Small Enterprise Association of Australia and New Zealand
27th Annual SEAANZ Conference Proceedings
16-18 July Sydney 2014
A research framework for the adoption of
Business Intelligence by Small and Medium-sized
enterprises
Waranpong Boonsiritomachai ᵃ, Michael McGrath ᵇ and Stephen
Burgess ᶜ
ᵃVictoria University, Australia, contact – Email: [email protected]; ᵇ Victoria University,
Australia, contact – Email: [email protected] ; ᶜ Victoria University, Australia contact –
Email: [email protected]
Abstract:
Due to the complexities of making effective and timely business decisions in highly competitive
markets, Data-driven decision-making using Business Intelligence (BI) applications has attracted
many organisations worldwide. However, despite these applications being suited for use in most
organisations regardless of size, only the larger enterprises have reached a stage of maturity in BI
use while small and medium-size enterprises (SMEs) still lag behind. Whilst many academic
researchers have conducted BI research focused on large organisations, literature relating to BI
adoption within SMEs has remained limited. To fill this research gap and support the adoption rate
of BI in SMEs, this paper proposes a research framework for identifying the current state of BI
adoption by SMEs and the enabling factors that impact BI adoption in SMEs. Future research
directions for using this framework are also discussed.
Keywords: Business Intelligence, small and medium-sized enterprises, Information Evolution Model.
©copyright Boonsiritomachai, McGrath and Burgess (2014) all rights reserved.
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INTRODUCTION
With the advances of information technology (IT), increased competition, greater flexibility of
products and more demands from customers, firms are now required to operate their businesses in
highly complex and dynamic environments. Organisations that survive and succeed in these market
conditions need to make decisions in a timely, effective and appropriate manner (Habjan and
Popovic, 2007). However, many organisations are faced with the challenges of data overload where
small subsets of large amounts of data are key to the overall evaluation of information (Arnott and
Pervan, 2005). For example, the International Data Corporation (IDC) reported that digital data
growth was up by 48 percent in 2012, with 90 percent of information being unstructured. As a result
of this type of data complexity, many businesses are now challenged to understand and analyse the
wide range of information involved (Gens, 2011). However, as many business users lack access to
the information they need, many tend to make decisions based on instinctive knowledge that can
result in loss of productivity, reduced agility in the marketplace, and flawed decision-making
(Hilgefort, 2010). In this situation, it is important to seek ways to provide useful information that
supports decision makers and adds value to business organisations.
In order to increase efficiency, many organisations have implemented IT systems in their business
operations to collect, combine, access, and analyse massive amounts of data. One such analytical
tool is BI technology that turns data into information and then into knowledge (Golfarelli et al.,
2004). BI technology supports firms not only in driving performance improvement throughout their
enterprises (Hill and Scott, 2004), but also assists in forecasting by analysing historical data
(Marjanovic, 2007). For example, in conducting a survey among 2,053 Chief Information Officers
(CIO) covering 36 industries in 41 countries, Gartner Research (2013) found that BI technology is
often a first priority in technology investments. This finding agrees with O'Brien and Kok (2006) and
Kimball et al. (2008) who found that BI technology had reached a stage of maturity that is widely
used at all levels of the business world.
Demand for BI technology has continually grown even at a time when the demand for many IT
products has decelerated (Soejarto, 2003). Recently, the International Data Corporation (IDC, 2013)
reported that the Business Intelligence market had grown by 8.7% in 2012, while the total software
market and total Information Communication Technology (ICT) market had grown by only 2.9% and
3.6%, respectively. BI technology is therefore expected to continue to grow, albeit at single digit
rates, over the next few years (IDC, 2013). However, despite BI technology being normally
considered as reserved for larger firms, the current demand for BI is not restricted to firm size
(Pegasus Software, 2008). Indeed, both small and medium-sized enterprises now have as much need
for BI utilisation as the larger companies (LogicXML, 2009).
The accepted classifications of SMEs differ, not only from industry to industry but also from country
to country (Atkins and Lowe, 1997), depending on the political and economic objectives defining
them (Simpson et al., 2004). However, the most common criteria applied to classify the term SME,
includes number of employees, invested capital, annual turnover and industry type (Ministry of
Economic Development, 2011). According to the European Union, an SME is defined as a company
with fewer than 250 employees and having either an annual turnover of less than 50 million euro or
an annual balance sheet total of less than 43 million euro. In particular, a small business is one which
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employs fewer than 50 persons and whose annual turnover or annual balance sheet total does not
exceed 10 million euro (European Commission, 2003).
The European Commission (2008) claimed that more than 95 percent of enterprises fall within the
SME group as the main driver of the world’s economy. As SMEs employ the majority of workers, they
contribute to the economic growth of most countries, and are thus widely recognised as vital to
economic development and expansion (Levy and Powell, 2005, Roy and Sander, 2004). As a result,
the majority of governments support the growth of SMEs as a priority via the creation of various
programs including technical support, training, regulatory provisions and policy interventions
(Madrid-Guijarro et al., 2009, Esselaar et al., 2007, Chen, 2006). However, even though SMEs are
often supported by governments, most SMEs underestimate the value of IT innovations by limiting
them to administrative tasks rather than complex business operations (Festing, 2007). As a result,
SMEs have lagged in the BI uptake despite being an important part of enterprise decision support for
over two decades (Wirtschaft et al., 2010).
In an attempt to provide a conceptual framework to understand the adoption of BI among SMEs, the
research questions that guide this study are: (1) what is the state of BI adoption in SMEs and (2)
What are the enabling factors affecting the adoption of BI in SMEs. The remainder of this paper
discusses the term ‘BI’, and discuss the adoption of BI in SMEs. The following section reviews the
classification levels of BI to identify the current state of BI adoption among SMEs and develop a
conceptual framework for this study. Then the theoretical background for developing this
framework is presented, followed by potential factors that can affect the decisions for BI adoption.
The paper concludes with a summary and suggestions for future research directions.
LITERATURE REVIEW
What is Business Intelligence?
Although BI is not a new area of information systems (Vitt et al., 2002), academic research in this
field is at an early stage (Arnott and Pervan, 2005, Gibson et al., 2004, Negash, 2004) – with the term
being defined in various ways according to context (Niu et al., 2009). The bulk of BI literature
originates from the business world and IT industry (Gibson et al., 2004, Jagielska et al., 2003), with
the various consulting companies and software vendors judging BI as compatible with their
products, and promoting their particular connotations (Arnott and Pervan, 2005). For this reason,
there is currently no commonly agreed definition of BI. For example, Negash (2004) defined BI as a
system that combines “data gathering, data storage, and knowledge management with analytical
tools to present complex internal and competitive information to planners and decision makers”.
Turban et al. (2007) defined BI as “an umbrella term that encompasses tools, architectures,
databases, data warehouses, performance management, methodologies, and so forth, all of which
are integrated into a unified software suite”. Elbashir et al. (2008) defined BI as “a specialised tool
for data analysis, query and reporting that supports an organisational decision-making that
potentially enhances the performance of a range of business processes”. Watson (2009) defined BI
as “a broad category of applications, technologies, and processes for gathering, storing, accessing,
and analysing data to help business users make better decisions”.
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Although there is no commonly agreed upon definition of BI, existing definitions share two common
characteristics. The first is the fundamental aspect of BI which includes collecting, storing, analysing
and delivering information that is available both internally and externally, and the second is the aim
of BI which is to support the strategic decision-making process of the firm. However, a problem
arises when considering the existing definition of BI because it only discusses the process, software
and technology components. English (2005) claims that the key component of BI is to understand
what is occurring within the firm and what is the most suitable action to take in order to reach firm
goals. Therefore, the human factor is also important because BI cannot be evaluated independent of
interpreting its meaning but considered according to information gained from the practical
knowledge of the users. Furthermore, an earlier study of BI in Finnish companies by Hannula and
Pirttimaki (2003) found that more than 75 percent of responding business managers believed that
human ability to use BI represented a major aspect of its usage. For this reason, the definition of BI
in this paper adjusts Watson’s definition (2009) by including the aspect of human ability to use BI.
Accordingly, BI in this paper is defined as: the capability of an enterprise to use its human resources
together with a broad category of processes, applications and technologies for accessing, collecting,
accumulating and analysing data in order to generate actionable and competitive information that
can support its users to make better decisions.
Business Intelligence in SMEs
The advantages of implementing BI to support business operations are clear, and by utilising BI
technology appropriately, a number of benefits can be anticipated (Ko and Abdullaev, 2007, Watson
and Wixom, 2007, Ranjan, 2005). Many studies have reviewed the potential benefits of adopting BI
in various types of business (Popovic et al., 2010, Sahay and Ranjan, 2008, Ko and Abdullaev, 2007,
Ranjan, 2005, Anderson-Lehman et al., 2004, Eckerson, 2003). For instance, automobile
manufacturers have increased returns on investment (ROI) using a financial BI solution by identifying
repossessed vehicle loans more quickly. Electronics retailers have accrued substantial amounts of
money by identifying smaller quantities of out-of-stock items using BI solutions(Eckerson, 2003).
Similarly, BI has reduced inventory expenses through identifying more accurate information on
supplier shipments (Sahay and Ranjan, 2008). More recently, Dumitrita (2011) found that BI can also
help access more reliable and faster reports, improve decision making processes, increase the
quality of client relationships, increase incomes and cut non-IT expenses.
Although a large number of studies have focused on the benefits of BI implementation, research
focusing on SMEs is scarce. In one study Hill and Scott (2004) showed that some SMEs have the
ability to access both historical and short-term order and production figures using BI applications,
and in another study Wirtschaft et al. (2010) found that BI adoption benefited German SMEs by
improving data and decision support, and cost savings. However, even though most SMEs realise
that IT adoption will help them improve their businesses, many fail to take advantage (Bruque and
Moyano, 2007), and BI is still far from being widely used (Voicu et al., 2009).
According to technology adoption research in SMEs, a number of researchers have found that the
structural characteristics of SMEs are different from those in large enterprises, which explicitly
affects their IT usage behaviours (Gutierrez et al., 2009, Struker and Gille, 2008, Buonanno et al.,
2005). Man et al. (2002) point out that a small enterprise is not a small version of a large enterprise,
but has dissimilarities in terms of structure, policy making procedures, and utilisation of resources.
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Another study on SMEs by Deros et al. (2006) classifies these dissimilarities in terms of structures,
systems and procedures, cultures and behaviours, human resources, and markets and customers. In
accordance with these understandings, this paper does not directly apply the concepts used to
conduct research into large organisations to the study of SMEs.
Lack of resources is one of the key characteristics many researchers address when studying SMEs
(Bhaird and Lucey, 2010; Deros et al., 2006; Knight et al., 2004; Levy and Powell, 2003). These limited
resources include finance, technology, knowledge and human resources. In particular, Bhaird and
Lucey (2010) found that financial resources are personally funded by the owner in most SMEs. Due
to their restricted financial budgets and low number of employees in SMEs, the majority of
employees perform multiple tasks which do not specialise in any particular area (Kirchmer, 2011,
Hudson et al., 2001). Moreover, the unskilled workforce with lack of technical specialisation results
in SME managers being conservative when adopting IT innovations (Karkoviata, 2001). This may be
the reason why many SMEs are reluctant to invest in new technologies and overly careful in
assessing any investment strategies involving IT (Nguyen, 2009). For instance, Fuller-Love (2006)
found that owner-managers in SMEs deal with IT adoption only when they perceive the promise of
success, because they do not want to take risks. This is especially the case for BI, since BI
maintenance and implementation costs are very high as is the failure rate of implementation when
compared to other technologies (Lawton, 2006). Also, Hill and Scott (2004) conducted in-depth
discussions with eleven SMEs located in Northern Ireland to find that BI technologies are not widely
implemented in SMEs because they depend on personal contact networks as a fundamental way of
doing business, and are unable to rationalise BI investments for financial benefits. Moreover, due to
their limitations in both human and financial resources and scale and complexity of operations,
SMEs require different BI approaches from those adopted by large firms (Barnard, 2010).
The level of BI adoption
As the term BI can refer to both simple and complex technologies, the need for classifying BI levels is
important. Organisations that adopt high levels of BI tend to have characteristics that are distinct
from those with lower levels, and as a result have different enabling factors underpinning BI
adoption (Olszak, 2013, Ong and Siew, 2013). However, as the number of studies on levels of BI
adoption is limited (Sacu and Spruit, 2010), this section reviews the few existing studies that have
categorised the levels of BI adoption. For example, when categorising BI levels in terms of
technologies, (Gibson and Arnott (2003)) proposed five levels:
1) Personal decision support
2) Executive information systems
3) Data warehousing
4) Intelligence systems
5) Knowledge management.
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However, McDonald (2004) preferred to define BI levels from the solutions perspective, stating that
data structure positively impacts the efficiency of BI solutions. His framework comprised four levels:
1) BI infrastructure: refers to the process of collecting, integrating and transforming data in
order to generate the report for supporting decision-making;
2) Business Performance Management (BPM): refers to the use of data from level 1) above to
provide feedback based on Key Performance Indicators (KPI) to management;
3) Decision enablement: emphasises the use of data from a knowledge repository to generate
automatic decisions; and
4) Business Activity Monitoring (BAM): refers to the process of monitoring changes or trends
to assist users in taking the right action.
Other studies defined BI as not only a technology but also a process that transforms data into
information and then knowledge, with the argument that BI involves other entities such as
organisational function and human interaction, and applied the concept of a maturity model to
explain the levels of BI adoption (Lahrmann et al., 2010, Najmi et al., 2010, Eckerson, 2007, English,
2005). As Klimko (2001) explained, Maturity models are characterised by sequentially ordered levels
with specific requirements at each level. In the BI context, the most commonly used maturity model
is the Information Evolution Model (IEM) proposed by SAS, the leading company in business
analytics software and services (Davis et al., 2006). The purpose of this model is to study the
enablers of BI usage and explain firms’ use of information to improve business, thus classifying BI
adoption levels across four critical dimensions as follows:
1) Infrastructure: includes the implementation of technologies including hardware, software
and networking tools, to create, handle, store, distribute and apply information;
2) Knowledge process: includes the role of information in corporate knowledge sharing, the
role of information in decision-making, and the improvement of information accuracy and
quality;
3) Human capital: includes capabilities, responsibilities, decision-making, training, enterprise
goals and improvement of personnel skill-sets related to technological information; and
4) Culture: includes the moral, social and behavioural norms of corporate culture in relation
to the information flow within an organisation.
However, as this IEM model (Davis et al., 2006) does not address the analytical application of BI, the
present paper adds another dimension, ‘Application’ as derived from Sacu and Spruit (2010):
5) Application: includes analytic applications that organisations have implemented from basic
software programs that generate reports to advanced programs that detect relationships
in the data, provide predictive results, and generate an automated exception reporting
when something unusual occurs.
Recognition of the relationships between these five dimensions makes the IEM model presented in
this current study unique. Furthermore, in accordance with Davis et al. (2006), each dimension is
given five levels of maturity in the following order:
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1) Operate: This basic level of BI adoption is found in organisations that focus only on general
information from day-to-day operations, without long-range plans;
2) Consolidate: This next level refers to organisations that consolidate information by
integrating and storing information at the department level for supporting decision-making;
3) Integrate: Organisations at this level collect data in a central data warehouse to gain new
knowledge from performing enterprise-wide analysis and bridge the borders of separated
departments;
4) Optimise: At this level organisations use new technologies for deeper analysis in order to
better understand the marketplace and their customers in comparison to their
competitors, to better serve their customers; and
5) Innovate: Organisations at this highest level seek ways to reinvent and transform their
value position for sustainable growth.
As shown above, this IEM maturity model can assist organisations to assess their use of current
information resources and rank themselves on one of the five levels in order to decide their business
direction. Therefore, to address the research question regarding the current state of BI adoption by
SMEs, the levels of BI are categorised primarily using the IEM model. Also, due to SMEs in different
levels possibly having different enabling factors to BI adoption, all five levels of BI from this IEM
model are included in the conceptual framework (see Figure 1).
Figure 1: The key enabling factors of BI adoption in SMEs
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CONCEPTUAL FRAMEWORK
Based on a multi-perspective framework, three adoption models, namely the Diffusion of Innovation
theory (Rogers, 1995), Technology-Organisation-Environment model (Tornatzky and Fleischer, 1990),
and Information Systems Adoption Model for Small Business (Thong, 1999), have been selected as
the basic foundation for development of the conceptual model in this paper. Using these three
models, possible enabling factors impacting innovation adoption are categorised into four
characteristics including technological, organisational, environmental, and owner-managers.
However, due to the limited number of studies related to BI in SMEs, the enabling factors in this
paper have been developed based on prior research into technologies related to BI, in the context of
both large enterprises and SMEs.
Technological characteristics
In this paper, the technological characteristics of BI are based on the Diffusion of Innovation theory
as proposed by Rogers (1983). According to this theory, the attributes affecting technological
innovation adoption are relative advantage, complexity, compatibility, trialability and observability.
Chen (2003) also employed these attributes to examine electronic businesses and found that they
influence technological innovation adoption. As BI is an innovation technology (Ramamurthy et al.,
2008), in this paper the possible factors affecting BI adoption will build on the studies of Rogers
(1995) and Chen (2003).
Relative advantage of technologies refers to the degree an innovation is perceived as being better
than existing ideas or systems (Rogers, 1995). Prior research studies indicated that BI technology can
offer several advantages to firms. For example, retail companies used Data Analysis tools in BI
technology to determine which of their products are most profitable, and where to place them in
their stores. The banking industry used BI to create better processes for checking credentials and
generating credit reports of customers (Williams and Williams, 2003). Furthermore, using
complicated BI tools, banks have been better able to detect money laundering (Khan et al., 2010).
However, in spite of these benefits, Information Week (cited in Khan et al., 2010) conducted a study
of 388 technology professionals in the United States in 2007 and revealed that more than 30 percent
of their respondents claimed that BI vendors cannot explain the benefits of BI to the stakeholder. If
the BI vendors have no ability to explain the benefits of BI, then the customers are unable to justify
the adoption of BI applications. Furthermore, a study by O'Brien and Kok (2006) found that the full
benefits of BI are not entirely understood by firms due to a lack of communication.
Complexity is the degree to which an innovation is perceived to be difficult to understand or use
(Rogers, 1995). Many researchers have found that complexity is a barrier to innovation adoption
(Chang et al., 2010; Alam et al., 2008; Sahay and Ranjan, 2008; Bradford and Florin, 2003).
Ramamurthy et al. (2008) found that lower complexities in a technology resulted in higher positive
effects on the adoption of Data warehousing solutions. Therefore, due to the high complexity of BI
technology, employees resist its adoption and continue to use traditional spreadsheet technologies
(The Economist Intelligence Unit, 2007). Voicu et al. (2009) confirmed that BI models are highly
complicated because they integrate mathematical functions to predict trends in a firm’s
performance to provide solutions in a variety of situations. Therefore, users with a weak IT and
computing knowledge require a simple and stable solution that will meet their needs in the shortest
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time.
Compatibility is the degree to which an innovation is perceived to be consistent with existing values,
past experiences, and needs of possible adopters (Rogers, 1995). Some researchers claim that BI
systems are an expansion of Enterprise Resource Planning (ERP) systems, and provide higher
performances in consolidating, transforming and analysing data (Hawking and Sellitto, 2010,
Radding, 2000). Moreover, Voicu et al. (2009) regarded ERP systems as a minimal prerequisite for
implementing BI tools. Any firms that have implemented an ERP system have to decide whether to
employ their current ERP vendors, that can reduce compatibility related problems, or to use another
BI vendor (Radding, 2000). Furthermore, the Business Intelligence Guide (2009) reported that 40
percent of BI project costs were related to the development of analytics and transformation of data
between systems. If existing systems are not compatible with the BI technology, it will take a
significant investment of time and resources to migrate and integrate data.
Trialability is the extent to which potential adopters have the opportunity to experiment with an
innovation (Rogers, 1995). The higher the trialability, the more comfortable the potential adopters
are with the technology and the more likely will be its adoption. Therefore, if BI providers give
potential users opportunities to experience BI systems before adoption, doubts related to the
unknown will be diminished. A number of empirical studies have confirmed that the perceived
trialability of innovation had an impact on potential user adoption of diverse IT such as information
retrieval systems (Venkatesh and Morris, 2000), Business-to-Business (B2B) e-marketplaces (White
et al., 2007), and e-learning (Zhang et al., 2010). Based on a study of B2B adoption in healthcare
industries, White et al. (2007) found that trialability was important in decisions for adoption, in
which hospital professionals test new procedures before rolling out the B2B procedures more
widely. Moreover, based on a survey of 102 SMEs located in the Northwest of England, Ramdani et
al. (2009) found that trialability had an impact on the adoption of enterprise systems including ERP,
Customer Relationship Management (CRM) and Supply Chain Management (SCM).
Observability is the degree to which potential adopters of an innovation can perceive the results of
using that innovation from users who have already adopted it (Rogers, 1995). Lundblad (2003)
claimed that the visible results of an innovation affect the perceptions of its value by both
individuals and communities. Moreover, the visibility of results stimulated them to communicate
about the innovation, as peers were found to frequently request information related to the
evaluation of an innovation. Therefore, readily observable innovation effects normally lead to rapid
adoption. In a study on e-commerce adoption by Alam et al. (2008), a survey conducted among 194
electronic manufacturing firms in Malaysia (in which 75 percent were SMEs), showed that
observability is a significant factor in e-commerce adoption. Chiasson and Lovato (2001) also found
that the observability of decision support system (DSS) benefits were a significant factor in DSS
adoption. A recent study of BI adoption in telecommunications companies in Malaysia by Ahmad
(2011) found that the perceived observability of BI had a positive effect on the success of its
deployment in companies.
Organisational characteristics
Organisational characteristics are based on the Technology-Organisation-Environment (TOE) model
proposed by Tornatzky and Fleischer (1990). Although Roger’s model of innovation contributed to
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explaining the foundations of technological innovations, some studies have suggested that
technological innovation alone is not sufficient to guarantee success in diffusion of technology (Surry
and Ely, 1999, Pool, 1997). The ability of organisations to adopt and implement technological
innovation is also a considerable issue affecting the adoption decision. In the organisational
dimension, there are four possible enabling factors that many researchers have used as a predictor
of whether an organisation should adopt innovation or not: organisational size, organisational age,
absorptive capacity, and organisational resource availability.
A number of studies have investigated the adoption of IT in relation to the organisational size factor
and found this factor has a strong influence on the innovation adoption (Jang et al., 2009,
Ramamurthy et al., 2008, Lee and Xia, 2006). However, these results are mixed and inconsistent,
with some arguing that small firms are more efficient in adopting innovation than larger firms
(Yeaple, 1992), and others suggesting that large companies have more financial and human
resources that meet the requirements for innovation (Scuilli, 1998). Lee and Xia (2006) conducted a
study using a statistical meta-analysis methodology for merging the results of many individual
analyses in the same area. They analysed 21 empirical studies and concluded that organisational size
and technological innovation adoption have a positive relationship, but the direction and strength of
that relationship depends on moderators including innovation type, organisation type, adoption
stage, scope of size, and type of size measurement. In earlier versions of BI, due to its high
complexity and cost, implementation normally required the financial resources and skilled workers
(Sahay and Ranjan, 2008) which are in accord with the capabilities of larger companies. In
agreement, O'Brien and Kok (2006) suggest that due to the cost and complexity of BI architecture,
the suitable size for firms implementing BI should be more than 100 employees. Conversely, from
the perspective of BI vendors, BI applications are now more diverse, flexible, cheap and less complex
than in the past, offering targeted products that are specially tailored for a wide range of SMEs with
financial and resources constraints (LogicXML, 2009).
Organisational age refers to the length of the firm’s time in business operation. This factor was also
found to have an influence on organisations’ decisions to adopt an innovation. For example, in an
earlier study Kimberly and Evanisko (1981) found that organisational age negatively related to the
adoption of innovations, because newer organisations were using new technologies as an
organisational strategy for defining a niche, while the older organisations were already firmly
established. Rogers (1995) found that as newer organisations are born into an environment
saturated with advanced communication and information technologies, they naturally rely on
technologies to achieve competitive advantage. A later study by Daniel and Myers (2000) also found
that the older the organisation, the more likely it will resist to engagement in an innovation process
because it cannot easily change routines and is unable to adapt innovative activities that respond to
externally generated major technical advances. In agreement, a study by Flanagin (2000) found that
newer organisations are more prone to innovation adoption, particularly as they need to
complement existing systems and goals.
Griffith et al. (2003) define the absorptive capacity of an organisation as the ability of its members in
using existing or pre-existing knowledge. This ability helps organisations to increase recognition
about the value of new and external information, and as a result, can be applied to increase the
economic benefits of the company. Moreover, absorptive capacity can be used as a predictor of the
organisation’s ability to adopt innovation or not (Lal, 2007). BI technologies require awareness and
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understanding from its users. These requirements can provide the potential for development of IT
within the firms context (O'Brien and Kok, 2006). O'Brien and Kok (2006) conducted a study on
telecommunication firms in South Africa and found that many organisations were not utilising BI to
the full potential due to the lack of knowledge, shortage of technical skills, and lack of training.
Organisational resource availability is another factor that many studies have identified as influencing
innovation adoption (Adler-Milstein and Bates, 2010; Oliveira and Martins, 2010; Soares-Aguiar and
Palma-dos-Reis, 2008). Managers will support the adoption of new technology when capital,
equipment, human resources and organisational time to implement are available (Chong et al.,
2009). Scupola (2003) found that resource unavailability prevented Taiwan’s SMEs from investing in
ERP. In the BI context, implementation normally requires financial resources and skilled workers due
to its complexity and high cost (Sahay and Ranjan, 2008).
Environmental characteristics
Environmental characteristics are based on the Technology-Organisation-Environment (TOE) model.
Environmental factors are commonly and frequently used as a key determinant of innovation
adoption (Damanpour and Schneider, 2006). It is necessary to examine the influence of
environmental factors before adopting a technology because competitive pressure and the selection
of vendors influence the success of innovation adoption.
Competitive pressure tends to stimulate firms to look for new approaches to raise their efficiency
and increase productivity, that leads to firms achieving competitive advantage (Themistocleous et
al., 2004). Waarts et al. (2002) found that competitors were the key drivers in innovation technology
adoption. This is particularly so when competitive pressure significantly impacts on IT adoption
(Premkumar et al., 1997, Iacovou et al., 1995, Premkumar and Ramamurthy, 1995, Mansfield et al.,
1977). For example, in more recent research studies on SMEs, Alshawi et al. (2011) found that
competitive pressure was an important influence on organisational adoption of CRM systems.
Another study on data warehouse technology adoption by Hwang et al. (2004) found that
environmental attributes including the degree of competitive pressure and vendors selection, were
key factors in data warehouse adoption.
Vendor selection is another environmental factor affecting the adoption of technology (Ghobakhloo
et al., 2011, Lin and Hsu, 2007, Hwang et al., 2004, Chau and Hui, 2001). According to Seyal et al.
(2004)’s study, the variable ‘vendor selection’ can be grouped and measured by quantifying the
following items: the vendors’ reputation and successful experience possessed; vendors’ technical
competence with the specific BI system proposed; and the professional competence of the
consultant. In general, vendors are responsible for providing software, hardware, user training and
technical support to customers in order to maintain their optimal performance (Moffett and
McAdam, 2003). In the study by Hwang et al. (2004), the authors found a relationship between BI
vendor selection and technology adoption. As BI is different from other enterprise information
technologies, it requires a tailored solution to suit each particular firm and industry, and not just a
total package (Hill and Scott, 2004).
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Owner-manager characteristics
Owner-manager characteristics are fundamental to the Information Systems Adoption Model for
Small Business proposed by Thong and Yap (1996). Ghobakhloo et al. (2011) claim that SMEs
generally have simple and highly centralised structures, with authority mainly being given to the
Chief Executive Officer (CEO), and, oftentimes, the owner and CEO are the same person. Thus, the
owner-manager is the sole decision-maker having a direct effect on the decision-making processes
ranging from daily functions to future investments (Nguyen, 2009; Bruque and Moyano, 2007;
Jarvenpaa and Ives, 1991). A study by Thong (1999) proposed that owner-managers who have
innovativeness and an IT background have increased potential for IT adoption success.
According to Zhu et al. (2003), innovativeness refers to a willingness to introduce newness and
novelty through experimentation and creative process aimed at developing new products, services,
and process. A study by Chang et al. (2010) found that that an owner-managers’ innovativeness is a
significant determinant in ERP adoption for SMEs. Similarly, a survey of 325 manufacturing SMEs
located in the central industrial sector of Iran found that the innovativeness of an owner-manager
significantly impacted on their adoption of e-commerce (Ghobakhloo et al., 2011).
Owner-managers’ IT background (or knowledge) is another trait influencing the adoption of IT in
SMEs (Ghobakhloo et al., 2011; Drew, 2003; Fink, 1998). According to Grover (1993), ownermanagers’ IT background refers to experience and knowledge in information technology. This
attribute involves the background of the managers, their experience and awareness in IT activities,
their recognition of IT potential, as well as their ability to plan strategically. Thong and Yap (1995)
claimed that owner-managers who have more IT knowledge will be more likely to be innovative.
Chao and Chandra (2012) conducted a survey with 217 small manufacturers and financial services
organisations in the USA and found that the level of an owner’s IT knowledge is a key predictor of IT
strategic alignment, as well as the adoption of IT. An interesting aspect of this finding is that
although owner-managers’ IT knowledge can increase the chances of IT adoption in organisations,
advanced IT applications including CRM and BI have received quite low rates of adoption among
smaller firms due to the critical constraints of resources in such firms.
As shown in the research framework (Fig. 1), the above review of studies in the IT domain found
thirteen potential enabling factors under four characteristics affecting innovation adoption. These
four characteristics consist of Technological, Organisational, Environmental and Owner-managers.
First, the Technological characteristics include five factors: Relative Advantage, Complexity,
Compatibility, Trialability and Observability. Second, the four factors under Organisational
characteristics include: Organisational size, Organisational age, Absorptive capacity and
Organisational resource availability. Third, the two factors under Environmental characteristics
include: Competitive pressure and Selection of vendors. Fourth, the two factors under Ownermanager characteristics include: Owner-managers’ innovativeness and Owner-managers’ IT
knowledge. Furthermore, in order to allow for different enabling factors or characteristics that may
impact SMEs’ decisions to adopt at each level of current BI, this framework also includes a
classification of SME’s current BI adoption into five levels: Operate, Consolidate, Integrate, Optimise
and Innovative, ranging from the lowest to highest, with Operate being the lowest.
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CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
The conceptual framework outlined in this paper aims to provide an understanding of the current
adoption levels of BI in Thai SMEs and the enabling factors that may influence SMEs’ adoption of BI.
Based on the framework of this study, SMEs should be classified into different levels of BI adoption
before examining the factors that impact their decision on BI adoption. Rather than considering the
BI adoption as a binary function, to adopt or not to adopt, the classification of organisations allows
researchers to explore the enabling factors that impact each of the BI adoption levels and to gain an
understanding of whether SMEs in the upper BI level have different impact factors from those in the
lower levels. For this reason, different BI level groups of organisations will require different types of
attention to help them prolong their use of BI technologies. According to the multiple-perspective
framework that was used as the foundation for developing the conceptual framework in this paper,
thirteen potential enabling factors under four characteristics that influence technological innovation
adoption are found to drive the organisations’ decision to adopt technological innovation. These
four characteristics are Technology, Organisation, Environment and Owner-managers. Because the
BI adoption framework of this paper has been based on the literature in the area of IT, it is open and
requires testing to determine its relevancy and validity in the practical environment. Furthermore, as
the proposed model may not be complete, future studies may find additional factors that can
potentially affect BI adoption. All enabling factors in the framework can provide testable hypotheses
for future researchers to examine (see Table 1).
Table 1: Summary of testable hypotheses
Enabling factors
Hypotheses
Technological Characteristics
Relative advantage
H1: Relative advantage has an effect on the adoption of BI in SMEs.
Complexity
H2: Complexity has an effect on the adoption of BI in SMEs.
Compatibility
H3: Compatibility has an effect on the adoption of BI in SMEs.
Trialability
H4: Trialability has an effect on the adoption of BI in SMEs.
Observability
H5: Observability has an effect on the adoption of BI in SMEs.
Organisational Characteristics
Organisational size
H6: Organisational size has an effect on the adoption of BI in SMEs.
Organisational age
H7: Organisation age has an effect on the adoption of BI in SMEs..
Absorptive capacity
H8: Absorptive capacity has an effect on the adoption of BI in SMEs.
Organisational
resource availability
H9: Organisational resource availability has an effect on the adoption of BI
in SMEs.
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Environmental Characteristics
Competitive pressure
H10: Competitive pressure has an effect on the adoption of BI in SMEs.
Vendor selection
H11: Vendor selection has an effect on the adoption of BI in SMEs.
Owner-managers Characteristics
Owner-managers’
innovativeness
H12: Owner-managers’ innovativeness has an effect on the adoption of BI
in SMEs.
Owner-managers’ IT
knowledge
H13: Owner-managers’ IT knowledge has an effect on the adoption of BI in
SMEs.
The contribution of the conceptual framework of this paper is to advance knowledge and provide a
clearer understanding of the BI adoption rates and factors contributing to the process for BI
adoption in SMEs. In utilising this framework, prospective outcomes are expected to form a basis for
both government agencies and IT providers to determine the current levels of BI adoption in SMEs.
By categorising SMEs into different groups, they can design policies and tailor the products that are
most appropriate to their organisations, which will ultimately accelerate BI diffusion among other
SMEs. The government and IT providers will be able to adjust their policies and allocate resources
more efficiently in the areas (factors) that they determine to be overlooked and under-resourced, in
order to introduce the more pertinent technologies into SMEs. Technology alone is not sufficient to
guarantee the adoption of IT innovation by organisations, because other social, technical,
organisational and individual factors may impact on the selection and adoption of technologies. The
development of this new research framework is expected to guide future studies in the growing area
of academic inquiry into IT adoption. This framework has the potential of being a research tool for
examining enabling factors in the adoption of other technological innovations in the SME context.
Further studies could also investigate the reasons for slow IT adoption in SMEs, and investigate ways
to overcome inhibitors to IT adoption.
REFERENCE
Adler-Milstein, J. and Bates, D. W. 2010. Paperless healthcare: Progress and challenges of an ITenabled healthcare system. Business Horizons, 53, 119-130.
Ahmad, A. 2011. Business Intelligence for Sustainable Competitive Advantage: The Case of
Telecommunications Companies in Malaysia. Doctor of Philosophy, Curtin University of
Technology, Australia.
Alam, S. S., Khatibi, A., Ahmad, M. I. S. and Ismail, H. B. 2008. Factors affecting e-commerce adoption
in the electronic manufacturing companies in Malaysia. International Journal of Commerce and
Management, 17, 125-139.
Alshawi, S., Missi, F. and Irani, Z. 2011. Organisational, technical and data quality factors in CRM
adoption--SMEs perspective. Industrial Marketing Management, 40, 376-383.
th
27 Annual SEAANZ Conference 2014
Page | 14
Anderson-Lehman, R., Watson, H. J., Wixom, B. H. and Hoffer, J. A. 2004. Continental airlines flies
high with real-time business intelligence. MIS Quarterly Executive, 3, 163-176.
Arnott, D. and Pervan, G. 2005. A critical analysis of decision support systems research. Journal of
Information Technology Cases & Applications, 20, 67-87.
Atkins, M. H. and Lowe, J. F. 1997. Sizing up the small firm: UK and Australian experience.
International Small Business Journal, 15, 42-55.
Barnard, S. 2010. Business Intelligence for SMEs [Online]. Sydney: Inside Info. Available:
http://www.insideinfo.com.au/files/business@20intelligence@[email protected] [Accessed 12
Aug 2011].
Bhaird, C. and Lucey, B. 2010. Determinants of capital structure in Irish SMEs. Small Business
Economics, 35, 357-375.
Bradford, M. and Florin, J. 2003. Examining the role of innovation diffusion factors on the
implementation success of enterprise resource planning systems. International Journal of
Accounting Information Systems, 4, 205-225.
Bruque, S. and Moyano, J. 2007. Organisational determinants of information technology adoption
and implementation in SMEs: the case of family and cooperative firms. Technovation, 27, 24153.
Buonanno, G., Faverio, P., Pigni, F., Ravarini, A., Sciuto, D. and Tagliavini, M. 2005. Factors affecting
ERP system adoption: a comparative analysis between SMEs and large companies. Journal of
Enterprise Information Management, 18, 384-426.
Chang, S. I., Hung, S. Y., Yen, D. C. and Lee, P. J. 2010. Critical Factors of ERP Adoption for Small-and
Medium-Sized Enterprises: An Empirical Study. Journal of Global Information Management
(JGIM), 18, 82-106.
Chao, C. A. and Chandra, A. 2012. Impact of owner's knowledge of information technology (IT) on
strategic alignment and IT adoption in US small firms. Journal of Small Business and Enterprise
Development, 19, 114-131.
Chau, P. Y. and Hui, K. L. 2001. Determinants of small business EDI adoption: an empirical
investigation. Journal of Organizational Computing and Electronic Commerce, 11, 229-252.
Chen, J. 2006. Development of Chinese small and medium-sized enterprises. Journal of Small
Business and Enterprise Development, 13, 140-147.
Chen, M. 2003. Factors affecting the adoption and diffusion of XML and Web services standards for
E-business systems. International Journal Human-Computer Studies, 58, 259-279.
Chiasson, M. W. and Lovato, C. Y. 2001. Factors influencing the formation of a user's perceptions and
use of a DSS software innovation. ACM SIGMIS Database, 32, 16-35.
Chong, A. Y. L., Ooi, K. B., Lin, B. and Raman, M. 2009. Factors affecting the adoption level of ccommerce: an empirical study. Journal of Computer Information Systems, 50, 13-22.
Damanpour, F. and Schneider, M. 2006. Phases of the Adoption of Innovation in Organisations:
Effects of Environment, Organisation and Top Managers. British Journal of Management, 17,
215-236.
Daniel, E. and Myers, A. 2000. Levelling the playing field: electronic commerce in SMEs. Capstone
Publishing Limited.
th
27 Annual SEAANZ Conference 2014
Page | 15
Davis, J., Miller, G. J. and Russell, A. 2006. Information revolution: using the information evolution
model to grow your business, Wiley.
Deros, B. M., Yusof, S. M. and Salleh, A. M. 2006. A benchmarking implementation framework for
automotive manufacturing SMEs. Benchmarking: An International Journal, 13, 396-430.
Drew, S. 2003. Strategic uses of e-commerce by SMEs in the east of England. European Management
Journal, 21, 79-88.
Dumitrita, B. M. 2011. Business Intelligence. Journal of Information Systems & Operations
Management, 5, 175-181.
Eckerson, W. 2003. Smart companies in the 21st century: the secrets of creating successful business
intelligence solutions. TDWI Report Series, 1-38.
Eckerson, W. W. 2007. Predictive Analytics–Extending the Value of Your Data Warehousing
Investment. TDWI Best Practice Report, The Data Warehousing Institute, Renton.
Elbashir, M., Collier, P. A. and Davern, M. 2008. Measuring the effects of business intelligence
systems: The relationship between business process and organisational performance
International Journal of Accounting Information Systems 9, 135-153.
English, L. P. 2005. Business Intelligence Defined [Online]. Beyenetwork. Available: http://www.beye-network.com/view/1119 [Accessed 03 March 2012].
Esselaar, S., Stork, C., Ndiwalana, A. and Deen-Swarray, M. 2007. ICT Usage and Its Impact on
Profitability of SMEs in 13 African Countries. Information Technologies & International
Development, 4.
European Commission. 2003. The new SME definition [Online]. Available:
http://ec.europa.eu/enterprise/policies/sme/files/sme_definition/sme_user_guide_en.pdf
[Accessed 31 May 2014].
European Commission. 2008. Enterprises by size class – overview of SMEs in the EU [Online]. Eurostat
Statistics in Focus. Available: http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-SF-08031/EN/KS-SF-08-031-EN.PDF [Accessed 28 April 2011].
Festing, M. 2007. Globalisation of SMEs and implications for international human resource
management research and practice. International Journal of Globalisation and Small Business, 2,
5-18.
Fink, D. 1998. Guidelines for the successful adoption of information technology in small and medium
enterprises. International journal of information management, 18, 243-253.
Flanagin, J. 2000. Social pressures on organizational website adoption. Human Communication
Research, 26, 618-646.
Fuller-Love, N. 2006. Management development in small firms. International Journal of
Management Reviews, 8, 175-190.
Gartner Research. 2013. Gartner Executive Program Survey of More Than 2,000 CIOs Shows Digital
Technologies Are Top Priorities in 2013 [Online]. Available:
http://www.gartner.com/newsroom/id/2304615 [Accessed 11 February 2014].
Gens, F. 2011. TOP 10 PREDICTIONS, IDC Predictions 2012: Competing for 2020. International Data
Corporation.
th
27 Annual SEAANZ Conference 2014
Page | 16
Ghobakhloo, M., Arias-Aranda, D. and Benitez-Amado, J. 2011. Adoption of e-commerce applications
in SMEs. Industrial Management & Data Systems, 111, 1238-1269.
Gibson, M. and Arnott, D. Business Intelligence for Small Business: Assessment, Framework &
Agenda. the 7th Pacific Asia Conference on Information Systems, 2003 Adelaide, Australia.
PACIS.
Gibson, M., Arnott, D., Jagielska, I. and Melbourne, A. Evaluating the intangible benefits of business
intelligence: review & research agenda. In: MEREDITH, R., ed. Proceedings of the 2004 IFIP WG
8.3 International Conference on Decision Support Systems (DSS2004): Decision Support in an
Uncertain and Complex World, 2004 Caulfield East, Victoria, Australia: Monash University.
Citeseer, 295-305.
Golfarelli, M., Rizzi, S. and Cella, I. Beyond data warehousing: what's next in business intelligence?
Proceedings of the 7th ACM International Workshop on Data Warehousing and OLAP, 2004.
Griffith, T. L., Sawyer, J. E. and Neale, M. A. 2003. Virtualness and knowledge in teams: managing the
love triangle of organisations, individuals, and information technology. MIS Quarterly, 27, 265287.
Grover, V. 1993. An Empirically Derived Model for the Adoption of Customer-based
Interorganisational Systems. Decision sciences, 24, 603-640.
Gutierrez, A., Orozco, J. and Serrano, A. 2009. Factors affecting IT and business alignment: a
comparative study in SMEs and large organisations. Journal of Enterprise Information
Management, 22, 197-211.
Habjan, A. and Popovic, A. 2007. Achieving Business Process Change With Improved Business
Intelligence Systems: A Case Of Slovenian Company. 7th WSEAS International Conference on
Applied Computer Science. Venice, Italy.
Hannula, M. and Pirttimaki, V. 2003. Business intelligence empirical study on the top 50 Finnish
companies. Journal of American Academy of Business, 2, 593-599.
Hawking, P. and Sellitto, C. 2010. Business Intelligence (BI) Critical Success Factors. ACIS 2010.
Hilgefort, I. 2010. Inside SAP BusinessObjects Explorer, Boston, SAP Press.
Hill, J. and Scott, T. 2004. A consideration of the roles of business intelligence and e-business in
management and marketing decision making in knowledge based and high-tech start-ups.
Qualitative Market Research: An International Journal, 7, 48-56.
Hudson, M., Smart, A. and Bourne, M. 2001. Theory and practice in SME performance measurement
systems. International Journal of Operations & Production Management, 21, 1096-1115.
Hwang, H. G., Ku, C. Y., Yen, D. V. and Cheng, C. C. 2004. Critical factors influencing the adoption of
data warehouse technology: A study of the banking industry in Taiwan. Decision Support
Systems, 37, 1-21.
Iacovou, C. L., Benbasat, I. and Dexter, A. S. 1995. Electronic data interchange and small
organisations: Adoption and impact of technology. MIS quarterly, 19, 465-485.
IDC. 2013. Worldwide Business Analytics Software 2013-2017 Forecast and 2012 Vendor Shares
[Online]. International Data Corporation. Available:
https://www.idc.com/getdoc.jsp?containerId=241689 [Accessed 9 February 2014].
th
27 Annual SEAANZ Conference 2014
Page | 17
Jagielska, I., Darke, P. and Zagari, G. Business intelligence systems for decision support : concepts,
processes and practice. 7th International Conference of the International Society for Decision
Support Systems, 2003.
Jang, W. Y., Lin, C. I. and Pan, M. J. 2009. Business strategies and the adoption of ERP: Evidence from
Taiwan's communications industry. Journal of Manufacturing Technology Management, 20,
1084 - 1098.
Jarvenpaa, S. L. and Ives, B. 1991. Executive involvement and participation in the management of
information technology. MIS quarterly, 205-227.
Karkoviata, L. 2001. Making the customer king. Asian Business, 37, 47-48.
Khan, A. M. A., Amin, N. and Lambrou, N. 2010. Drivers and barriers to business intelligence
adoption: A case of Pakistan. Information Systems Journal 1-23.
Kimball, R., Ross, M., Thornthwaite, W., Mundy, J. and Becker, B. 2008. The Data Warehouse
Lifecycle Toolkit, Indianapolis, Wiley & Sons.
Kirchmer, M. 2011. Small and Medium Enterprises Also Benefit from MPE. High Performance
Through Process Excellence, 147-157.
Klimko, G. Knowledge management and maturity models: building common understanding. The
Second European Conference on Knowledge Management, 2001 Bled, Slovenia. 269-278.
Knight, G., Madsen, T. K. and Servais, P. 2004. An inquiry into born-global firms in Europe and the
USA. International Marketing Review, 21, 645-665.
Ko, S. and Abdullaev, S. 2007. A study on the aspects of successful business intelligence system
development. Computational Science–ICCS 2007, 729-732.
Lahrmann, G., Marx, F., Winter, R. and Wortmann, F. Business Intelligence Maturity Models: An
Overview. VII Conference of the Italian Chapter of AIS (itAIS 2010), 2010 Naples, Italy.
Lal, K. 2007. Globalisation and the adoption of ICTs in Nigerian SMEs. Science Technology & Society,
12, 217-244.
Lawton, G. 2006. Making Business Intelligence More Useful. Computer, 39, 14-16.
Lee, G. and Xia, W. 2006. Organizational size and IT innovation adoption: A meta-analysis.
Information & Management, 43, 975-985.
Levy, M. and Powell, P. 2003. Exploring SME internet adoption: towards a contingent model.
Electronic Markets, 13, 173-181.
Levy, M. and Powell, P. 2005. Strategies for Growth in SMEs: The Role of Information and
Information Systems, Oxford, Elsevier Butterworth-Heinemann.
Lin, H.-Y. and Hsu, P.-Y. 2007. Application of the Analytic Hierarchy Process on Data Warehouse
System Selection Decisions for Small and Large Enterprises in Taiwan. International Journal of
Computer, the Internet and Management, 15.
LogicXML. 2009. BI for Small and Mid-sized Businesses [Online]. LogicXML. Available:
http://www.logixml.com/content/02_SMBMarket.pdf [Accessed 12 Aug 2011].
Lundblad, J. P. 2003. A review and critique of Rogers' diffusion of innovation theory as it applies to
organisations. Organization Development Journal, 21, 50-64.
Madrid-Guijarro, A., Garcia, D. and Van Auken, H. 2009. Barriers to innovation among Spanish
manufacturing SMEs. Journal of Small Business Management, 47, 465-488.
th
27 Annual SEAANZ Conference 2014
Page | 18
Man, T. W., Lau, T. and Chan, K. 2002. The competitiveness of small and medium enterprises: a
conceptualization with focus on entrepreneurial competencies. Journal of Business Venturing,
17, 123-142.
Mansfield, E., Rapoport, J., Romeo, A., Villani, E., Wagner, S. and Husic, F. 1977. The production and
application of new industrial technology, Norton New York.
Marjanovic, O. The next stage of operational business intelligence – creating new challenges for
business process management. In: SPRAGUE, R. H. J., ed. Proceedings of the 40th Annual Hawaii
International Conference on System Sciences (HICSS’07), 2007 Los Alamitos. IEEE Computer
Society.
McDonald, K. Is SAP the right infrastructure for your enterprise analytics'. American SAP User Group
Conference, 2004 Atlanta, GA.
Ministry of Economic Development. 2011. SMEs in New Zealand: Structure and Dynamics [Online].
New Zealand. Available: http://www.med.govt.nz/business/business-growthinternationalisation/pdf-docs-library/structure-and-dynamics-2011.pdf [Accessed 14 June
2011].
Moffett, S. and McAdam, R. 2003. Contributing and enabling technologies for knowledge
management. International Journal of Information Technology and Management, 2, 31-49.
Najmi, M., Sepehri, M. and Hashemi, S. The evaluation of Business Intelligence maturity level in
Iranian banking industry. 2010. IEEE, 466-470.
Negash, S. 2004. Business Intelligence. Communications of the Association for Information Systems,
13, 77-195.
Nguyen, T. H. 2009. Information technology adoption in SMEs: an integrated framework.
International Journal of Entrepreneurial Behaviour & Research, 15, 162-186.
Niu, L., Lu, J. and Zhang, G. 2009. Cognition-Driven Decision Support for Business Intelligence –
Models, Techniques, Systems and Applications, Berlin Heidelberg, Springer.
O'Brien and Kok, J. A. 2006. Business intelligence and the telecommunications industry: can business
intelligence lead to higher profits? South African Journal of Information Management, 8, 1-20.
Oliveira, T. and Martins, M. F. 2010. Understanding e-business adoption across industries in
European countries. Industrial Management & Data Systems, 110, 1337-1354.
Olszak, C. M. Assessment of business intelligence maturity in the selected organizations. Computer
Science and Information Systems (FedCSIS), 2013 Federated Conference on, 2013. IEEE, 951958.
Ong, I. L. and Siew, P. H. 2013. An Empirical Analysis on Business Intelligence Maturity in Malaysian
Organizations. International Journal of Information System and Engineering, 1, 1-10.
Pegasus Software. 2008. Using Business Intelligence to navigate in stormy times: an SME's Guide
[Online]. Pegasus Software Available: http://www.castle-cs.com/assets/downloads/Castle%20%20Business%20Intelligence%20White%20Paper.pdf [Accessed 12 Aug 2011].
Pool, R. 1997. Beyond engineering: How society shapes technology, Oxford University Press, USA.
Popovic, A., Turk, T. and Jaklic, J. 2010. Conceptual model of business value of business intelligence
systems. Management, 15, 5-30.
th
27 Annual SEAANZ Conference 2014
Page | 19
Premkumar, G. and Ramamurthy, K. 1995. The Role of Interorganisational and Organisational Factors
on the Decision Mode for Adoption of Interorganisational Systems*. Decision sciences, 26, 303336.
Premkumar, G., Ramamurthy, K. and Crum, M. 1997. Determinants of EDI adoption in the
transportation industry. European Journal of Information Systems, 6, 107-121.
Radding, A. 2000. Knowledge management appears on ERP radar [Online]. Datamation. Available:
http://itmanagement.earthweb.com/erp/article.php/621411/Knowledge-managementappears-on-ERP-radar.htm [Accessed 1 May 2011].
Ramamurthy, K., Sen, A. and Sinha, A. P. 2008. An empirical investigation of the key determinants of
data warehouse adoption. Decision Support Systems, 44, 817-841.
Ramdani, B., Kawalek, P. and Lorenzo, O. 2009. Predicting SMEs' adoption of enterprise systems.
Journal of Enterprise Information Management, 22, 10-24.
Ranjan, J. 2005. Business Intelligence: Concepts, Components, Techniques and Benefits. Journal of
Theoretical and Applied Information Technology, 9, 600-607.
Rogers, E. M. 1983. The Diffusion of Innovation, New Yok, Free Press.
Rogers, E. M. 1995. Diffusion of Innovations, New York, Free Press.
Roy, T. and Sander, W. 2004. Entrepreneurship, small business and economic growth. Journal of
Small Business and Enterprise Development, 11, 140-149.
Sacu, C. and Spruit, M. BIDM: The Business Intelligence Development Model. Proceedings of the
12th International Conference on Enterprise Information Systems, 2010 Funchal, MaderiaPortugal.
Sahay, B. S. and Ranjan, J. 2008. Real time business intelligence in supply chain analytics. Information
Management &Computer Security, 16, 28-48.
Scuilli, L. M. 1998. How organisational structure influences success in various types of innovation.
Journal of Retail Banking Services, 20, 13-18.
Scupola, A. 2003. The Adoption of Internet Commerce by SMEs in the South of Italy: An
Environmental, Technological and Organisational Perspective. Journal of Global Information
Technology Management, 6, 55-71.
Seyal, A. H., Awais, M. M., Shamail, S. and Abbas, A. 2004. Determinants of electronic commerce in
Pakistan: preliminary evidence from small and medium enterprises. Electronic Markets, 14, 372387.
Simpson, M., Tuck, N. and Bellamy, S. 2004. Small business success factors: the role of education and
training. Education+ Training, 46, 481-491.
Soares-Aguiar, A. and Palma-dos-Reis, A. 2008. Why do firms adopt e-procurement systems? Using
logistic regression to empirically test a conceptual model. Engineering Management, IEEE
Transactions on, 55, 120-133.
Soejarto, A. 2003. Tough Times Call for Business Intelligence Services [Online]. Available:
http://www.crn.com/news/channel-programs/18823134/tough-times-call-for-businessintelligence-services.htm [Accessed 27 December 2013].
th
27 Annual SEAANZ Conference 2014
Page | 20
Struker, J. and Gille, D. The SME Way of Adopting RFID Technology – Empirical Findings from a
German Cross-Sectoral Study. Proceedings of 16th European Conference on Information
Systems (ECIS 08), 2008 Galway, Ireland.
Surry, D. W. and Ely, D. P. 1999. Adoption, diffusion, implementation, and institutionalisation of
educational technology [Online]. Available:
http://www.southalabama.edu/coe/bset/surry/papers/adoption/chap.htm [Accessed 1 May
2012].
The Business Intelligence Guide. 2009. Drivers of Business Intelligence [BI] [Online]. The Business
Intelligence Guide Available:
http://www.thebusinessintelligenceguide.com/bi_strategy/Drivers_Of_BI.php [Accessed 1 May
2011].
The Economist Intelligence Unit. 2007. Business Intelligence: Putting Enterprise Data to Work
[Online]. EIU Oracle Sponsored Study. Available:
http://www.eiu.com/report_dl.asp?mode=fi&fi=252823010.PDF&rf=0 [Accessed 30 April 2011].
Themistocleous, M., Irani, Z., Kuljis, J. and Love, P. E. D. Extending the information system lifecycle
through enterprise application integration: a case study experience. 2004. IEEE, 8 pp.
Thong, J. 1999. An integrated model of information systems adoption in small business. Journal of
Management Information Systems, 15, 187-214.
Thong, J. and Yap, C. 1996. Information technology adoption by small business: an empirical study.
In: KAUTZ, K.andPRIES-HEJE, J. (eds.) Diffusion and adoption of information technology. London:
Chapman & Hall.
Thong, J. and Yap, C. S. 1995. CEO characteristics, organisational characteristics and information
technology adoption in small businesses. Omega, 23, 429-442.
Tornatzky, L. G. and Fleischer, M. 1990. The Process of Technology Innovation, MA Lexington Books.
Turban, E., Sharda, R., Aronson, J. and King, D. 2007. Business Intelligence, New Jersey, Prentice Hall.
Venkatesh, V. and Morris, M. G. 2000. Why Don't Men Ever Stop to Ask for Directions? Gender,
Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS quarterly,
24, 115-139.
Vitt, E., Luckevich, M. and Misner, S. 2002. Business Intelligence: Making Better Decisions Faster,
Microsoft Press.
Voicu, V., Zirra, D. and Ciocirlan, D. 2009. Business intelligence effective sollutions of management.
Proceedings of the 10th WSEAS international conference on Mathematics and computers in
business and economics. Prague, Czech Republic: World Scientific and Engineering Academy and
Society (WSEAS).
Waarts, E., Everdingen, Y. M. and Hillegersberg, J. 2002. The dynamics of factors affecting the
adoption of innovations. Journal of Product Innovation Management, 19, 412-423.
Watson, H. J. 2009. Tutorial: business intelligence–past, present, and future. Business, 25, 487-510.
Watson, H. J. and Wixom, B. H. 2007. The current state of business intelligence. Computer, 40, 96-99.
White, A., Daniel, E., Ward, J. and Wilson, H. 2007. The adoption of consortium B2B e-marketplaces:
An exploratory study. The Journal of Strategic Information Systems, 16, 71-103.
th
27 Annual SEAANZ Conference 2014
Page | 21
Williams, S. and Williams, N. 2003. The business value of business intelligence. Business Intelligence
Journal, 8, 30-39.
Wirtschaft, P., Schieder, C., Kurze, C., Gluchowski, P. and Böhringer, M. 2010. Benefits and
Challenges of Business Intelligence Adoption in Small and Medium-Sized Enterprises. ECIS 2010
Proceedings.
Yeaple, R. N. 1992. Why are small R&D organisations more productive? IEEE Transactions on
Engineering Management, 39, 332-346.
Zhang, L., Wen, H., Li, D., Fu, Z. and Cui, S. 2010. E-learning adoption intention and its key influence
factors based on innovation adoption theory. Mathematical and Computer Modelling, 51, 14281432.
Zhu, K., Kraemer, K. and Xu, S. 2003. Electronic business adoption by European firms: a cross-country
assessment of the facilitators and inhibitors. European Journal of Information Systems, 12, 251268.
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27 Annual SEAANZ Conference 2014
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