Quality on Business
Performance
2.2.5 Accuracy and business performance
Accounting software is not only speedy but also accurate and reliable. Computers
are being used to collect data and process into meaningful information that
management use to make timely and effective decisions and the entire data
processing are carried out using accounting software through sorting, classifying
calculating, summarizing the data and production of reports.
Information systems processing and production processing in manufacturing
organizations are homogeneous. Firm will lose business when customers (users)
will be dissatisfied if the product (information) is not delivered on time
(timeliness) and the product (information) does not conform to the needs
(relevance) of customers (users) (Clikeman, 1999). Non conformation of
information provided by an IS leads to disruption of operations in the organization
and hefty maintenance costs, ensuing in high costs to the organization. When the
operational information from accounting software is highly accurate, firm will
benefit from reduced labour costs and waste, effective use of machinery, and low
inventory costs. Thus, information content with high accuracy, completeness and
relevancy leads to better product cost control and increase organizational
efficiency through increased profit margin and decision making efficiency.
Ravichandran and Rai (2000) assert by leveraging IT correctly, internal
operational efficiencies of organization can be achieve through efficiently and
effectively manage of internal resources and improve customer service to attain
strategic advantages. Thus, accuracy of accounting software is highly important to
organizations in terms of benefit through value derived by providing accurate and
timely information to improved decision making, identifying possible future
business avenues and profitable ventures to. Dependency on information systems

ISSN: 22897615
Page 10
and losses associated with accuracy of information urge management to improving
IS quality deliberately (Ravichandran and Rai, 2000).
The information is accurate and credible when it does not contain significant
errors, it is not biased, and users can trust that it accurately represents what it has
set out to be or what they expected to. The information must represent accuracy of
the transactions and other events that are intended to be represented and is
reasonably expected to represent them. With the aid of the software, accountants
tend to improve the overall accuracy of their record thus eliminating or reducing
human error. Accounting involves a lot more of math calculations done by hand. A
little mathematical error in the computation at the beginning of this process can
significantly affect the result. But accountants can process data accurately using
accounting software to provide complete, accurate and timely information outputs
for decision making in driving business efficiency and growth. Accuracy of financial
data is consistency and efficiency driver across the entire organization enhancing
the company’s performance and the achievement of key business goals,
operationally and financially.
Therefore, we formulate the following hypothesis (see also Figure 1);
H5: There is a significant positive impact of Software Accuracy on Business
Performance
3.0 RESEARCH METHODOLOGY
This section discussed about the research design and methodology
employed to conduct this research.
3.1 Research Paradigm
This study was guided by the positivism paradigm. Positivism is based on values of
reason and validity and focuses on facts garnered from theories and which can be
measured empirically using quantitative methods. The positivist research
approach assumes that only knowledge that is based on observations of the
external reality is valid (Phillips & Burbules, 2000). Positivism also assumes that
theories can explicate the relationship between dependent and independent
variables and developed hypothesis to be tested and forecast the outcomes of the
study. Therefore, the impacts of accounting software characteristics had on the
business performance in Malaysia firms were able to explore by using positivism
paradigm.
3.2 Research Design
Explanatory research is concerned with determining the causal relationship
between variables (Zikmund & Babin, 2010).
The purpose of this research is more on seek new insights and determining the
causal relationship between variables based on exploratory and explanatory. It is
exploratory because researcher is exploring the relationship of independent and
dependent variables in an unstructured and informal manner collection of
information and explanatory because researcher explained the impact of
dependent and independent variables (Hair, Rabin, Money and Samouel, 2003).

ISSN: 22897615
Page 11
Hence, the research was conducted using combination of exploratory and
explanatory research design.
3.3 Research Method
In this study, quantitative research approach was adopted as it allows collecting
more data to investigate the facts, testing theories and hypotheses. As a result,
quantitative is more suitable to represent tested theories comparing qualitative
method since researcher was unaware of essential variables to analyse and the
sensitivity of data collection relating to the social settings. Using this method
allows researcher to streamline research problem to restricted variables and
supports to understand the result of analyst (Creswell, 2003). This type of research
is more objective and reliable as the researcher cannot interpret the facts of the
research based on biased opinion.
3.4 Research Instrument
The research used close – ended questions in order to source information from
responses. The questionnaire was structured in a five point likert format to extract
the data or information.
A questionnaire is a set of questions which are usually sent to the selected
respondents to answer at their own convenient time and return back the filled
questionnaire to the researcher. In this study, questionnaires were used to collect
information from respondents. The reason for using questionnaires is because
they cover large sample at low cost, and gives respondents adequate time to give
well thought-out answers.
3.4.1 Data Collection Method
A total 150 questionnaires were distributed, 100 returned and out of 100, 22
questionnaires were rejected due suspicious answers. This research used cross-
sectional data collection technique which is a form of quantitative sampling
methods used in explanatory research (Brady and Johnson, 2008).
The sampling technique used in this research was random probability sampling
method which is popular among researchers as this technique eliminates
prejudices by allowing the results to exceed depending on sampling population. On
the contrary, respondents are selected by researcher in non-probability sampling
method may have impact on validity of the results.
3.5 Data Analysis Plan
Data analysis is very crucial path of any research involving process of inspecting,
cleansing, transforming, interpreting and modeling data with the goal of
discovering useful information to support the hypothesis underlying each question
(Judd & McClelland, 1989) depending on nature of the sampling, measurement and
data collecting method. Quantitative statistical analysis for questionnaire was
performed using Statistical Package for Social Sciences (SPSS) (IBM Corp, 2015)
due to its effectiveness in managing data with wide range of options, generate
better results and is specifically designed to analyse statistical data (Green and
Salkind, 2010).
In determining the objective of this research, statistical procedures are conducted
using Statistical package for social science (SPSS) to measure accuracy of the data.

ISSN: 22897615
Page 12
It helps to analyse data more effectively and efficiently. Five statistical methods
namely Descriptive Analysis, Normality Analysis, Reliability Analysis, Exploratory
Factor Analysis and Multiple Regression were employed to examine data for this
study.
4.0 ANALYSIS RESULTS AND FINDINGS
Section four presents data analysis, interpretation of results and hypotheses
testing for this study.
4.1 Demographic Analysis
The table below showed the distribution of demographics for the respondents.
37.2% respondents were males and 62.8% respondents were female. The collected
data revealed that more female workforce is using accounting software at their
workplace. A total of 52 respondents have been using Accounting software for
more than 7 years. The demographic profile also showed that all firms (100%)
adopted computerized system for their business and a significant number 98.7%
used computerized system to record data. Total of twenty-seven (27) respondents
were working with the same firm for 5 years, fifteen (15) respondents below 15
years and eighteen (18) respondents each were working below ten (10) years and
more than fifteen years respectively. In Malaysia, the most adopted software
package was SAP with twenty-one (21) firms using it for their business, followed
by Autocount (18) and Sage (14) while the least adopted software package was
Peachtree with only three (3) firms using it according to this study.
Table 1: Demographics of the study
Variables Measures Frequency Percent Valid Percent Cumulative Percent
Gender Male 29 35.8 37.2 37.2
Female 49 60.5 62.8 100.0
Experience in Using Accounting Software 0 - 2 5 6.2 6.4 6.4
3 - 4 7 8.6 9.0 15.4
5 - 6 14 17.3 17.9 33.3
Over 7 52 64.2 66.7 100.0
Years of Service in current firm Up to 5 27 33.3 34.6 34.6
6 – 10 18 22.2 23.1 57.7
11 – 15 15 18.5 19.2 76.9
Over 15 18 22.2 23.1 100.0
Firm used Computerised System? Yes 78 96.3 100.0 100.0
Firm used Computerised Data Recording? Yes 77 95.1 98.7 98.7
No 1 1.2 1.3 100.0
Software Package used by current firm Sage 14 17.3 17.9 17.9
Quick Books 5 6.2 6.4 24.4
SAP 21 25.9 26.9 51.3
Peachtree 3 3.7 3.8 55.1
Autocount 18 22.2 23.1 78.2
Others 17 21.0 21.8 100.0
Demographic Statistics
4.2 Descriptive Analysis and Normality Analysis
This research comprise of five independent variables and one dependent variable.
The dependent variable is business performance which is measured using
efficiency, reliability, ease of use, data quality, and accuracy. The descriptive
statistics analysis table below showed that R, DQ and EOU were found to affecting
BP with the mean value of 4.39, 4.79 and 4.33 each and standard deviation of

ISSN: 22897615
Page 13
0.631, 0.578 and 0.605. A and Ef mean value are 4.14 and 4.11 respectively with
standard deviation 0.708 and 0.642 are also two of the important independent
variables that least affects BP (See Table 2). All the means and standard deviation
are ±1 shows that the data set is good.
Table 2: Descriptive and Normality statistics generate from SPSS
N Minimum Maximum Mean
Std.
Deviation
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Business Performance (BP) 78 3 5 4.05 .597 .015 .272 -.593 .538
Accuracy (A) 78 2 5 4.14 .708 -.821 .272 .091 .538
Reliability (R) 78 3 5 4.39 .631 -.791 .272 -.443 .538
Efficiency (Ef) 78 2 5 4.11 .642 -.662 .272 .183 .538
Ease of Use (EOU) 78 3 5 4.33 .605 -.610 .272 -.392 .538
Data Quality (DQ) 78 3 5 4.37 .578 -.792 .272 .236 .538
Valid N (listwise) 78
Descriptive Statistics
Skewness
Kurtosis
The above table shows the five (5) independent variables (A, R, Ef, EOU, DQ) are
negative values and the dependent variables BP show positive values. The
independent variable A were highly skewed with negative skewness value -0.821
indicates a long left tail and a significant sharp-rising center peak compared to
normal distribution and positive Kurtosis value of 0.91 indicates a leptokurtic
distribution with a fatter tails. The independent DQ and Ef were also highly skewed
with each skewness value -0.792 and -0.662 while Kurtosis value 0.236 and 0.183
indicates a long left tail and fatter tail similar to A variable. The independent
variable R and EOU were also another highly skewed distribution with skewness
value -0.791 and -0.610 while Kurtosis value -0.443 and -0.392 respectively
indicates similar long left tail but a platykurtic distribution with thinner tail.
Dependent variable BP shown above were positively skew with the skewness
value 0.015 and negative Kurtosis value -0.593 indicates a long right tail and the
central peak is higher and sharper than the normal distribution (Leptokurtic). The
skewness and Kurtosis value were within ±1 indicates the distribution for this
study is normal (Hair et al., 2010).
4.3 Reliability Analysis
Reliability is a test helps to ensure the overall consistency of measures. A general
accepted rules describing internal consistency using Cronbach's alpha is as
follows: Excellent ≥ 0.9 ; Good ≥ 0.8; Acceptable ≥ 0.7; Questionable ≥ 0.6; Poor ≥
0.5; Unacceptable ≤ 0.5 which may vary depending on the sample size (DeVellis,
2012; Hair et al., 2010). Pilot study was conducted prior to the main study
enhanced the likelihood of success of the main study and potentially help to avoid
failure in main studies (Van Teijlingen and Hundley, 2001). The Table 3 shows all
constructs in this research are reliable where the values are ranged between 0.80-
0.90. Variables R and BP were excellently reliable to conduct the study with
Cronbach’s alpha figure above 0.90. Similarly, reliability of Ef, EOU, DQ and A were
in good state with Cronbach’s alpha above 0.80 to conduct this research. All the
value of Cronbach’s alpha coefficients met with requirements of reliability to
proceed with further analysis.

ISSN: 22897615
Page 14
Table 3: Cronbach’s alpha coefficients (SPSS)
Constructs Number of Items Cronbach's Alpha
All Variables 42 0.975
Efficiency 7 0.879
Reliability 9 0.924
Ease of Use 6 0.821
Data Quality 6 0.857
Accuracy 7 0.858
Business Performance 7 0.911
4.4 Exploratory Factor Analysis (EFA)
Exploratory factor analysis was conducted after confirmed that the data was
suitable for factor analysis, with principal components analysis (PCA) as the
method of factor extraction, to identify the underlying factor structure of the
accounting software characteristic and business performance. The PCA
summarizes the interrelationships among the original variables in smaller set of
underlying dimensions. As in this case it is used when the researcher has no pre-
existing knowledge about the factors that may explain the correlations between a
set of variables.
4.4.1 Correlation Matrix and Anti-Image Matrix
The original 42 variables were subjected to principal component analysis and
orthogonal varimax rotation with Kaiser Normalization. The suitability of data for
factor analysis was assessed prior to performing the EFA. Inspection of the
correlation matrix revealed the presence of many coefficients of 0.3 and above. The
correlation matrix produced a significant number of large correlations suggesting
that factor analysis is a relevant statistical methodology. Moreover, each of the
diagonals of the anti-image correlation matrix was above 0.7.
4.4.2 Sample Adequacy Analysis
The Kaiser–Meyer–Olkin (KMO) test and Bartlett test of Sphericity were
undertaken. These tests are used to establish the adequacy of the item correlation
matrix upon which factor analysis is based. Data were analysed using the IBM SPSS
Statistics V21.
The KMO coefficient (Table 4) for this dataset fell at Meritorious Level 0.855 (in
between 0.80 to 0.89) exceeding the recommended value of 0.6 (Kaiser 1974),
indicating that the sample is adequate (Hutcheson & Sofroniou, 1999). The
approximate of Chi-square is 3307.151 with 741 degrees of freedom and Barlett’s
Test of Sphericity is significant at 0.000 which is less than 0.05 (Cerny & Kaiser,
1977) indicating that properties of the correlation matrix justified factor analysis
to be used. Hence Factor Analysis is considered as an appropriate technique and
valid for further analysis of the data.

ISSN: 22897615
Page 15
Table 4: KMO and Bartlett’s Test (SPSS)
.855
Approx. Chi-Square 3307.151
df 741
Sig. 0.000
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
Bartlett's Test of Sphericity
4.4.3 Communalities
Principal Components Analysis was deployed to extract the communalities. The
communality for a variable is the variance accounted for by all the extracted
factors. The higher the communality indicating the variable is more reliable. The
mean level of communality is preferable to be at least .70 and for communalities
not to deviate over a wide range (MacCallum, Widaman, Zhang & Hong, 1999). The
mean communality for the 42 variables in this study is .78.
4.4.4 Total Variance Explained, Scree Plot and Rotated Component
Matrix
The results indicated that the total variance explained by each of the extracted
components. A component is represented by all the variation in each of the
variables. Each variable is standardized with the maximum variance for each as
1.0. An eigenvalue reflects the proportion of variance explained by the component.
Kaiser’s Criterion (Kaiser, 1960) specifies that only components with an
eigenvalue of 1.0 or greater should be retained for analysis. Kaiser’s Criterion is
the default retention method in SPSS. Conway and Huffcut (2003) found that
among organizational researchers, Kaiser’s Criterion was the most highly utilized
method of identifying the number of components to use in conducting a factor
analysis. Higher percentages of total variance explained is an indicator that a
strong relationship exists among a group of variables under study.
Before conducting factor analyses, we consulted peer-reviewed literature on the
effect of using accounting software (; Ali, Omar and Bakar, 2016; Ismail and King,
2005). We noted that 10 of the 42variables were designed to function as validity
checks to address a number of factors that shape the context for interpreting the
impact accounting software characteristics. The initial rotated solution revealed 7
components with an eigenvalue of 1.0 or greater that explained 77.9% of the
variance. Inclusion of the 10 validity check items in this analysis, which were
dispersed across the 7 factors and one variables X7 did not contribute
meaningfully to the factor structure, posed a likely confound to the solution, and
was therefore removed. The remaining 32 variables were submitted to a second
EFA using the same procedure, yielding a 7-factor solution that accounted for
78,4% of the variance. Despite the slight increase in variance explained, the
number of factor remained same at 7 which vary from the theoretical factor of 6.
Therefore, problem variables, X25 which loaded to component 7 and the cross
loading variable, X37 were eliminated, leaving 39 variables. The loading factor of
above 0.65 is required for significance for a sample of 78 respondents (Hair et al.,
2010).

ISSN: 22897615
Page 16
The same EFA procedure was repeated on the 39 retained items, resulting in a 6-
factor solution that explained 76.6% of the overall variance. Examination of the
scree plot suggested that a six-factor solution provided the best fit. The suitability
of a six-factor solution was also evident from initial eigenvalues and total variance
associated with the six individual factors. The final rotated factor matrix for the
EFA is presented in Table 4. Results showed each item correlated strongest with
the factor to which it was assigned.
The first Factor, which was labelled Business Performance, describes the degree of
impact from the use of accounting software. This encompasses nine items covering
issues such as support, competencies, capabilities, system benefit and user
satisfaction. This factor accounted for 50.78% of the total variance and had an
Eigenvalue of 19.8. The Cronbach’s alpha coefficient for this factor was 0.924. The
second Factor was labelled Accuracy and accounted for 8.8% of the total variance
and had an Eigenvalue of 3.446. The Cronbach’s alpha coefficient was 0.949. The
third factor was labelled Reliability and accounted for 5.4% of the variance and
had an Eigenvalue of 2.116. The Cronbach’s alpha coefficient for this Factor was
0.915. The forth factor was labelled Efficiency and accounted for 4.8% of the
variance and had an Eigenvalue of 1.888. The Cronbach’s alpha coefficient for this
Factor was 0.927. The fifth factor was labelled Ease of Use and accounted for 3.7%
of the variance and had an Eigenvalue of 1.453. The Cronbach’s alpha coefficient
for this Factor was 0.89. The last factor was labelled Data Quality and accounted
for 2.96% of the variance and had an Eigenvalue of 1.152. The Cronbach’s alpha
coefficient for this Factor was 0.816. The Cronbach’s alpha coefficient for the whole
Index was 0.973.
4.5 Regression Analysis
This research performed a multiple regression analysis using SPSS to examine the
impact of accounting software on business performance based on the five
independent variables of accounting software characteristics efficiency, reliability,
ease of use, data quality and accuracy. The dependent factor in this study is
business performance. Model summary produced by linear regression explains the
significant of the overall research model in achieving the desired output (Hair et
al., 2010).

ISSN: 22897615
Page 17
4.5.1 Model Summary
Table 5: Regression Model Summary (SPSS)
R R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-Watson
1
.843a.711 .691 .33183 1.546
Model Summaryb
Model
a. Predictors: (Constant), Data_Quality, Efficiency, Accuracy, Ease_of_use, Reliability
b. Dependent Variable: Business_Performance
The Model Summary (Table 5) above predicts that R as 0.843, R Square showing
impact of accounting software characteristics on business performance of firms in
Malaysia as 0.711 and adjusted R Square 0.691, indicating that 69.1% of the
variance of accounting software characteristics can be predicted by the
independent variables of efficiency, reliability, ease of use, data quality and
accuracy. According to Zygmont & Smith (2014), in normal terms a healthy
variation dependent variable must be at least 60%, thus this model is found to be a
good fit as it predicted above 60% of the entire model. Lastly, the Durbin Watson is
found to be 1.546 which clearly indicates no auto correlation amongst the chosen
sample. The Durbin-Watson test for auto-correlation shows that the value of the
model is at 1.546 which is between the values 1.5 to 2.5 indicates that no auto
correlation amongst the chosen sample (Hair et al., 2010). This shows that each
sample is separately independent and not being influenced by the other samples.
4.5.2 Model Significance
The ANOVA statistics is used to represent the regression model significance. As in
Table 6, the significance value for the F statistics is 35.48 and the significance ratio
is 0.000 which is less than 0.05, which concludes that the regression model is
statistically significant (Hair et al., 2010).
Table 6: Model Significance (SPSS)
Sum of
Squares
df
Mean
Square
F Sig.
Regression 19.532 5 3.906 35.478 .000b
Residual 7.928 72 .110
Total 27.460 77
ANOVAa
Model
1
a. Dependent Variable: Business Performance
b. Predictors: (Constant), DataQuality, Efficiency, Accuracy, Ease of use, Reliability
4.5.3 Hypothesis Testing
Table 7 displays the results of hypothesis testing for the five independent variables
based on the significant value from regression analysis, conducted using SPSS tool.
A coefficient summarizes the parameters of the regression model. The
standardized beta coefficients show the impact of each independent variable on
the dependent variable (Business Performance).

ISSN: 22897615
Page 18
Table 7: Coefficient
Standardized
Coefficients
B Std. Error Beta Tolerance VIF
(Constant) .405 .319 1.270 .208
Accuracy .055 .075 .066 .739 .462 .509 1.965
Reliability .112 .102 .118 1.101 .275 .348 2.875
Efficiency .428 .096 .460 4.441 .000 .373 2.677
Ease of use .361 .102 .365 3.547 .001 .378 2.643
Data Quality -.090 .091 -.087 -.983 .329 .516 1.937
1
a. Dependent Variable: Business Performance
Coefficientsa
Model
Unstandardized
Coefficients
t
Sig.
Collinearity Statistics
As presented above, software Efficiency and Ease of Use has a moderate and
significant impact of 46% and 36.5% on Business Performance respectively with
the p-values (Sig.) less than 0.05. Therefore, if software efficiency and ease of use
increase, business performance will increase. However, software Data Quality has
a negligible -8.7% impact on Business Performance which is also not statistically
significant as p-value (Sig.) is more than 0.05. The standardized beta coefficient for
software Accuracy and Reliability are 0.066 and 0.118 shows a 6.6% and 11.8% of
a positive impact of the independent variable (Accuracy and Reliability) on
dependent variable (Business Performance) with a significance value of 0.462 and
0.275 which is more than 0.05. This indicates that Accuracy and Reliability places
an insignificant impact on business performance of firms in Malaysia.
The following Table 8 shows the summary of Hypotheses Testing Results.
Table 4: Hypotheses Testing Results
Hypotheses
Beta
Coefficient
Significant
(P<0.05)
Decision Interpretations
0
Significant as the
calculated p-value
is less than 0.05.
0.275
Not Significant as
the calculated p-
value is more than
0.05.
0.001
Significant as the
calculated p-value
is less than 0.05.
0.329
Not Significant as
the calculated p-
value is more than
0.05.
0.462
Not Significant as
the calculated p-
value is more than
0.05.
H5: There is a significant
positive impact of Software
Accuracy on Business
Performance
0.066
Rejected
The beta coefficient of 0.066
indicates that Software Accuracy
has a 6.6% positive impact on
business performance.
H3: There is a significant
positive impact of Software
Ease of Use on Business
Performance
0.365
Accepted
The beta coefficient of 0.365
indicates that Software Ease of
Use has a 36.5% positive impact
on business performance.
H4: There is a significant
positive impact of Software
Data Quality on Business
Performance
-0.087
Rejected
The beta coefficient of 0.329
indicates that Software Data
Quality has a 32.9% positive
impact on business performance.
H1: There is a significant
positive impact of Software
Efficiency on Business
Performance
0.46
Accepted
The beta coefficient of 0.460
indicates that Software Efficiency
has a 46.0% positive impact on
business performance.
H2: There is a significant
positive impact of Software
Reliability on Business
Performance
0.118
Rejected
The beta coefficient of 0.118
indicates that Software Reliability
has a 11.8% positive impact on
business performance.

ISSN: 22897615
Page 19
4.6 Discussions
4.6.1 Efficiency (Ef)
The standardized beta coefficient is 0.46 which shows a 46% positive impact of the
independent variable (Efficiency) on dependent variable (Business Performance)
with a significance value of 0.00 which is less than 0.05. This indicates that
software efficiency places a significant impact on the business performance of
business firms in Malaysia.
To ensure the relevancy of this result further, researcher have deep dive into other
similar research to examine how those research could further support this
research. According to Information Processing Theory utilized by Rogers et al.
(1999), describe business organizational behaviour and present a model
describing how effective and ineffective business strategies are developed. Levy et
al. (2011) stated that firms have highest alignment and good performance with
improves system efficiency. Study on IS implementation in India conducted by
Sharma and Bhagwat (2003) suggest the operational efficiency of IS have
positively impact on firms performance. Similarly, Greene and Segal (2004) show
that efficiency increase profitability of the firms in terms of return on equity in the
insurance industry. There is a strong positive link between the firm’s value and the
utilisation of the firm’s resources.
4.6.2 Reliability (R)
The standardized beta coefficient is 0.118 which shows a 11.8% positive impact of
the independent variable (Reliability) on dependent variable (Business
Performance) with a significance value of 0.275 which is more than 0.05. This
indicates that software reliability places an insignificant impact on the business
performance of business firms in Malaysia.
This finding is similar to the study by Goodhue & Thompson (1995) that systems
reliability had no impact on productivity and effectiveness and found no
relationship between reliability and performance for individual users of ERP
systems. On the other hand, this finding contradicts with Bharati & Chaudhury
(2006) where they found a significant relationship in reliability of system to
decision-making satisfaction in an e-commerce environment. Gorla et al., (2010)
explores the linkage between IS quality and organizational impact . The results
indicate that IS quality dimensions have a significant positive influence on
organizational impact either directly or indirectly Accounting software system is
reliable when information delivered on time and with error-free performance will
result in timely and efficient decision making, which in turn leads to better internal
organizational efficiency (Gorla et al., 2010).
4.6.3 Ease of Use (EOU)
The standardized beta coefficient is 0.365 which shows a 36.5% positive impact of
the independent variable (Ease of Use) on dependent variable (Business
Performance) with a significance value of 0.001 which is less than 0.05. This
indicates that software ease of use places a significant impact on the business
performance of business firms in Malaysia.

ISSN: 22897615
Page 20
The finding of this study is supported by several other studies. Bias and Mayhew
(2005) found an increase in ease of use positively influence several aspects of a
company's output quality such as increased sales and revenues, productivity and
customer satisfaction. Bharati and Chaudhury (2006) found a significant
relationship between perceived ease of use and performance. Gefen and Straub
(2000) explore the relative importance of perceived ease of use in IS adoption as to
impact on firms performance. Findings by Goodhue & Thompson (1995) however,
contradicted as they found systems perceived ease of use had no impact on
productivity and effectiveness.
4.6.4 Data Quality (DQ)
The standardized beta coefficient is 0.087 which shows a 8.7% negative impact of
the independent variable (Data Quality) on dependent variable (Business
Performance) with a significance value of 0.329 which is more than 0.05. This
indicates that software data quality places an insignificant impact on the business
performance of business firms in Malaysia.
This finding contradicts with most of the trending empirical findings in literature
Few studies have scrutinized the effect of the AIS’ data quality as critical success
factor on organisational performance. Emeka-Nwokeji (2012) concluded that data
quality is significant for the success of AIS as the quality of data is assured for
enhancing firm’s performance. Wixom & Watson (2001) assert data quality as one
of the critical factor is relevant directly to effort for decision making. Al_Qudah and
Shukeri (2014) noted data quality is strongly related and positively impacts the
perception of company internal auditors. More scientific studies on found data
quality and AIS performance are strongly related (Emeka-Nwokeji, 2012). The
results from the study provide empirical support, suggesting that data quality
positively influences the business performance. The analysis also suggests that
data quality is an important antecedent of business performance.
4.6.5 Accuracy (A)
The standardized beta coefficient is 0.066 which shows a small 6.6% positive
impact of the independent variable (Accuracy) on dependent variable (Business
Performance) with a significance value of 0.462 which is more than 0.05. This
indicates that software accuracy places an insignificant impact on the business
performance of business firms in Malaysia.
According to this research, accuracy has no significant influential role on business
performance in the context of this research. This finding however contradicts with
past research findings. Swanson (1997) found non conformation of information
provided by an IS leads to disruption of operations in the organization and hefty
maintenance costs, ensuing in high costs to the organization. Banker et al. (1990)
assert that firm will benefit from reduced labour costs and waste when the
operational information from accounting software is highly accurate.
5.0 CONCLUSION & RECOMMENDATION
This research was conducted to investigate the impact accounting software
characteristic in business performance of firms in Malaysia. The quantitative data
required for the study was gathered through a sample size of 78 participants

ISSN: 22897615
Page 21
(accountants or employees who involve in using accounting software in their
work). Regression analysis was employed to gauge the impact of independent
variables (Efficiency, Reliability, Ease of Use, Data Quality and Accuracy) on the
Dependent Variable (Business Performance).
All the variables (dependent and independent) chosen for the study have indicated
Cronbach’s alpha scoring of above 0.80, indicating all variables have high internal
consistency and are acceptable. The descriptive data analysis also supported that
most of the respondents perceived that the independent variables chosen for this
study affects positively to the dependent variable. The regression analysis
indicated two variables (Efficiency and Ease of Use) out of the five independent
variables have significant impact on business performance. Meanwhile, the other
three variables Reliability, Data Quality and Accuracy were not found to have a
significant impact on business performance.
In conclusion accounting software systems is of great importance and has a great
value to businesses, organization and the economy. The accurate and reliable
information flow is very crucial to the growth of economy. Performance
management play a key role in improving the overall value of an organization.
Prior researches have shown that accounting information system adoption does
increases firm’s performance, profitability, and efficiency operations. This study
showed that there is strong relationship between the characteristic of accounting
software and business performance, which means access to accurate accounting
information, will lead to organizational effectiveness. Therefore, it can be
concluded that accounting software has an impact on business performance of
firms in Malaysia.
5.1 Research limitations
This study comprises of few limitations. Firstly, this study was conducted in
English. The study could have included the survey questions in the local language,
Bahasa Malaysia. This would make the survey sample more evenly distributed
among the population and would eliminate misinterpretation of the survey
questions. Secondly, this research was done generally on any business
organization in Malaysia without specifically looking into one industry. Hence,
different industry may have different requirement on the accounting software
which may have different impact on the business performance. Another limitation
is the difficulty in getting feedback on the questionnaire from respondents due to
time constraints.
5.2 Future research directions
Throughout this research work, the suggestions concerning the expansion of the
present study ascend. First, in data collection, I would suggest to collect data from
disparate sources. Secondly, I would suggest similar studies to be done in different
industries to compare the findings. Finally, a similar study could be carried out
focusing on the effectiveness of accounting software systems in improving the
business performance. Similarly, a study could also be carried out focusing on
factors affecting the implementation of accounting software systems or challenges
faced during implementation of accounting software systems in Malaysia.

ISSN: 22897615
Page 22
References
Al_Qudah, H. M. A. & Shukeri, S. N. B. (2014). The Role Of Data Quality And Internal
Control In Raising The Effectiveness Of Ais In Jordan Companies.
International Journal of Technology Enhancements and Emerging
Engineering Research, 3(8), 298–303.
Alford, A. and J. Boatsman (1995). Predicting long-term stock return volatility:
Implications for accounting and valuation of equity derivatives. The
Accounting Review (October): 599 - 618.
Al-Hakim, L. (2007). Information quality management: theory and applications
(1st ed.). U K: IGI Global.
Ali, B.J., Omar, W.A.W. and Bakar, R. (2016). ACCOUNTING INFORMATION SYSTEM
(AIS) AND ORGANIZATIONAL PERFORMANCE: MODERATING EFFECT OF
ORGANIZATIONAL CULTURE.
Banker, R.D., Kauffman, R.J. and Morey, R.C., 1990. Measuring gains in operational
efficiency from information technology: a study of the Positran deployment at
Hardee’s Inc. Journal of Management Information Systems, 7(2), pp.29-54.
Bias, R.G. and Mayhew, D.J. eds., 2005. Cost-justifying usability: An update for the
Internet age. Elsevier.
Brady, H.E. and Johnson, R., 2008. The rolling cross section and causal
distribution. Ann Arbor, MI: University of Michigan Press.
Brynjolfsson, E. and Hitt, L., 1996. Paradox lost? Firm-level evidence on the returns
to information systems spending. Management science, 42(4), pp.541-558.
Brynjolfsson, E. and Hitt, L.M., 2000. Beyond computation: Information technology,
organizational transformation and business performance. The Journal of
Economic Perspectives, 14(4), pp.23-48.
Casler, D. and T. Hall. 1985. Firm-specific asset valuation accuracy using a
composite price index. Journal of Accounting Research (Spring): 110-122.
Cerny, C.A., & Kaiser, H.F. (1977). A study of a measure of sampling adequacy for
factor-analytic correlation matrices. Multivariate Behavioral Research,
12(1), 43-47.
Chan YE, Huff SL, Barclay DW, Copeland DG, 1997. Business strategic orientation,
information systems strategic orientation and strategic alignment. Inf Syst
Res 1997;8(2):125– 50.
Chandar, N. and Bricker, R., 2002. Incentives, Discretion, and Asset Valuation in
Closed–End Mutual Funds. Journal of Accounting Research, 40(4), pp.1037-
1070.
Choe. J M (2002) The Organizational Learning Effect of Management Accounting
Information Under Advanced Manufacturing Technology, European Journal
of Information systems, 11: 142-158.
Clikeman, P.M., 1999. Improving information quality. Internal Auditor, 56(3),
pp.32-34.
Cragg PB, King M, Hussin H. (2002), IT alignment and firm performance in small
manufacturing firms. Journal of Strategic Information System; 11:109–32.
Creswell, J. W. (2009). Research design: Qualitative and quantitative approaches
(3rd ed.). London: SAGE Publication.
Cronbach, L. J., 1951. Coefficient alpha and the internal structure of tests.
Psychometrika. 16(3), Pp. 297-334.
DeVellis, R.F. (2012). Scale development: Theory and applications. Los Angeles:
Sage. pp. 109–110.

ISSN: 22897615
Page 23
Dubey, S.K., Rana, A. and Sharma, A., 2010. Usability and Development
environment for software applications: An integrated model. international
journal of Advanced Research in Computer Science, 1(2).
Emeka-Nwokeji, N. (2012). Repositioning accounting information system through
effective data quality management: A framework for reducing costs and
improving performance. International Journal Of Scientific Technology
Research, 1(10), 86–94. Citeseer.
Esmeray, A., 2016. The Impact of Accounting Information Systems (AIS) on Firm
Performance: Empirical Evidence in Turkish Small and Medium Sized
Enterprises. International Review of Management and Marketing, 6(2).
Gefen, D. and Straub, D.W., 2000. The relative importance of perceived ease of use
in IS adoption: A study of e-commerce adoption. Journal of the association
for Information Systems, 1(1), p.8.
Gefen, D., & Straub, D.W. (1997). Gender differences in the perception and use of
email: an extension to the technology acceptance model. MIS Quarterly,
389e400.
Gefen, D., Straub, D.W., & Boudreau, M. C. (2000). Structural equation modeling and
regression: guidelines for research practice. Communications of the
Association for Information Systems, 4, 1e77.
Goodhue, D.L. and Thompson, R.L., 1995. Task-technology fit and individual
performance. MIS quarterly, pp.213-236.
Gould, J.D., 1988, October. Designing for usability: The next iteration is to reduce
organizational barriers. In Proceedings of the Human Factors Society Annual
Meeting (Vol. 32, No. 1, pp. 1-9). Sage CA: Los Angeles, CA: SAGE
Publications.
Gorla, N., Somers, T.M. and Wong, B., 2010. Organizational impact of system
quality, information quality, and service quality. The Journal of Strategic
Information Systems, 19(3), pp.207-228.
Green, S.B. and Salkind, N.J., 2010. Using SPSS for Windows and Macintosh:
Analyzing and understanding data. Prentice Hall Press.
Greene, W.H. and Segal, D., 2004. Profitability and efficiency in the US life insurance
industry. Journal of Productivity Analysis, 21(3), pp.229-247.
Gullkvist, B., 2002. Towards paperless accounting and auditing. Finland: E-
Business Research Center, pp.1-12.
Hair, J. F. J., Babin, B., Money, A. H., & Samouel, P. (2003). Essential of business
research methods. United States of America: John Wiley & Sons.
Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E., 2010. Multivariate Data
Analysis. 7th ed. Prentice Hall.
Hla, D. and Teru, S.P., 2015. Efficiency of Accounting Information System and
Performance Measures–. Int. J. of Multidisciplinary and Current research, 3.
Huang, Huan-Tsae, Lee, Y. W. and Wang, R. Y. (1999). Quality information and
knowledge, Prentice Hall PTR.
Hutcheson, G.D. and Sofroniou, N., 1999. The multivariate social scientist:
Introductory statistics using generalized linear models. Sage.
IBM Corp. (2015). IBM SPSS statistics for Windows, version 23. Armonk: IBM Corp.
Igbaria, M. and Tan, M., 1997. The consequences of information technology
acceptance on subsequent individual performance. Information &
management, 32(3), pp.113-121.

ISSN: 22897615
Page 24
Ismail, N.A. and King, M., 2005. Firm performance and AIS alignment in Malaysian
SMEs. International Journal of Accounting Information Systems, 6(4),
pp.241-259.
Ismail, N.A. and King, M., 2014. Factors influencing the alignment of accounting
information systems in small and medium sized Malaysian manufacturing
firms. Journal of Information Systems and Small Business, 1(1-2), pp.1-20.
Judd, C. M., & McClelland, G. H. (1989). Data analysis: A model comparison approach.
New York: Harcourt Brace Jovanovich.
Kaiser, H. F. (1960). The application of electronic computers to factor analysis.
Educational and Psychological Measurement, 20(1), 141–151.
doi:10.1177/001316446002000116.
Kouser, R., Awan, A., Rana, G. and Shahzad, F., 2011. Firm Size, Leverage and
Profitability: Overriding Impact of Accounting Information System. Journal
of Management and Business Review, 1(10), pp.58-64.
Landauer, T. K. (1996). The trouble with computers. Cambridge, MA, The MIT
Press. p158.
Lee, C.S., 2001. Modeling the business value of information technology. Information
& Management, 39(3), pp.191-210.
Levy, M. Powel, P & Yetton, P. (2011). Contingent dynamics of IS strategic
alignment in small & medium sized enterprises. Journal of Systems &
Information Technology, 13(2): 106-124.
Maines, L.A. and Wahlen, J.M., 2006. The nature of accounting information
reliability: Inferences from archival and experimental research. Accounting
Horizons, 20(4), pp.399-425.
Osei-Bryson, K.M. and Ko, M., 2004. Exploring the relationship between
information technology investments and firm performance using regression
splines analysis. Information & management, 42(1), pp.1-13.
Pérez Estébanez, R., Urquía Grande, E. and Muñoz Colomina, C., 2010. Information
technology implementation: evidence in Spanish SMEs. International
Journal of Accounting & Information Management, 18(1), pp.39-57.
Phillips, D. C., & Burbules, N. C. (2000). Postpositivism and educational research.
Lanham, MD: Rowman & Littlefield.
Ravichandran, T. and Rai, A., 2000. Quality management in systems development:
an organizational system perspective. MIS quarterly, pp.381-415.
Raymond L, Pare G, Bergeron F. Matching information technology and
organisational structure: An empirical study with implications for
performance. Eur J Inf Syst 1995;4:3– 16.
Rogers, P. R., Miller, A., & Judge, W. Q. (1999). Using information-processing theory
to understand planning/performance relationships in the context of
strategy. Strategic Management Journal, 20, 567-577.
Saira, K., Zariyawati, M.A. and Annuar, M.N., 2010. Information system and firms’
performance: the case of Malaysian small medium enterprises. International
business research, 3(4), p.28.
Sajady H, Dastgir M, Nejad HH (2008) Evaluation of the effectiveness of accounting
information systems. International Journal of Information Science and
Management 6: 49-59.
Sam, M., Fazli, M., Hoshino, Y. and Tahir, M.N.H., 2012. The adoption of
computerized accounting system in small medium enterprises in Melaka,
Malaysia.

ISSN: 22897615
Page 25
Sharma, B. and Gadenne, D., 2002. An inter-industry comparison of quality
management practices and performance. Managing Service Quality: An
International Journal, 12(6), pp.394-404.
Sharma, M. & Bhagwat, R. (2003). Performance measurements in the
implementation of information systems in small and medium sized
enterprises: A framework and empirical analysis. Measuring Business
Excellence, 10(4): 8-21.
Shriver, K. 1986. Further evidence on the marginal gains in accuracy of alternative
levels of specificity of the producer price indexes. Journal of Accounting
Research Vol. 24, No. 1 (Spring): 151-165.
Shriver, K. 1987. An empirical examination of the potential measurement error in
current cost data. The Accounting Review Vol. 62, No. 1 (January): 79-96.
Sunder, S. and Waymire, G., 1983. Marginal gains in accuracy of valuation from
increasingly specific price indexes: Empirical evidence for the US
economy. Journal of Accounting Research, pp.565-580.
Swanson, L., 1997. Computerized maintenance management systems: a
study of system design and use. Production and inventory management
journal, 38(2), p.11.
Wickramsainghe, D.M.J., Pemarathna, R.M.M.D., Cooray, N.H.K. and Dissanayake,
T.D.S.H., 2017. Impact of accounting software for Business
Performance. Imperial Journal of Interdisciplinary Research, 3(5).
Wilkinson JW, Cerullo MJ, Raval V, Wong-On-Wing B (2000) Accounting
information systems: Essential concepts and applications. John Wiley and
Sons, New York.
Wixom, B. H. & Watson, H. J. (2001). An empirical investigation of the factors
affecting data warehousing success. MIS quarterly, 25(1), 17–41. JSTOR.
Xu, H. (2003). Critical success factors for accounting information systems data
quality.
Xu, H. (2009). Data quality issues for accounting information systems’
implementation: Systems, stakeholders, and organizational factors. Journal
of Technology Research, 1, 1. Academic and Business Research Institute
(AABRI).
Zikmund, W.G., Babin, B.J. and Carr, J.C., dan Griffin, M.(2010). Business research
methods.
Zygmont, C. & Smith, M. R. (2014). Robust factor analysis in the presence of
normality violations, missing data, and outliers: Empirical questions and possible
solutions. The Quantitative Methods for Psychology, 10 (1), 40-55.
IJISE is a FTMS Publishing Journal