KLB4105

Proceeding - Kuala Lumpur International Business, Economics and Law Conference 4 (KLIBEL4)
Vol. 1. 31 May – 1 June 2014. Hotel Putra, Kuala Lumpur, Malaysia. ISBN 978-967-11350-3-7

Abstract
The Adoption of Business Information Technology in the SMEs:An Empirical
Investigation in Eastern Province, Sri Lanka
Moujood Mohamed Shiraj
Department of Management, Faculty of Mangement and Commerce, South Eastern University of Sri Lanka.
E- Mail : mmshiraj@gmail.com ; Tel: 0094772355332
SMEs play a major role in development of Sri Lanka. It seems that SMEs has not yet produced
sufficient outcomes when compared with the other developed and developing countries in the
Eastern province. There is a vast opportunity for Sri Lanka to develop this sector by adopting
business information technology. This research is to study the adoption of business information
technology and the determinants of the levels of business information technology adoption for
marketing purposes by small and medium sized enterprises (SMEs) in Eastern Province of Sri
Lanka. The primary objective of the study is to understand on what extend the business
information technology is adopted by SMEs. The model described in this study is a mixture of
Roger’s model of innovation adoption and the Resource-based View of the firm. Innovation
adoption is hypothesized as a decision making practice and this decision consists of three
components. The researcher used a non-probability sampling technique which is judgment
sampling since the survey was conducted in three districts of Eastern province, Sri Lanka. Linear
Regression is used to test the model and the resulting hypotheses. Furthermore, different factors
affect different levels of adoption so that when studying innovation adoption by enterprises.
Different measures were used to measure adopt versus not adopt and simple adopt versus
sophisticate adopt on the business information technology. There are more non adopters in Eastern
province of Sri Lanka, but most of the non-adopters are willingness to adopt the business
information technology new future.
Keywords: Business information technology, SME, Adoption.

The Adoption of Business Information Technology in the SMEs:An Empirical
Investigation in Eastern Province, Sri Lanka
Introduction
The adoption of business information technology is the theme of this research for the purpose of
marketing in the SMEs industry. It is necessary to mention from the initiating that this research is
interested in the real time use of the business information technology or information system for
marketing purpose in developing business transection and managing customer relationship. The
globalization of the world economy highlights the need for SMEs as the backbone of the national
economy. Successful adoption of information technology provides competitive advantages to the
SMEs.
The main objective of this research is to provide managerial conclusions in relation to the factors
of the Business information technology adoption for marketing purpose through small medium
sized enterprises (SMEs) in Eastern province, Sri Lanka. Business information technology
adoption is precisely defined in this research that the possession of an information system such as
point of sales (POS) system to do their business activities and manage the relationship with the
customers and vendors. This research elaborates and gives a clear-cut idea how the business
information technology is being used to interact with customers, vendors and stakeholders.
Further, the levels of adoption denote the different levels that SMEs undergo in their adoption
progression starting with not possessing an information system to being a minor adopter to being a
major adopter. In this adaptation, non adopters do not possess any information system at their
SMEs.
The main gaps identifies are lack of research on SMEs adoption of the technology form a different
level of adoption, lack of research on technology adoption in developing countries such as Sri
Lanka, too much focus on consumer adoption in contrast to organizational adoption, shortage of
research on innovation adoption from a different level of adoption and a need to identify the
critical factors that affect each level of adoption. This research combines of Resource-Based View
of the firm (RBV) and Roger’s innovation adoption model to produce a moderate
conceptualization for the adoption of innovations.

Literature Review
Small firms have different characteristics than larger ones including limited resources and more
flexible structure (Hausman, 2005) which are possible to affect their adoption decisions. Secondly
small size organizations have been much slower than large size organization in adopting the
business information technology and also relevant research has been gentler in developing the
theme of information technology adoption. Third, there is a few amount of researches have been
deliberate on the factors affecting the levels of business information technology adoption by
SMEs. Very few numbers of researchers have endeavored to discuss the factors that lead these
small companies to information technology adoption. Fourthly, Small medium size Enterprise is
significant important to the global market and it is worth analyzing whether the same issues and
factors that influence on information technology adoption by large companies also influence
SMEs’ information technology adoption. Finally, Since the Sri Lanka is a developing country it
would be a great contribution to the business society to enhance the business performance of the
SMEs industry.
The Rogers’s model is one of the leading concepts on adoption and can be applied successfully to
a variety of innovations. Originally this model was helping to the social science track, and in
particular rural area innovation adoption, now it is directed toward the individual customer; it has
changed throughout the years to include technological innovations and to serve both business and
consumer adoption units. During the knowledge stage the decision-making unit gets exposed to
the innovation’s existence and starts to understand how it operates (Rogers, 2003). Previous
practice, needs and problems, innovativeness of the individual and norms of the social system are
prior condition. At the persuasion stage the “individual becomes more psychologically involved
with the innovation” and thus starts to actively seek information about the new idea (Rogers, 2003
p.175). Perceived characteristics of the innovation: relative advantage, compatibility, complexity,
trialability and observability are keys in contributing the individual form a positive or negative
attitude toward the innovation.
Rogers classifies individuals into five stages that are: innovators, early adopters, early majority,
late majority and laggards (Rogers, 2003 p.280). For Rogers, innovativeness helped in
understanding the desired and main behavior in the innovation-decision process. Thus, he
categorizes the adopters based on innovativeness. Figure 2.2 shows the distribution of adopters is
a normal distribution.

Technology Acceptance Model (TAM), has been widely used in explaining and predicting
individuals’ acceptance of information systems (Venkateshand Davis, 2000). TAM recommends
that external variables such as constraints and individual differences are predictable to affect user
acceptance of technology as far as they facilitate the two key belief constructs of perceived
usefulness and perceived ease of use. Davis (1986) introduced TAM which specifically addresses
the determinants of computer acceptance among end users. TAM theorizes that an individual’s
behavioral intention to use a system is affected by two beliefs: perceived usefulness and perceived
ease of use.
The resource-based view (RBV) of the firm is explains the concept of competitive advantage that
emphasizes the link between a firm’s internal resources, strategy, behaviour and performance
(Wright et al., 1994). Firm resources have been defined by Wernerfelt (1984) as tangible and
intangible assets that are semi permanently tied to the firm. Barney (1991) expanded this
definition to include “all assets, capabilities, organizational processes, firm attributes, information,
knowledge, etc. controlled by a firm that enable the firm to conceive of and implement strategies
that improve its efficiency and effectiveness.”
After a comprehensive review of the innovation adoption literature and with a particular focus on
the business information technology, considerable gaps have been identified and these addressed
by this study those are business information technology adoption is more of a continuous process
that contains of several phases rather than simply an adoption versus non-adoption decision, few
researches went through the adoption in small and medium sized enterprises studies in Sri Lanka,
this large number of variables recommends that more research is needed to identify the important
ones.
Methodology
This research covers mixed methods research that combines both qualitative and quantitative
research methods. The population of this research is all small medium industries in Eastern
province of Sri Lanka who carry out the business in small medium scale. The Eastern province
includes three districts namely; Ampara, Batticaloa and Trincomalee. Entrepreneurs who do
business in Eastern province are the ones interested in marketing their business with business
information technology as they need to improve their business process and enjoy the advantages
of business information technology adoption.

Non-probability sampling was used in this research which is judgment sample in selecting a
sample that is believed to represent the population of interest (Diamantopoulos, and
Schlegelmilch, 1997). The variable part of the questionnaire is separated into three sectors. The
first sector collects descriptive information of enterprise. The second sector measures the
dependent variable by finding adopters from non-adopters and also identifying level of adoption.
The final sector focuses on measuring independent variables as defines in the model.
Proposed Conceptual Model
The model described in this study is a mixture of the Resource-based View of the firm (RBV) and
Roger’s model of innovation adoption. Innovation adoption is hypothesized as a decision making
practice and this decision consists of three components. These are the decision maker, innovation
and the external context.

Conceptual Model of the Factors that affect enterprises’ adoption of the Business
information technology in the SMEs industry
Derivation of Hypotheses of the Research
The hypotheses is formed with three group of factors mentioned in the proposed theoretical model.
H. 1: Management’s attitude toward change will be positively related to the level of business
information technology adoption.
H. 2: Management’s response to risk will be positively related to the level of business information
technology adoption.
H.3: Top management support will be positively related to the level of Business information

H. 4: The size of SMEs will be positively related to the level of business information technology
adoption.
H. 5: Employees’ IT knowledge will be positively related to the level of business information
H.6: A positive orientation toward organizational learning will be positively related to the level of
business information technology adoption.
H.7: The perceived compatibility of the business information technology will be positively related
to the level business information technology adoption.
H. 8: The perceived complexity of the business information technology will be negatively related
to the level of business information technology adoption.

Research Objectives
The primary objective of this study is to understand on what extend the business information
technology is adopted by SMEs. The main aim of this research is to provide empirical
contributions through selecting the SMEs industry as the field of application and through studying
business information technology adoption.
Sub Objectives are as follows:
1. To understand the present status on Business information technology adoption of the
SMEs in Eastern region - Sri Lanka.
2. To identify the determinants of business information technology adoption by small and
medium sized enterprises.
3. To formulate a theoretical framework for business information technology adoption by
small and medium sized enterprises.
Result and Discussion
The data collection process was not without difficulties. The main problems encountered included
difficulty in taking an appointment, uncooperative attitude and lack of interest among respondents
participating in the research. These problems affected the sample size as only 246 completed
questionnaires could be secured out of the 350 entrepreneurs available. It is worth noting however,
that most of the entrepreneurship who did not participate either could not be reached due to very
unhelpful attitude from respondents or did not agree to participate. Out of 246 questionnaires 23
questionnaires were removed for several reasons including illegible questionnaires, inconsistent

responses and incorrect information. The remaining usable 223 questionnaires consisted of 75
adopters and only 148 non-adopters.
The individual alpha’s for all scales measuring the independent variables are shown in the table 1.
Cronbach Alpha values shows that the reliability coefficient is within a satisfactory level which
supports the argument that the research instrument is reliable with regard to its internal
consistency, because all the values greater than 0.7.
Table 1 Cronbach Alpha Coefficient

Variables Cronbach Alpha
Attitude toward Change 0.925
Response to Risk 0.951
Top Management Support 0.963
Employees’ IT Knowledge 0.949
Organizational learning 0.940
Compatibility 0.898
The adoption of Business Information Technology in the SMEs is measured in different ways.
First measuring simple adopter which is does your company have a business information
technology or information system or not. Second, measuring sophisticated adopters which is does
your company use business information technology or information system to do your business or
not. Those who have business information technology or information system classified as simple
adopters whereas those who use business information technology or information system classified
as sophisticated adopters.
The 223 collected cases include 75 adopters representing 33.63% of the cases and 148 non-adopters
representing 66.37% as shown in the following table. This indicated 66.37 percent of the
entrepreneurs are non-adopters in Eastern province of Sri Lanka.
Table 2 Representation of Adoption Status
Adoption Status (Own BIT or not)
Total
Adopters Non-adopters
Count 75 148 223
Percent 33.63 66.37 100
Table 3 Use of Business Information Technology within Adopters
Adopters Status (Use BIT or not)
Total
Simple Adopters Sophisticated Adopters
Count 32 43 75


Percent 42.67 57.33 100
Table 4 Use of Business Information Technology within Sample
Adoption Status (Use BIT or not)
Total
Simple Adopters
Sophisticated
Adopters
Non-adopters
Count 32 43 148 223
Percent 14.35 19.28 66.37 100
Further, out of 75 adopters those who use the business information technology classified as
sophisticated adopters and others classified as simple adopters. Table 3 shows that, the 75 adopters
include 32 simple adopters representing 42.67% and 43 sophisticated adopters representing
57.33% within adopters. Table 4 shows that, 32 simple adopters representing 14.35%, 43
sophisticated adopters representing 19.28% and 148 non adopters who never use business
information technology representing 66.37% within sample. This indicated 66.37 percent of the
entrepreneurs are non-adopters in Eastern province of Sri Lanka and only 43 entrepreneurships
representing 19.28 percent of the cases gave responses to what percent of business information
technology usage. The remaining 148 cases include entrepreneurs who do not use business
information technology because they are simple adopters and do not own business information
technology.
An SPSS multiple linear regression model was used to test the hypothesized effects of the
independent variables. Results of the questionnaires were plotted on a table in order to determine
whether a linear relationship existed between variables. A t-test was also conducted in order to
ensure that coefficients differ from zero in order to determine whether the findings are statistically
significant the fit of the model was assessed. ANOVA was used to test whether the groups are
clearly different. Significance test is used to determine the probability of a relationship between
variables.
Adopters’ View about Business Information Technology
Table 5 Adoption Linear Regression
Model Summary
R Square = 0.767
Adjusted R Square = 0.739
F = 27.152
p= 0.000
Variables Beta t-value Sig.
Attitude toward Change 0.50 5.22 0.000
Response to Risk 0.01 0.08 0.934


Top Management Support 0.70 8.58 0.000
Company Size -0.20 -2.61 0.011
Employees’ IT Knowledge -0.27 -2.68 0.009
Organizational Learning 0.06 0.86 0.392
Compatibility -0.01 -0.14 0.890
Complexity 0.03 0.39 0.699
Table 5 shows the adopters’ (75) views within sample. The above table shows that over all model
is significant with a p-value of zero to three decimal places, the model is statistically significant
and The R-squared is 0.767 meaning that approximately 76.7% of the variability can be explained
by the variables in the model. Two variables from individual factors (Attitude toward change, Top
management support) are positively impact and significant in relation to the adoption of Business
information technology. The magnitude of the relations is presented by the beta coefficients.
Attitude toward change is significant with a beta value of 0.50 (p=0.000) and top management
support with a beta value of0.70 (p=0.000). Another two variables from firm resource (Company
Size, Employees’ IT Knowledge) are negatively impact and significant in relation to the adoption
of Business information technology. The magnitude of the relations is presented by the beta
coefficients. Attitude toward change is significant with a beta value of -0.20 (p=0.011) and top
management support with a beta value of -0.27(p=0.009).
Again these results indicate that the adoption/ non-adoption decision is highly influenced by
individual factors, firm resource factors in the model include attitude toward change and top
management support, company size, employees’ IT knowledge except response to risk and
organizational learning.
Non-adopters’ View about Business Information Technology
Table 6 Non-adoption Linear Regression
Model Summary
R Square = 0.944
F = 291.304
p= 0.000
Response to Risk -0.156 -2.436 0.016


Company Size -0.007 -0.318 0.751
Employees’ IT Knowledge 0.112 2.125 0.035
Organizational learning 0.017 0.251 0.802
Compatibility -0.012 -0.200 0.842
Complexity 0.028 0.533 0.595
Table 6 shows the non-adopters’ (148) views about business information technology within
sample. The above table shows that over all model is significant with a p-value of zero to three
decimal places, the model is statistically significant and The R-squared is0.944meaning that
approximately 94.4% of the variability can be explained by the variables (Attitude toward Change,
Response to Risk, Top Management Support, Employees’ IT Knowledge, Organizational
Learning, Compatibility, Complexity) in the model. Four independent variables (Attitude toward
Change, Response to Risk, Top Management Support, and Employees’ IT Knowledge) are
positively correlated and significant in relation to the adoption of Business information
technology. The magnitude of the relations is presented by the beta coefficients. Attitude toward
change is significant with a beta value of 0.201 (p=0.000), response to risk is significant with a
beta value of 0.156 (p=0.016), top management support is significant with a beta value of
0.803(p=0.000) and employees’ IT knowledge is significant with a beta value of0.112 (p=0.035)
.Further non-adopters feel about business information technology that the adoption/ non-adoption
decision is highly influenced by individual factors in the model. Individual factors include attitude
toward change, response to risk and top management support except response to risk. Additionally
employees’ IT knowledge also contributes to the adoption of business information technology.
Simple Adopters’ View about Business Information Technology
Table 7 Simple Adoption Linear Regression
Model Summary
R Square = 0.945
F = 366.711
p= 0.000
Response to Risk -0.182 -3.244 0.001


Company Size -0.002 -0.103 0.918
Employees’ IT Knowledge 0.104 2.149 0.033
Compatibility -0.041 -0.951 0.343
Complexity 0.020 0.458 0.648
Table 7 shows the simple adopters’ (32) views about business information technology within
sample. The above table shows that over all model is significant with a p-value is 0.000, the model
is statistically significant and The R-squared is 0.945 meaning that approximately 94.5% of the
variability can be explained by the variables (Attitude toward Change, Response to Risk, Top
Management Support, Employees’ IT Knowledge, Organizational Learning, Compatibility,
Complexity) in the model. Simple adopters have the same view of non-adopters with in sample
because same variables are significant in this model. This means simple adopters is the adopters
who just own the business information technology or information system and they never use it for
their business purpose. Four independent variables (Attitude toward Change, Response to Risk,
Top Management Support, and Employees’ IT Knowledge) are positively correlated and
significant in relation to the adoption of Business information technology. The magnitude of the
relations is presented by the beta coefficients. Attitude toward change is significant with a beta
value of 0.186 (p=0.000), response to risk is significant with a beta value of 0.182 (p=0.001), top
management support is significant with a beta value of 0.840 (p=0.000) and employees’ IT
knowledge is significant with a beta value of 0.104 (p=0.033).
Sophisticated Adoption Linear Regression: Sophisticated Adopters’ Model
Sophisticated Adopters’ View Business information technology
Table 8 Sophisticated Adoption Linear Regression
Model Summary
R Square = 0.857
F = 25.519
p= 0.000
Response to Risk 0.028 0.237 0.814
Company Size -0.050 -0.602 0.551


Employees’ IT Knowledge -0.327 -2.635 0.013
Compatibility 0.191 2.235 0.032
Complexity 0.183 2.363 0.024
Table 8 shows the Sophisticated adopters’ (43) views about business information technology
within sample. The above table shows that over all model is significant with a p-value is 0.000, the
model is statistically significant and The R-squared is 0.857 meaning that approximately 85.7% of
the variability can be explained by the variables (Attitude toward Change, Response to Risk, Top
Management Support, Employees’ IT Knowledge, Organizational Learning, Compatibility,
Complexity) in the model. Sophisticated adopters have different views within sample. This means
sophisticated adopters is the adopters who actually use the business information technology or
information system and they such level of knowledge in business information technology. Five
independent variables (Attitude toward Change, Top Management Support, Employees’ IT
Knowledge, Compatibility, and Complexity) are positively correlated and significant in relation to
the adoption of Business information technology. The magnitude of the relations is presented by
the beta coefficients. Attitude toward change is significant with a beta value of 0.748(p=0.000),
top management support is significant with a beta value of 0.241(p=0.029), employees’ IT
knowledge is significant with a beta value of 0.327(p=0.013), Compatibility is significant with a
beta value of 0.191(p=0.032), and Complexity is significant with a beta value of 0.183 (p=0.024).
Hypotheses Results
Hypotheses testing required an evaluation and analysis of the results across different measures of
the dependent variable and different formations of the independent variables which produced
contradictory results for many of the constructs. Furthermore, results were not entirely consistent
across all models. This research used a single set of hypotheses to measure both probability and
level of adoption because the same factors are seen as relevant to both decisions but the strength of
their impact may vary. However, while designing the questionnaire and as explained earlier in this
chapter, a number of approaches were used to measure adoption of the business information
technology. A simple measure was used to measure adopt versus not adopt and use business
information technology versus not use business information technology. While a richer and more
sophisticated measure of percentage of usage on business information technology was used to
measure the simple versus sophisticated adoption. These different measurement methods used
resulted in different results that made analysis more complex. Four different views are constructed

in the analysis those are adopters, non-adopters, simple and sophisticated adopters’ view. Testing
the hypotheses relied more heavily on the sophisticated adopters’ view when the relevant construct
appeared as an evolving factor.
Conclusion
This study shows that the management’s attitude toward change has a significant positive
relationship with adoption and non-adoption decision. Attitude toward change showed a positive
significant relation in all models. Companies not willing to make simple adoption of the business
information technology are less likely to take the risks associated with the business information
technology. Top management support and business information technology adoption are
interconnected because support of management is vital when considering whether to adopt or not
adopt the business information technology. All adopters are small and medium enterprises not
micro enterprises and most of the micro enterprises not adopt business information technology
because of inadequate resource and facilities. But adopter’s views shows that a negative relation
indicating that smaller companies have more flexibility is more likely to adopt the business
information technology.
Employees’ IT Knowledge is an important factor in decision making regard business information
technology adoption. Sophisticated adopters who actually use the business information technology
only know the compatibility issues and others do not have the idea about this complexity issues
unless they own or use business information technology or information. Difficulty of using the
business information technology is an influential factor whereby companies who adopted the
business information technology already as being complex are less likely to adopt. There are more
non adopters in Eastern province of Sri Lanka, but most of the non-adopters are willingness to
adopt the business information technology new future. All the factors except organizational
learning are supported to the business information technology adoption in eastern province of Sri
Lanka. Therefore, it can be concluded that there is potential for the adoption of Business
information technology in the SMEs in Eastern province of Sri Lanka. Further it can be concluded
that there are several factors positively influence while one factor not positively influence on the
adoption of Business information technology.
The model includes adoption of the business information technology as the dependent variable and
independent variable as three factors. The questionnaire attempted to measure different levels of
adoption. Different measures were used to measure adopt versus not adopt and simple adopt

versus sophisticate adopt on the business information technology. Percentage of business is done
via the business information technology or information system was used to measure the simple
versus sophisticated adoption. This way of conceptualizing adoption represents another theoretical
contribution. The following will review the findings of the research regarding the effect of each of
the three groups of factors studies and business information technology adoption.
This study can bring out knowledge about the present status on business information technology
adoption of the SMEs in Eastern region - Sri Lanka. The results of this study will raise awareness
of sustainable development in SMEs. Further this will encourage the better business development.
Findings of this study could also form the path for future research questions for further
investigation in future, on SMEs in Sri Lanka in the years to come.
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