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A basic model of electronic
commerce adoption barriers
A study of regional small businesses in
Sweden and Australia
Robert C. MacGregor and Lejla Vrazalic
School of Economics and Information Systems, University of Wollongong,
Wollongong, New South Wales, Australia
Abstract
Purpose – To develop a basic model of e-commerce adoption barriers to small businesses located in
regional areas of developed countries.
Design/methodology/approach – An empirical survey of 477 small businesses in Sweden and
Australia about e-commerce adoption and implementation. The data was analysed using correlation
matrices and factor analysis to derive a model of e-commerce barriers.
Findings – E-commerce adoption barriers to small businesses in regional areas in both Sweden and
Australia can be grouped according to two distinct factors: e-commerce is either “too difficult” or
“unsuitable” for the business. The model derived is based on these factors.
Research limitations/implications – Limitations – inability to derive industry specific
conclusions; standard limitations associated with mailed survey instruments; further qualitative
research is necessary. Research implications – “first of its kind” model of e-commerce adoption
barriers to small businesses located in regional areas; consolidated understanding of e-commerce
adoption barriers.
Practical implications – Significant implications for government organizations engaged in
promoting e-commerce adoption in small businesses. This research indicates where and how adoption
initiatives should be targeted.
Originality/value – This research fills a gap in our knowledge about e-commerce adoption barriers
and overcomes some of the fragmentation associated with e-commerce adoption in small businesses.
Keywords Electronic commerce, Trade barriers, Small enterprises, Australia, Sweden
Paper type Research paper
Journal of Small Business and
Enterprise Development
Vol. 12 No. 4, 2005
pp. 510-527
q Emerald Group Publishing Limited
1462-6004
DOI 10.1108/14626000510628199
Introduction
Small businesses are defined as organisations that employ less than 50 people (Europa,
2003). Yet despite their size, small businesses are seen as significant contributors to the
prosperity of national economies. The European Commission views small businesses
as the backbone of the European economy (Europa, 2003). Similarly, the Australian
government recognises that small businesses are a “powerhouse” of economic
potential, whose employees account for almost five million members of the workforce
(NOIE, 2002) making them a major source of jobs. In recent years, small businesses
have faced a number of challenges. With the establishment of Free Trade Agreements
between countries worldwide, small businesses are increasingly competing in global
markets. This has been made possible by the advent of electronic commerce
(e-commerce) technology.
E-commerce involves the application of web-based information technologies
towards automating business processes, transactions and workflows, and buying and
selling information, products, and services using computer networks (Kalakota and
Whinston, 1997). E-commerce technology has the potential to become a major source of
competitive advantage to small businesses because it is a cost effective way of
reaching customers globally and competing on par with larger counterparts.
Governments worldwide have recognised this and created funding schemes and
initiatives to facilitate e-commerce adoption in small businesses.
Despite government support and the exponential growth of e-commerce, it is mainly
the larger businesses that have reaped the benefits of this technology (Riquelme, 2002).
In contrast, the rate of e-commerce adoption in the small business sector has remained
relatively low (Magnusson, 2001; Poon and Swatman, 1998; Van Akkeren and Cavaye,
1999). This sluggish pace of e-commerce diffusion into small businesses has been
attributed to various barriers or impediments that are faced by these organisations. A
number of different e-commerce adoption barriers have been documented in research
studies (Quayle, 2002; Purao and Campbell, 1998; Lawrence, 1997; Riquelme, 2002; Van
Akkeren and Cavaye, 1999). Some of these include the high costs associated with
e-commerce, lack of technical resources and expertise to implement e-commerce, the
complexity of e-commerce technology and the difficulty of measuring the return on
investment. Along with the research examining barriers to e-commerce adoption, there
have been attempts by many authors to formalise and group these barriers into a
tentative model (Van Akkeren and Cavaye, 1999; Quayle, 2002). While these tentative
barrier groupings may be useful for further research studies, a closer examination
shows that the groupings are, for the most part, “manufactured” by the researchers in
order to develop logical structures for designing survey or case study questions. As a
result, our understanding of e-commerce adoption barriers and what needs to be done
to overcome them appears to be fragmented and incomplete, rendering government
initiatives to promote e-commerce adoption ineffective. In addition to the lack of
empirical models of e-commerce adoption barriers, it remains unclear whether these
barriers are applicable to small businesses located in regional areas.
This paper aims to correct the situation by presenting the findings of a study of
regional small businesses in Sweden and Australia that investigated e-commerce
adoption barriers (amongst other things). The data collected from the study was used
to analyse correlations between various e-commerce adoption barriers (sourced from
the existing literature), with the view to deriving a basic model of these barriers. The
paper begins by examining the nature of small businesses and identifying features that
are unique to the sector to create a context for the study. Small businesses in regional
areas and government initiatives to promote e-commerce technology adoption by small
businesses are then discussed in order to demonstrate the practical significance of the
study. This is followed by a literature review of e-commerce adoption barriers that
were used to develop a survey instrument for the Swedish and Australian study. A
description of the study and the results ensues, before correlation matrices and a factor
analysis are performed on the results. A basic model of e-commerce adoption barriers
is then derived and discussed. Finally, the limitations of the study are presented along
with the conclusions, practical implications and future research directions.
Small businesses
More than 99 per cent of all businesses in Sweden are classified as small to
medium enterprises (SMEs), which means they employ less than 250 people. Of those,
A basic model of
e-commerce
511
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94 per cent are small businesses with less than 10 employees (MIEC, 2003). In Australia,
the Australian Bureau of Statistics reported in 2001 that more than 1.2 million
organisations fell into the small business category. Small businesses are not simply
scaled down versions of large businesses (Wynarczyk et al., 1993). Although size is a
major distinguishing factor, small businesses have a number of other unique features
that set them apart from large businesses. There have been various studies carried out
in order to isolate these features (Bunker and MacGregor, 2000; Dennis, 2000; Tetteh
and Burn, 2001; Miller and Besser, 2000; Hill and Stewart, 2000). An extensive review
of the available literature was undertaken to identify the features and create a context
for the study. Following this process, an analysis of the features identified revealed
that they could be classified as being internal or external to the business. Internal
features include management, decision-making and planning processes within the
organisation, and the availability of resources, while external features are related to the
market (products/services and customers) and the external environment (risk taking
and uncertainty). These are presented in Table I.
Some of the features unique to small businesses are also constraints faced by the
small business. For example, small businesses have fewer resources and expertise
available to them (Blili and Raymond, 1993; Cragg and King, 1993) and have less
control over their external environment (Hill and Stewart, 2000). These constraints
limit the expansion and growth opportunities of small businesses. The relevance of
these types of constraints will become more apparent later when the paper discusses
barriers to e-commerce adoption. However, for the purposes of the current discussion,
the issue is raised because small businesses in regional areas are particularly
susceptible to the types of constraints described above.
Small businesses in regional areas
Small businesses located in regional areas are affected by circumstances inherent to
their location. Regional areas are defined as geographical areas located outside
metropolitan centres and major cities. The Australian Bureau of Statistics (2001)
classifies regional areas into inner and outer regions, remote and very remote areas.
Determining the classification of a region is based on a formula which primarily relies
on the measures of proximity to services in terms of physical distance, and population
size. Rather than remote and rural areas (which are sparsely populated), the research
presented in this paper focuses on inner and outer regional areas (which are more
urbanised).
Regional areas are of particular interest to governments because they are
characterised by high unemployment rates (Larsson et al., 2003), a shortage of skilled
people, limited access to resources and a lack of infrastructure (Keniry et al., 2003). Yet,
at the same time, businesses located in regional areas in Australia contribute 50 per
cent of the national export income (Keniry, 2003). This implies that small businesses
have the potential to play a major role in developing regional areas. This potential has
not gone unnoticed by government organisations. The European Union views small
businesses as a catalyst for regional development (Europa, 2003). In 2001, the Swedish
Parliament passed legislation that resulted in the creation of Regional Development
Councils (Johansson, 2003). The councils have a mandate to promote a positive
business climate and sustainable growth in their respective regions. Small businesses
have been earmarked as playing an important role in promoting growth because they
Features unique to small businesses
Related literature
Internal features
Features related to management, decision-making and planning processes
Small businesses have a centralised management
Bunker and MacGregor (2000)
strategy with a short range planning perspective
Welsh and White (1981)
Small businesses have poor management and business
Blili and Raymond (1993)
skills
Dennis (2000)
Small businesses exhibit a strong desire for
independence and avoid business ventures which
impinge on their independence
Small business owners often withhold information from
Dennis (2000)
colleagues
Bunker and MacGregor (2000)
Decision making processes in small businesses are
intuitive, rather than based on detailed planning and
exhaustive study
Small business owners have a strong influence in the
Bunker and MacGregor (2000)
decision making process
Family values and concerns may intrude with the
Bunker and MacGregor (2000)
decision making processes of small businesses
Dennis (2000)
Small businesses have informal and inadequate
Tetteh and Burn (2001)
planning and record keeping processes
Miller and Besser (2000)
Features related to resource availability
Small businesses face difficulties obtaining finance and
Blili and Raymond (1993)
other resources, and as a result have fewer resources
Cragg and King (1993)
Welsh and White (1981)
Walczuch et al. (2000)
Small businesses are more reluctant to spend on
Dennis (2000)
information technology and therefore have limited use of
MacGregor and Bunker (1996)
technology
Martin and Matlay (2001)
Small businesses have a lack of technical knowledge and
Bunker and MacGregor (2000)
specialist staff and provide little information technology
Blili and Raymond (1993)
training for staff
Cragg and King (1993)
External features
Features related to products/services and markets
Small businesses have a narrow product/service range
Bunker and MacGregor (2000)
Quayle (2002)
Small businesses have a limited share of the market
Hadjimonolis (1999)
(often confined towards a niche market) and therefore
Lawrence (1997)
heavily rely on few customers
Small businesses s are product-oriented, while large
Bunker and MacGregor (2000)
businesses are more customer-oriented
MacGregor et al. (1998)
Small businesses are not interested in large shares of the
MacGregor et al. (1998)
market
Small businesses are unable to compete with their larger
Lawrence (1997)
counterparts
Features related to risk taking and dealing with uncertainty
Hill and Stewart (2000)
Small businesses have lower control over their external
environment than larger businesses, and therefore face
more uncertainty
Small businesses face more risks than large businesses
DeLone (1988)
because their failure rates are higher
Cochran (1981)
Small businesses are more reluctant to take risks
Walczuch et al. (2000)
Dennis (2000)
A basic model of
e-commerce
513
Table I.
Internal and external
features unique to small
businesses
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are seen as a key source of jobs and employment prospects (Keniry et al., 2003; Larsson
et al., 2003). To encourage growth and development in regional areas, government
organisations have been heavily promoting the adoption of information and
communication technology (ICT) by small businesses.
Government ICT initiatives and programs
In Sweden, the Swedish Business Development Agency (NUTEK) runs a national
program known as IT.SME which provides skills training in ICT for small businesses.
The program targets regional small businesses in particular (MIEC, 2003). The agency
also runs a similar program that concentrates on increasing the use of ICT in small
businesses located in regional areas to strengthen their competitiveness on the global
market. Specifically on the e-commerce front, the Swedish Alliance for Electronic
Business has set an objective of having 80 per cent of small businesses starting to use
e-commerce tools by the end of 2004 (MIEC, 2003).
In Australia the initiatives have been just as forthcoming. The Federal Government
recently announced a $6.5 million scheme over two years to accelerate the uptake of
e-commerce in small businesses (NOIE, 2002). Similarly, the Information Technology
Online (ITOL) funding program offers up to $200,000 to support the adoption of
collaborative e-business by small businesses. Yet, despite these programs and
initiatives, the rate of e-commerce adoption in small businesses has been reported as
being low. The reasons for this are diverse, however, they are generally categorised as
barriers or inhibitors to e-commerce adoption. The following section will examine some
of the barriers to e-commerce adoption that are faced by small businesses.
Barriers to e-commerce adoption in small businesses
E-commerce has been widely touted as providing small businesses with an opportunity
for instant access to global markets and customers (Coviello and McAuley, 1999).
However, research shows that it is mostly larger businesses that have benefited from
e-commerce adoption (Riquelme, 2002) with small businesses showing a much slower
pace of adoption. The reasons for this are diverse and have been examined in various
studies as inhibitors or barriers that prevent small businesses from adopting and,
subsequently fully reaping the benefits of e-commerce. A summary of different
e-commerce adoption barriers in small businesses based on an extensive literature
review is presented in Table II.
It is interesting to note that most of the barriers listed in Table II can be directly
attributed to the unique features that are also constraints faced by small businesses
discussed above (Table I). For example, one of the most commonly cited barriers to
e-commerce adoption is that it is too expensive to implement, a barrier that arises from
the fact that small businesses face difficulties obtaining finance, unlike their larger
counterparts. If the finance was readily available to small businesses, high cost may
not be a barrier to e-commerce adoption. The exact nature of the relationship between
the unique features of small businesses and e-commerce adoption barriers is not fully
clear and it is not the intention of this paper to explore it in detail, but simply to draw
attention to the need for further research.
While a number of studies have identified individual e-commerce adoption barriers,
as already noted previously, a model of these barriers and how they relate to each other
is not available. Any attempt to categorise e-commerce adoption barriers into a formal
Barriers to e-commerce adoption
Related literature
High cost of e-commerce implementation; internet Iacovou et al. (1995), Quayle (2002), Purao and
technologies are too expensive to implement
Campbell (1998), Lawrence (1997), Riquelme
(2002) and Van Akkeren and Cavaye (1999)
E-commerce is too complex to implement
Quayle (2002)
Small businesses require short-term ROI and
Lawrence (1997) and McGowan and Madey (1998)
e-commerce is a long-term investment
Organisational resistance to change because of
Lawrence (1997) and Van Akkeren and Cavaye
the fear of new technology amongst employees
(1999)
Lawrence (1997), Venkatesan and Fink (2002) and
Preference for and satisfaction with traditional
Poon and Swatman (1999)
manual methods, such as phone, fax and
face-to-face
Quayle (2002), Lawrence (1997), Riquelme (2002),
Lack of technical skills and IT knowledge
Van Akkeren and Cavaye (1999), Iacovou (1995)
amongst employees; lack of computer
and Chau and Turner (2001)
literate/specialised staff
Lack of time to implement e-commerce
Walczuch et al. (2000), Lawrence (1997) and Van
Akkeren and Cavaye (1999)
E-commerce is not deemed to be suited to the way Hadjimonolis (1999) and Iacovou et al. (1995)
the organisation does business, or the way our
clients do business
E-commerce is not deemed to be suited to the
Walczuch et al. (2000), Kendall and Kendall (2001)
products/services offered by the small business and Hadjimonolis (1999)
E-commerce is perceived as a technology
Lawrence (1997)
lacking direction
Iacovou et al. (1995) and Quayle (2002)
Lack of awareness about business
advantages/opportunities that e-commerce can
provide
Lack of available information about e-commerce Lawrence (1997)
Concern about security of e-commerce
Quayle (2002), Purao and Campbell (1998),
Riquelme (2002), Van Akkeren and Cavaye (1999),
Poon and Swatman (1999) and Hadjimonolis
(1999)
Lack of critical mass among customers, suppliers Hadjimonolis (1999)
and business partners to implement e-commerce
Heavy reliance on external consultants (who are Lawrence (1997), Van Akkeren and Cavaye (1999)
and Chau and Turner (2001 )
often considered by small businesses to be
inadequate) to provide necessary expertise
Lack of e-commerce standards
Tuunainen (1998)
structure is generally undertaken for the purposes of designing the research
methodology. Indeed, Fillis et al. (2003) suggest that much of the research work
surrounding e-commerce and small business is prescriptive both in terms of
conceptualisation as well as dissemination. Stockdale and Standing (2004) suggest that
many of the government led surveys subdivide barriers into four categories: lack of
resources and knowledge; skill levels of employees; security concerns; and readiness of
the small business. Earlier studies (Cragg and King, 1993; Thong et al., 1996) also
grouped barriers into four categories (education; management time; economic
concerns; and technical know-how), while Kurnia and Johnson (2000, cited in Scupola,
2003) add an extra category termed supply chain structure. Hadjimonolis (1999), in a
study of e-commerce adoption by small businesses in Cyprus, classified e-commerce
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e-commerce
515
Table II.
Summary of e-commerce
adoption barriers in small
businesses
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barriers into two generic types: internal and external. External barriers could be
further categorised into supply barriers (difficulties obtaining finance and technical
information), demand barriers (e-commerce not fitting with the products/services or
not fitting with the way clients did business) and environmental barriers (security
concerns). Internal barriers were further subdivided into resource barriers (lack of
management and technical expertise) and system barriers (e-commerce not fitting with
the current business practices).
Although previous research has attempted to categorise e-commerce adoption
barriers in order to make sense of the diverse range of barriers identified in empirical
studies, these attempts have not resulted in a model e-commerce barriers that would
explain how they are related. In order to do this, it is necessary to examine the
correlations between the different e-commerce adoption barriers. Also, while research
examining the inhibitors to e-commerce adoption in small businesses has identified a
large number of barriers, there has not been any research effort to find out whether
these barriers affect regional small businesses. As a result, our current understanding
of e-commerce adoption barriers is fragmented and incomplete. Without having a clear
awareness of the key issues that affect e-commerce adoption in small businesses,
government initiatives such as the ones described above may prove to be ineffective
and poorly targeted. The study described in the following section attempts to extend
our knowledge about e-commerce adoption barriers in small businesses located in
regional areas by deriving a basic model. This model would serve as an explanatory
tool and assist in the development of government initiatives aimed promoting
e-commerce adoption.
Methodology
Ten of the most commonly occurring barriers to e-commerce adoption in Table II were
identified from the literature. A series of six in-depth interviews with regional small
businesses in Australia was undertaken to determine whether the barriers were
applicable and complete. All of the identified barriers were found to applicable and no
additional barriers were forthcoming. Based on the six in-depth interviews, a survey
instrument was developed to collect data about e-commerce adoption barriers
(amongst other things). Respondents who had not adopted e-commerce were asked to
rate the importance of each barrier to their decision not to adopt e-commerce (Figure 1)
using a standard 5 point Likert scale. The Likert scale responses were assumed to
posses the characteristics of an interval measurement scale for data analysis purposes.
The study was primarily concerned with small businesses located in regional areas,
especially since no other research has investigated e-commerce adoption barriers in
these areas specifically. As a result, this study was conceived primarily as exploratory
in nature. Sweden and Australia were chosen to carry out the study for several reasons.
Both countries have a large number of small businesses located in regional areas and
the governments of both countries are keen to promote e-commerce adoption by small
businesses in these areas (as described above). Furthermore, both Sweden and
Australia are classified by the World Bank Group as high income nations and
members of the Organisation for Economic Co-operation and Development (OECD).
Finally, ease of access to small businesses in regional areas of the two countries was a
major contributing factor. To qualify as a regional area, the following criteria was
developed and applied to several areas in Sweden and Australia.
A basic model of
e-commerce
517
Figure 1.
Question about barriers to
e-commerce adoption used
in survey
.
.
.
.
the location must be an urban regional area, and not a major/capital city or rural
area;
a viable government initiated Chamber of Commerce must exist and be well
patronised by the small business community;
the location should have a full range of educational facilities (including a
university); and
the business community must represent a cross-section of business ages, sizes,
sectors and market foci.
As a result, two locations were chosen: Karlstad (Sweden) and Wollongong (Australia).
Both locations met all of the location criteria. A total of 1,170 surveys were distributed
by post to randomly selected small businesses in Sweden, and 250 surveys were
administered by telephone in the Wollongong. The mode of the data collection was
selected based on previous research by de Heer (1999) which indicated that
Scandinavian countries (including Sweden) had historically high survey response rates
(although he notes that this is declining), while Australia had a higher non-response
rate. Therefore, a low cost mail survey was used in Sweden, while the more expensive
mode of phone surveys was used in Australia to ensure higher levels of participation.
Results and findings
Responses were obtained from 313 small businesses in Sweden giving an unexpectedly
low response rate of 26.8 per cent. (It is interesting to note that the low response rate
supports de Heer’s (1999) findings that survey response rates in Sweden are falling.)
Of these, 275 responses were considered to be valid and usable. The total number
non-adopters (i.e. small businesses not using e-commerce) was 123, representing
44.7 per cent of the valid responses. The responses of these non-adopters were
examined in detail and it was determined that 89 of them responded to every statement
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in the question regarding barriers to e-commerce adoption. The responses of these 89
small businesses formed the basis for the statistical analysis carried out using SPSS for
Sweden.
Responses were obtained from 164 small business organisations in Australia giving
a higher response rate of 65.6 per cent which is consistent with phone surveys (Frazer
and Lawley, 2000). In Australia, the total number non-adopters was 139, representing
84.4 per cent of the valid responses. The responses of the non-adopters were examined
in detail and it was determined that all 139 responded to every statement in the
question regarding barriers to e-commerce adoption. Again, the responses formed
the basis for the statistical analysis carried out using SPSS. An inspection of the
frequencies indicated that the full range of the scale was utilised by respondents both
in Sweden and Australia (i.e. every barrier had at least one instance of each rating from
1 to 5). A profile of the survey respondents who had not adopted e-commerce is shown
in Table III. It is interesting to note that a significantly higher portion of Swedish small
businesses had adopted e-commerce – 55 per cent as opposed to 15 per cent in
Australia.
The main aim of the statistical analysis for each location, was to establish the
correlations between e-commerce adoption barriers in the data set. Prior to this, the
scales of measurement for the barriers were tested using a Cronbach a reliability test.
Cronbach’s a was 0.9660 for the Swedish respondents, and 0.9820 for the Australian,
indicating a high level of reliability in both cases. The correlations between the barriers
were then examined for each location individually and the results are shown in the
correlation matrices (Tables IV and V). The barriers have been abbreviated for
readability and correlations which were significant at the 0.001 level are shown in bold
lettering.
The correlation matrices show an interesting pattern of results because of the
similarities between Sweden and Australia. The first four barriers in both the Swedish
and Australian instance all seem to correlate with each other, but show weak or no
correlations with the last set of barriers. Similarly, it appears that correlations exist
between the last five barriers in both the correlation matrices. Therefore, two distinct
groupings of results can be identified from the correlation matrices. In the first
grouping, there is a strong positive correlation between the barriers “E-commerce is
not suited to our products/services” and “E-commerce is not suited to our way of doing
business”. These two barriers also show moderately strong positive correlations with
the barriers “E-commerce is not suited to the ways our clients (customers and/or
suppliers) do business” and “E-commerce does not offer any advantages to our
organisation”. In the second grouping, the barriers relating to the investment, time,
number of options, complexity and lack of expertise about e-commerce adoption
generally show moderately strong positive correlations with each other. However, the
barriers in the second grouping appear to be unrelated to the barriers in the first
grouping, with the exception of very weak correlations for the barrier relating to
security and time in the Swedish case.
These findings suggested the use of factor analysis to investigate any separate
underlying factors and to reduce the redundancy of certain barriers indicated in the
correlation matrices. The results of Kaiser-Meyer-Olkin MSA (0.735 for Sweden and
0.905 for Australia) and Bartlett’s Test of Sphericity (x 2 ¼ 343, p ¼ 0.000 for Sweden
and x 2 ¼ 1395.670, p ¼ 0.000 for Australia) indicated that the data set satisfied the
E-commerce
Sweden
Total respondents
Adopters
152
Non-adopters
123
Missing
38
Size of business (non-adopters only)
Total respondents
Single owner
26
1-9 employees
67
10-19 employees
18
20-49 employees
10
Missing
2
No of years in business (non-adopters only)
Total respondents
Less than 1 year
3
1-2 years
6
3-5 years
15
6-10 years
24
11-20 years
31
More than 20 years
43
Missing
1
Business sector (non-adopters only)
Total respondents
Industrial
20
Service
49
Retail
23
Finance
0
Other
31
Missing
0
Market focus (non-adopters only)
Total respondents
Local
74
Regional
11
National
24
International
14
Missing
0
Australia
Total
25
139
0
177
262
38
A basic model of
e-commerce
519
27
90
9
8
5
53
157
27
18
7
10
15
19
22
34
35
4
13
21
34
46
65
78
5
10
65
57
0
7
0
30
114
80
0
38
0
41
78
16
0
4
115
89
40
14
4
assumptions for factorability. Principle Components Analysis was chosen as the
method of extraction in order to account for maximum variance in the data using a
minimum number of factors. A two-factor solution was extracted with eigenvalues of
3.252 and 2.745 for Sweden and 4.212 and 3.586 for Australia. This was supported by
an inspection of the Scree Plots. These two factors accounted for 59.973 per cent of the
total variance in Sweden and 77.977 per cent in Australia. These results are
summarised in Table VI.
The two resulting components were rotated using the Varimax procedure and a
simple structure was achieved as shown in the rotated component matrix in Table VII.
Five barriers loaded highly on the first component. These barriers were related to the
complexity of implementation techniques, range of e-commerce options, high
investments and the lack of technical knowledge and time. This component has been
termed the “Too Difficult” factor. The barriers highly loaded on the second component
Table III.
Profile of survey
respondents
Table IV.
Correlation matrix of
e-commerce adoption
barriers (Sweden)
0.530
0.547
0.054
0.059
0.303 * *
20.138
20.261 * *
20.005
0.280
2 0.097
0.065
0.098
0.092
2 0.056
2 0.033
Not fit client way
of work
0.249 *
0.106
0.249 *
20.104
20.195 *
0.062
No
advantages
Notes: *Correlation is significant at the 0.05 level; * *correlation is significant at the 0.01 level
0.462
0.482
2 0.030
2 0.009
0.184 *
2 0.051
2 0.245 *
2 0.056
0.746
Not fit our way
of work
0.544
0.277 *
0.445
0.432
0.514
0.516
0.481
0.587
0.579
0.217 *
0.174
0.334
No
expertise Complex Security
520
Not fit our way
of work
Not fit client way
of work
No advantages
No expertise
Complex
Security
Cost too high
No time
Many choices
No match
prod/services
0.448
0.494
Cost
too high
0.532
No
time
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0.435
0.654
0.213 *
0.039
20.047
0.119
0.011
0.035
0.747
0.804
0.647
0.221 * *
0.105
0.027
0.140
0.033
0.075
Not fit our way
of work
0.413
0.206 *
0.155
0.101
0.024
0.106
20.047
Not fit client way
of work
0.255 * *
0.177 * *
0.156
0.201 *
0.142
0.174 *
0.708
0.441
0.525
0.420
0.415
0.554
0.537
0.510
0.484
0.357
0.299
0.366
No
No
advantages expertise Complex Security
Notes: *Correlation is significant at the 0.05 level; * *correlation is significant at the 0.01 level
Not fit our way
of work
Not fit client way
of work
No advantages
No expertise
Complex
Security
Cost too high
No time
Many choices
No match
prod/services
No
time
0.603
Cost
too high
0.556
0.407
A basic model of
e-commerce
521
Table V.
Correlation matrix of
e-commerce adoption
barriers (Australia)
JSBED
12,4
522
are termed the “Unsuitable” factor and are related to the suitability of e-commerce to the
respondent’s business, including the extent to which e-commerce matched the
businesse’s products/services, the organisation’s way of doing business, their client’s
way of doing business and the lack of advantages offered by e-commerce
implementation. These two factors are independent and uncorrelated, as an
orthogonal rotation procedure was used. It is interesting to note that the barrier
relating to security loaded on both factors in Sweden, although the loading on the
“Too Difficult” factor was slightly higher (0.525). In Australia, the same barrier loaded
on the “Too Difficult” factor only (0.767).
Discussion
The results of the Swedish and Australian study presented above show that the
e-commerce barriers identified in the literature apply equally to small businesses in
regional areas. More importantly, the results are an important first step in consolidating
our understanding of barriers that affect e-commerce adoption by small businesses in
general because it appears that correlations between the barriers exist. The correlations
indicate that barriers can be grouped according to two distinct factors. These factors have
been termed “Too Difficult” and “Unsuitable”. The “Too Difficult” factor is related to the
barriers which make e-commerce complicated to implement, including barriers such as
the complexity of e-commerce implementation techniques, the difficulty in deciding which
Component
Table VI.
Total variance explained
(Sweden and Australia)
1
2
Eigenvalue
Sweden
Australia
3.252
2.745
4.212
3.586
Rotation sums of squared loadings
Percentage of variance
Cumulative per cent
Sweden
Australia
Sweden
Australia
32.520
27.453
42.116
35.860
32.520
59.973
Component 1:
too difficult
Sweden Australia
Table VII.
Rotated component
matrix (Sweden and
Australia)
E-commerce is not suited to our products/services
2 0.086
E-commerce is not suited to our way of doing business 2 0.034
E-commerce is not suited to the ways our clients
(customers and/or suppliers) do business
2 0.004
E-commerce does not offer any advantages to our
organisation
0.076
We do not have the technical knowledge in the
organisation to implement e-commerce
0.743
E-commerce is too complicated to implement
0.852
E-commerce is not secure
0.525
The financial investment required to implement
e-commerce is too high for us
0.703
We do not have time to implement e-commerce
0.742
It is difficult to choose the most suitable e-commerce
standard with so many different options available
0.800
42.116
77.977
Component 2:
unsuitable
Sweden Australia
0.209
0.271
0.844
0.909
0.917
0.912
0.262
0.643
0.909
0.355
0.731
0.837
0.787
0.869
0.767
0.074
0.102
0.385
0.349
0.237
0.216
0.795
0.813
2 0.092
2 0.294
0.272
0.205
0.802
2 0.054
0.217
standard to implement because of the large range of e-commerce options, the difficulty of
obtaining funds to implement e-commerce, the lack of technical knowledge and the
difficulty of finding time to implement e-commerce. The “Unsuitable” factor is related to
the perceived unsuitability of e-commerce to small businesses. The barriers in this group
include the unsuitability of e-commerce to the organisation’s products/services, its way of
doing business, and its client’s way of doing business, as well as the lack of perceived
advantages of e-commerce implementation. However, while the data on e-commerce
adoption barriers in both Sweden and Australia generally agreed where correlations
between the barriers were concerned, there appears to be a slight disagreement on the
barrier of security issues associated with e-commerce. In the case of Sweden, this barrier
was found to be related to both factors, although more so towards the “Too Difficult”
factor. In contrast, the security barrier in Australia related to the “Too Difficult” factor
only. Based on these results, it is possible to derive a basic model of e-commerce adoption
barriers in small businesses. This model is shown in Figure 2.
The results of this study are significant in several ways to government organisations
promoting e-commerce adoption in small businesses. The basic model derived from the
results reduces the fragmentation associated with the having a large number
of e-commerce adoption barriers. It also provides a more concise understanding of
e-commerce adoption barriers faced by small businesses because ten of the most common
barriers to e-commerce adoption are grouped in relation to two main factors. This is a
powerful explanatory tool because instead of accounting for ten different barriers, the
inhibitors to e-commerce adoption can be explained as a result of one of two things:
e-commerce is either too difficult to adopt or it is unsuitable to the business. This indicates
that small businesses fall into two categories in relation to e-commerce: potential adopters
and non-adopters. The non-adopters do not perceive e-commerce as being suited to their
organisation at all. This may include small businesses such as a corner shop selling basic
groceries. Government initiatives should therefore be targeted more towards potential
adopters and offer them support in two key areas: technical expertise and financial
assistance. Both of these areas are significant barriers associated with the “Too Difficult”
factor.
A basic model of
e-commerce
523
Figure 2.
Basic model of
e-commerce adoption
barriers in small
businesses
JSBED
12,4
The model shown in Figure 2 differs from previous attempts to group e-commerce
barriers in that it is derived based on empirical data gathered from regional small
businesses in Sweden and Australia and applies to both. This would appear to suggest
that the model is generalised to regional small businesses with similar characteristics.
However, further research is clearly required to determine whether this is the case.
524
Limitations
It should be noted that the study presented here has several limitations. The data for
the study was collected from various industry sectors so it is not possible to make
sector specific conclusions. Also, the choice of variables selected for the study is
somewhat problematic because of the complex nature of adoption barriers which
change over time. Furthermore, according to Sohal and Ng (1998), the views expressed
in the surveys are of a single individual from the responding organisation, and only
those interested in the study are likely to complete and return the survey. Finally, this
is a quantitative study, and further qualitative research is required to gain a better
understanding of the key issues.
Conclusion
The aim of this paper was twofold: to determine whether e-commerce adoption barriers
were applicable to small businesses located in regional areas, and to develop a model of
e-commerce adoption barriers by identifying correlations between them. A study of small
businesses in regional areas of Sweden and Australia was undertaken towards these aims.
The data collected from the study was analysed and correlation matrices were produced
for both locations. The correlation matrices indicated two distinct sets of groupings of
barriers and a two-factor solution was extracted using factor analysis. It was found that
e-commerce barriers could be generally grouped depending on whether e-commerce was
deemed to be “Too Difficult” to implement or “Unsuitable” for the organisation.
The study concluded that barriers identified in the literature were applicable to
small business regional areas, and that it was possible to derive a basic model of
e-commerce adoption barriers based on the correlations between barriers. This model
is shown in Figure 2. Unlike previous research which relied on what appear to be
arbitrarily derived groupings of e-commerce barriers, the groupings shown in Figure 2
have been derived from the empirical data. These groupings are by no means
exhaustive and further research is required to determine whether they are generalised
to all small businesses.
The implications of the study are significant for government organisations engaged
in promoting e-commerce adoption, especially in regional small businesses. Whereas, in
the past government organisations have been spent millions of dollars to facilitate
e-commerce technology use in small businesses, this research indicates that the funding
should be more explicitly targeted towards potential adopters in the form of technical
expertise and financial assistance to purchase the required components. Additional
research is required to establish how potential e-commerce adopters can be identified.
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