Information technology adoption in small business confirmation of a proposed framework2013

Journal of Small Business Management 2013 ••(••), pp. ••–••
doi: 10.1111/jsbm.12058

Information Technology Adoption in Small Business:
Confirmation of a Proposed Framework
by ThuyUyen H. Nguyen, Michael Newby, and Michael J. Macaulay

This paper investigates which drivers affect information technology (IT) adoption and which
factors relate to a successful IT implementation in small businesses, where the adoption rate is
traditionally low and the failure rate is high. The findings from this study suggest that customers
are the main driving force of IT adoption. When it comes to IT implementation, our results suggest
that managers/owner–managers must engage with five factors: organization, internal IT
resources, external IT consultants, supplier relations, and customer relations. These findings give
further insight into IT adoption in small businesses and highlight the importance of customer
relations in the adoption process.

Introduction
Information technology (IT) adoption is
the stage at which a decision is made about
adopting particular hardware and/or software technology (Thong 1999) and involves
various activities, including managerial and
professional/technical staff decision-making in
both the internal and external environment of
the organization, which must occur before the
given technology can have a physical presence
in the organization (Grover and Goslar 1993;
Preece 1995). There have been a number of
research studies on the determinants of IT adoption in small businesses such as those by
Bharadwaj and Soni (2007), Fuller (1996), Irvine
and Anderson (2008), Lee and Runge (2001),
Riemenschneider, Harrison, and Mykytyn

(2003), and Thong (1999), all of which focus on
searching for factors that affect the decision and
intention to adopt IT. These factors include cost

benefits, management innovativeness, perception, knowledge and skills, employee attitudes,
acceptances and contributions (the Theory of
Planned Behavior and the Technology Acceptance Model), IT skills and knowledge of management and employees, and IT infrastructure.
The decision to adopt is also influenced by
external factors such as consultants, business
partners, suppliers, and customers.
However, it is not always clear whether
small businesses see new IT as an opportunity
or a threat. Evidence suggests that IT adoption
rates in small business are low, and that failure
rates are high: the question is why. Some commentators have suggested that using IT is not
always going to be beneficial to such firms
(Bull 2003; Oakey and Cooper 1991), while
others have argued that IT is not appropriate
for every small firm (Macpherson et al. 2003;
Morgan, Colebourne, and Thomas 2006). Levy,
Powell, and Yetton (2001) suggest that IT

ThuyUyen H. Nguyen is Senior Lecturer in Business Analysis, Systems, and Supply Change Management
at Northumbria University, UK.
Michael Newby is Professor of Information Systems and Decision Sciences at California State University,
Fullerton.
Michael J. Macaulay is Associate Professor of Public Management at School of Government, Victoria
University, New Zealand.
Address correspondence to: ThuyUyen H. Nguyen, Newcastle Business School, Northumbria University,
Newcastle upon Tyne NE1 8ST, UK. E-mail:

NGUYEN, NEWBY, AND MACAULAY

1


adoption in small businesses often happens
without any proper planning, resulting in a low
percentage of successful implementations.
According to Carson and Gilmore (2000), small
businesses, especially new ones, often experience ambiguity and uncertainty regarding IT
adoption. Bhagwat and Sharma (2007) point
out that many difficulties are due to the lack of
resources (financial, technical, and managerial)
available to small businesses.
This paper extends these debates and suggests that there is no one single factor that
accounts for the low adoption rate or the high
failure rate of IT adoption in small businesses.
Indeed, this paper will demonstrate, through
an empirical study of the IT adoption process in
small businesses, that there are five interconnected factors that influence the success or
failure of IT adoption: organization, internal IT
resources, external IT consultants; supplier
relations, and customer relations. In so doing,
this paper offers a twofold approach: first, it
investigates drivers to or reasons for IT adoption in small businesses; second, it determines
factors relating to a successful implementation
in the specific context of three industries (retail,
financial services, and manufacturing) in Los
Angeles County and Orange County in Southern California.

The remainder of this paper is structured as
follows: the next section presents a review of
key aspects of the cognate literature in this area
and an outline of the components of the study
research framework. This is followed by the
research methodology, the results analysis, and
a discussion of the findings and implications.
Limitations of the study are also discussed with
some suggestions for future research.

Background and
Theoretical Framework
IT Adoption in Small Businesses
By changing the way staff capture and distribute information (Claessen 2005; Currie
2004), IT provides organizations with a number
of benefits—sustainable competitive advantage
(Bruque and Moyano 2007; Carbonara 2005;
Hung and Tang 2008; Lee and Runge 2001),
lower production and labor costs, added value
to products and services (Corso et al. 2003;
Nguyen, Sherif, and Newby 2007; Premkumar
2003)—while generally improving business processes (Búrca, Fynes, and Marshall 2005; Levy,
Powell, and Yetton 2001). Despite these potential benefits, there have been numerous cases of

2

unsuccessful IT implementations in this sector
(Acar et al. 2005; Mole et al. 2004; RuizMercader, Meroño-Cerdan, and Sabater-Sánchez
2006), and the adoption rate can be very slow
(Peltier, Schibrowsky, and Zhao 2009; Thong

1999). A survey conducted by the research and
advisory firm Gartner, for example, found that
more than half of the organizations that had
implemented IT encountered difficulties after
implementation (Baumeister 2002).
The key to this lack of success appears to be
a disconnection between vision and execution:
organizations do not do enough research and
planning before implementing the new technology, often because management is unclear
about how and why their firms are adopting
IT in the first place (Bull 2003; MazurencuMarinescu, Mihaescu, and Niculescu-Aron
2007). Added to this are other barriers to adoption. Some firms do not have the capabilities to
expand their IT resources (Acar et al. 2005;
Bharadwaj and Soni 2007; Claessen 2005) as
they lack business and IT strategies. Others
have only limited access to capital resources
and also have limited IT/Information Systems
skills (Ballantine, Levy, and Powell 1998;
Bruque and Moyano 2007). There are, inevitably, financial barriers (Lema and Duréndez
2007; Shin 2006). In addition, project execution
often failed or suffered from a lack of senior
management support, poor project management, or insufficient skills to complete the
project (Bull 2003; Näslund and Newby 2005).
At the same time, there is a significant influence
from major customers (Bhagwat and Sharma
2007) who are becoming more demanding and
expect rising standards of IT excellence. If customer influence goes unrecognized, and organizations rush into implementing IT, they will
experience problems (Mazurencu-Marinescu,
Mihaescu, and Niculescu-Aron 2007).
The present paper investigates the tendency

to adopt IT in small businesses using the
Nguyen (2009) IT adoption framework
(Figure 1). Here, it is suggested that small firms
adopt IT for reasons that come from either the
internal or external pressures or forces. These
reasons are known as drivers to adoption as
they are ultimately the cause of adoption of IT
in a business. In addition, the framework integrates four main aspects of small business
when it comes to IT adoption, and these are (1)
organizational, which includes management,
staff, culture, and knowledge; (2) network
orientation (or networking, as illustrated in

JOURNAL OF SMALL BUSINESS MANAGEMENT


Figure 1
Conceptualized Framework for Small and Medium-Sized Enterprises
(SME) Information Technology Adoption
- Management
- People & culture
- Absorptive
capacity of the firm

Organizational

Networking

- Network
relationship

- Knowledge and
learning

Market-pull/
Innovative

Life cycle/
Maturity
Information
technology
adoption

External
force

Internal
force

Technologypush/
Competitive

- Experience
- Recommendations

Growth
stages

External
expertise

Information
technology
resources

- Abilities
- Capacities
- Capabilities

Factor(s)
Driver(s)

Source: Adapted from Nguyen, T. H. (2009). “Information Technology Adoption in SMEs:
An Integrated Framework,” International Journal of Entrepreneurial Behaviour and
Research 15(2), 164.

Figure 1) that includes the relationship to the
suppliers, business partners, and customers; (3)
external IT consultants; and (4) internal IT
resources, which include the IT abilities,
capacities, and capabilities of the firm. These
aspects will be referred to as factors, as they are
predicated to affect the success of IT adoption
and will be explored and expanded upon afterward. In the context of this study, IT to be
adopted can range from the Microsoft Office
Suite (Microsoft, Redmond, WA, USA) to an
enterprise resources planning system or point
of sales (POS) system and is used to manage

resources and communications in daily business operations.

Drivers to Adoption
The report by the National Federation of
Independent Business (2005) on the state of
technology in small business indicates that the
most common reason for technology to be
upgraded in this sector is simply the desire to
upgrade it, but it is not clear what drives this.
Studies suggest that for many firms, the most
common objectives for IT adoption are to
enhance organizational survival and/or growth

NGUYEN, NEWBY, AND MACAULAY

3


and to remain competitive and/or enhance innovative capacity (Bridge and Peel 1999; Bruque
and Moyano 2007; Búrca, Fynes, and Marshall
2005). These can be the result of pressure from
both the internal and external environment
(Andries and Debackere 2006; Morel and
Ramanujam 1999; Winter et al. 2003), from
either an emphasis on improving efficiency and
business expansion or a pressure to meet certain
requirements from customers and industry standards (Ballantine, Levy, and Powell 1998; Corso
et al. 2003). Rogers (2003) refers to these drivers
as part of an innovation decision process, where
management and organizations assess the
advantage and disadvantage of the adoption.
This is an important aspect of small and

medium-sized enterprises (SMEs), especially in
small businesses, where it has been noted that
insufficient finance is one of the sector’s weaknesses when it comes to investment (Eden,
Levitas, and Martinez 1997; Lema and Duréndez
2007). Most small businesses do not have
sufficient financial resources and often, they
mortgage their own personal possessions as
collateral (Fuller-Love 2006). As a result, these
organizations search for positive potential benefits from any investment. They have to see or at
least believe that new IT will bring advantages to
their firms (Eden, Levitas, and Martinez 1997;
Riemenschneider, Harrison, and Mykytyn 2003).
Hence, drivers to adoption can be viewed not
only as reasons for, but also as catalysts, triggers,
or prerequisites for IT adoption in small businesses (Nguyen 2009). The decision to adopt IT
is the result of these drivers. However, it is not
part of the adoption process. The next section
details our research model and study hypotheses on the IT adoption process.

Research Model and
Study Hypotheses
IT Success Implementation
The dependent variable measured here is
the IT success implementation. As suggested
by Bruque and Moyano (2007), success can be
measured in terms of rapid and effective use
of the new technology, where the objective of
the adoption is to reach a desired outcome.
The objective of a successful implementation
can range from the return on investment

(ROI), increase in revenue, increase in sales,
or improvement in quality of products and
services (Anderson and Huang 2006; Payne
and Frow 2005; Raymond 2005; Roberts, Liu,

4

and Hazard 2005). Thong (1999) suggests that
success in implementation is directly influenced by organizational factors, particularly
the top management, and by IS external
expertise. Levy, Loebbecke, and Powell (2003)
suggest that SMEs benefit from their external
environment when it comes to knowledge
generated for the firms, whereas Caldeira and
Ward (2002) contend that the internal IT
resources contribute to the success of the
implementation.
In this study, the measure for the dependent
variable is on the five-point Likert scale
(strongly agree to strongly disagree). This
measure indicates the degree to which the
respondents rate their IT adoption to be successful. Five items were used to measure the IT
success implementation scale. The first and
second items assess the ROI and increase in
revenue, the third item concerns the increase in
sales and services volumes, and the fourth and
fifth items relate to the improvement in quality
of products and services.
The dependent variable was hypothesized to
be dependent on four factors: organizational,

network orientation, external IT consultants,
and internal IT resources. These four factors
construct an adoption environment, which
measures the overall preparedness (in terms of
attitude, resources, requirements, abilities,
capacities, and capabilities) of the business to
adopt new IT. The factors of the environment
are interrelated, and it is hypothesized that all
contribute to the success (or otherwise) of the
implementation.
Figure 2 summarizes the stages of the adoption process. The drivers to adoption lead to a
decision to adopt IT. This decision affects the
adoption environment within the business, and
the environment, in turn, affects whether the
implementation is successful or not, so the
success of the implementation is viewed as an
outcome of the adoption environment. Figure 3
gives details of our primary research model.
The methodology used here follows that of
Baker and Sinkula (2009), which involves
developing a survey instrument, then measuring and confirming the proposed research
model (see Figure 3).

The Relationship between Organizational
Factor and Successful Implementation
Previous studies have identified a number of
organizational factors that influence the IT
adoption process, including the size of the firm,

JOURNAL OF SMALL BUSINESS MANAGEMENT

Figure 2
Information Technology (IT)
Adoption Stages
Drivers to Adoption
-Internal forces
- External forces

IT Adoption

Adoption Environment
- Factors affecting
successful implementation

IT
Success
Implementation

its goals, the knowledge, skills and experience
of staff, and the organizational culture and
structure. It is suggested that a culture that is
flexible to change is more innovative than one
that is resistant to change (Denison, Lief, and
Ward 2004). Hence, in a flexible culture, the
adoption of IT is more likely to happen and is
more likely to succeed (Minguzzi and Passaro
2001; Ruiz-Mercader, Meroño-Cerdan, and
Sabater-Sánchez 2006). Organizational culture
in small business is seen as being strongly

influenced by the owner–manager’s attitude,
personality, and values (Dibrell, Davis, and
Craig 2008; Gudmundson, Tower, and Hartman
2003; Riemenschneider and McKinney 2001/
2002). In small organizations, management or
owner–managers make most, if not all, of the
key decisions (Fuller-Love 2006; Stanworth and
Gray 1992), and these decisions are based on
their existing knowledge, personal judgment,
and communication skills (Carson and Gilmore

2000). It is not only their decisions that affect
the adoption of IT, but also their commitment
to the adoption process as well (Näslund and
Newby 2005). At the same time, the employees’
knowledge, and degree and form of involvement contribute to the success of the IT adoption (Anderson and Huang 2006; Igbaria et al.
1997; Kotey and Folker 2007). In addition,
employees should understand the purpose
behind the adoption, their role within the
adoption, and their contribution to it. Hence,
communication between the management and
employees regarding the change is essential.
Failure to communication can lead to doubt in
employees about the usefulness of the new
technology, resulting in a negative attitude
towards the change, fear about job security,
and a low level of support. Finally, small businesses are viewed as knowledge generators
and knowledge dispersion enterprises (Dew,
Velamuri, and Venkataraman 2004; Levy,
Loebbecke, and Powell 2003). Their ability to

absorb existing knowledge, transform it, use it,
and generate new knowledge affects the IT
adoption process (Gray 2006; Macpherson and
Holt 2007; Zahra, Neubaum, and Larrañeta
2007). Management should ensure that there is
efficient knowledge sharing among individuals
within the firm, as the IT adoption process
requires teamwork and acceptance across all
functions within a firm (Phelps, Adams, and
Bessant 2007; Smith 2007). Moreover, technological learning and IT can promote entrepreneurial development and growth (Carayannis
et al. 2006). The discussion earlier leads us to
the following hypothesis:
H1: The organizational factor is directly
and positively related to a successful
implementation.

The Relationship between Network
Orientation Factor and Successful
Implementation
A core characteristic of small businesses is
their relationship networks (Fletcher 2002;
Lema and Duréndez 2007). These networks
emerge through the numerous interactions,
which take place between firms, business
partners, vendors, suppliers, and customers.
They can be personal networks (Lema and
Duréndez 2007) or business networks (and on
occasions, it can be difficult, if not impossible,
to differentiate between the two), and they are
not restricted by organizational boundaries

NGUYEN, NEWBY, AND MACAULAY

5


Figure 3
Information Technology (IT) Adoption Research Model
Drivers to Adoption
-Internal forces
- External forces

IT Adoption

Adoption Environment
Organizational
- Management
- People and culture
- Knowledge management
H1

Networking Orientation
- Network relationship
- Collaboration
- Knowledge management

External IT Consultant

H2

IT
Success
Implementation

H3

- Experience
- Recommendations
H4

Internal IT Resources
- Abilities
- Capacities
- Capabilities

(Taylor and Pandza 2003). Through these networks, firms exchange, collaborate, and share
knowledge, information, and communication
(Pittaway et al. 2004; Taylor and Pandza 2003).
Collaboration with customers or suppliers can
facilitate the development and improvement of
products and/or services (Levy, Loebbecke,
and Powell 2003; Rosenfeld 1996). According
to Rosenfeld (1996), this is where knowledge
is created, transferred, and transformed.
Collaboration with these external networks
brings learning opportunities (Rothwell 1991),
knowledge creation (Dew, Velamuri, and

6

Venkataraman 2004), and competitive advantage (Taylor and Pandza 2003). Because they
often lack IT resources and skills (Carbonara
2005; Chan and Chung 2002), small businesses
can benefit from network membership when it
comes to IT adoption (Au and Enderwick
2000), as networking can provide SMEs with
necessary resources (Fletcher 2002). Consequently, our second hypothesis is
H2: Network orientation is directly and
positively
related
to
a
successful
implementation.

JOURNAL OF SMALL BUSINESS MANAGEMENT


The Relationship between External IT
Consultants and Successful
Implementation
Because small businesses generally lack IT
expertise and skills (Izushi 2005), firms often
seek professional consultants when it comes
to IT adoption (Fuller 1996; Shin 2006). It has
been suggested that advice from professional
consultants or IT vendors can be useful for
small business management or owner–
managers, especially when they do not have
sufficient experience or understanding of IT

themselves (Hjalmarsson and Johansson
2003). Research by Thong, Yap, and Raman
(1996) suggests that external IT expertise
plays an important role in the IT implementation process. Turban, Aronson, and Liang
(2005) claim that consulting firms have
acquired and absorbed knowledge from assisting their clients, and therefore can offer this
knowledge to firms that seek their help.
Although IT expertise has been perceived to
have benefit for small business when it comes
to IT adoption, not all small businesses utilize
these resources as the knowledge comes at a
cost, and some firms are not in a financial
position to accommodate such expenses (Bull
2003; Izushi 2005). Therefore, we propose the
following hypothesis:
H3: External IT consultants are directly
and positively related to a successful
implementation.

The Relationship between Internal IT
Resources and Successful
Implementation
The IT resources factor focuses on the IT
abilities, capabilities, and capacities of a firm.
The former refers to the skills, the second to
the resources and strategies, and the latter the
ability of firms to absorb, process, and present
the information the firm holds (Carbonara
2005; Guan et al. 2006; Premkumar 2003).
According to Caldeira and Ward (2003), organizational competencies; organizational and

technical processes; technical, managerial, and
business skills; and the allocation of resources
within firms are the key ingredients for understanding IT adoption in the small enterprise
sector. Other studies suggest that IT managers
should not only understand the reasons why IT
needs to be implemented in their businesses,
but also the importance of taking into account
the needs of their suppliers and customers

(Guan et al. 2006; Mata, Fuerst, and Barney
1995). As mentioned earlier, IT can assist firms
in enhancing their business practices, so a clear
purpose for pursuing new IT should be identified before any key decision on IT adoption is
made. Guan and Ma (2003) argue that the IT
innovation capability of a firm cannot be measured by a single dimension alone, as it is
comprised of technology infrastructure, production, process, knowledge, experiences, and
organization. It involves an articulation
between internal experience and experimental
acquisition and includes a wide variety of
assets and resources. Hence, the IT abilities,
capabilities, and capacities of the organization
play a key role in the IT adoption process
(Búrca, Fynes, and Marshall 2005), and we
hypothesize that
H4: Internal IT resources are directly and
positively
related
to
a
successful

implementation.

Research Methodology
Sample and Data Collection
The sample was taken from owners and
managers of small businesses that are dealing
or participating in any IT adoption process in
the retail, financial services, and manufacturing
sectors in Southern California. With the help of
an employment agency, 437 employers were
contacted, and 284 agreed to participate in the
survey. The survey questionnaires were mailed,
and there were 117 responses. Five more completed questionnaires were received after
follow-up telephone calls, which gave a
response rate of 43 percent. Of the 122
responses, 17 were excluded because there
were too much incomplete data. This resulted
in 105 usable sets of data, which give an overall
response rate of 37 percent. This sample size is
not unusual for this type of study or for the
method used. It is similar in size to those used
by Baker and Sinkula (2009), Brouthers and
Nakos (2005), and Werbel and Danes (2010).
Of the firms that responded to the survey,
the industry breakdown is as follows: 36.6
percent were from retail, 45.8 percent from
financial services, and 20.5 percent in manufacturing. In terms of size, 19.6 percent have 10
employees or fewer, 30.8 percent between 11
and 25 employees, 38.3 percent between 26
and 50, and 11.2 percent more than 50 employees. Of the respondents, 58 percent were male

and 42 percent female. The age distribution

NGUYEN, NEWBY, AND MACAULAY

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was 9.8 percent under the age of 25, 40.0
percent between 25 and 34, 38.1 percent
between 35 and 44, and 12.4 percent over 45
years of age. All respondents had more than
three years experience. The data were tested
for potential effects associated with the specific
industry sector (retail, financial services, and
manufacturing). The results suggest that there
are no significant differences in the responses
due to industry sector.

Research Instrument and
Measuring Scale
The survey questionnaire was developed
and structured on four scales that correspond
to the factors of the IT adoption environment
(see Figure 3). These scales are organizational,
network orientation, external IT consultants,
and internal IT resources. Although this is the
first time this particular model has been tested,
scales and items from existing instruments
were used as much as possible. Organizational
and external IT consultant scales were taken

from the IS effectiveness instrument of Thong,
Yap, and Raman (1996). This instrument was
derived from Kirton (1976)’s Adaption–
Innovation Inventory. An additional two items
in these two scales were taken from Özgener
and I˙raz (2006) and Payton and Zahay (2005).
The network orientation scale measures the
orientations of the organization and its suppliers and customers. It was adapted from the
REMARKOR (Clarkson 1998). This instrument
is an extension of the MARKOR instrument
by Kohli, Jaworski, and Kumar (1993), which
measures the relationship orientation. The
REMARKOR instrument has seven scales. These
scales have between two and 17 items per scale
with a total of 44 items. Only items that are
relevant to the context of this study were used.
The internal IT resource scale was derived from
Caldeira and Ward (2002) and Özgener and I˙raz
(2006).
All items are on a five-point Likert scale
(strongly agree, agree, neutral, disagree, or
strongly disagree). Table 1 gives descriptive
information for each constructed scale. As the
number of items in each scale was different, the
mean score of each scale was calculated for
each individual response, so that for each scale,
the respondent had a score between 1 and 5.
Questions for possible reasons/drivers to IT
adoption for small businesses were derived
from Caldeira and Ward (2002), Payton and

Zahay (2005). They include customer require-

8

ment, business expansion, quality improvement, industry requirement, investment, and
cost control. These questions are not part of the
instrument because the drivers to adoption are
separate from the adoption environment (see
Figure 3). Questions on demographic information were also included.

Results
Instrument Validation
Exploratory factor analysis using principal
component analysis with varimax rotation was
performed on the 105 cases to extract the
factors that were hypothesized. According to a
number of authors, a sample size of 105 is more
than enough for four scales (Hair et al. 2005;
Kline 1994; Lawley and Maxwell 1971). The
Kaiser–Meyer–Olkin sampling adequacy measurement (Kaiser 1958, 1974) was 0.823. This is
classed as meritorious (Norusis 1990) and indicates that the matrix is factorable, and so, the
assumptions for carrying out factor analysis
were met. Using eigenvalues greater than 1.5 as
the criterion, five factors were extracted. Three
of the factors were as postulated: these were
internal IT resources, organizational, and external IT consultants; the other two both came
from network orientation. After examining the
items in the extracted components, it was
observed that most items in internal IT
resources, organizational, and external IT consultants load onto their a priori scale with the

exception of two, “management involvement”
and “management commitment.” These two
items were originally hypothesized to be part of
the organizational factor but load onto the
internal IT resources factor (see Table 2). Four
items originally hypothesized under the
network orientation factor were extracted
together composing a new factor (see Table 3).
Examining this new factor, all items were seen
to be related to customers and the authors
named it customer relations. The remaining
items within the original network orientation
factor were all related to suppliers, and so, it
was renamed as supplier relations. Table 2
gives a summary of results of factor loadings,
and Table 3 gives details of the new extracted
component.
The findings indicate that there are five
factors that contribute to the IT adoption environment in small businesses, and these five
factors are hypothesized to be directly and
positively related to a successful implementation outcome. The extracted five factors explain

JOURNAL OF SMALL BUSINESS MANAGEMENT


Table 1
Descriptive Information of the Developed Instrument
Scale

No. of

Items

Measure

Organizational

8

Network
Orientation

9

External IT
Consultants

6

Internal IT
Resources

10

Extent to which knowledge and
information are exchanged within
and throughout the organization.
Management and staff training,
development, and contribution
Extent to which the relationships to
the suppliers, business partners,

and customers are developed
from trust, shared benefits, and
investment
Extent to which external expertise
and software vendors are used
and encouraged in terms of ease
of access and usefulness to the
organization
Extent to which the IT group is
knowledgeable with respect to
the technical application and
business functions within the
organization, as well as the IT
investment and acquisition

Outcome
IT Success
Implementation

5

Reference
Özgener and I˙raz (2006);
Payton and Zahay (2005);
Thong, Yap, and Raman
(1996) derived from Kirton
(1976)
Clarkson (1998) derived from
Kohli, Jaworski, and Kumar
(1993)

Thong, Yap, and Raman (1996)

Caldeira and Ward (2002)
Özgener and I˙raz (2006)

Extent to which the IT application Caldeira and Ward (2002);
acquired is successfully
Payton and Zahay (2005)
implemented in terms of
satisfying the requirements of the
stakeholders

IT, information technology.
54.75 percent of the variance, which, according
to Kline (1994), is satisfactory for social sciences studies as it is 60 percent or less. Table 4
gives details of the new measurements, and
Figure 4 reflects the revised research model.
As the items from this instrument were
derived from previous instruments, it was necessary to test and evaluate the reliability of the
scales and examine the proposed factors. The
reliability of each factor was evaluated by
assessing the internal consistency of the items
within each factor using Cronbach’s alpha. The
results show the reliability values (see Table 5)
range between 0.70 and 0.87, which indicate
their internal consistency is reliable within each
scale (Cronbach 1951; Nunnally 1978). The test
for common method variance was conducted

on the five extracted factors using Pearson correlation matrix. The results indicated that multicollinearity did not seem to be present in the

sample, as all correlation coefficient values are
less than 0.7 (Hair et al. 2005).

Model Validation and Hypothesis Tests
Figure 4 illustrates a revised conceptual
model based on the factor analysis results (see
Table 2). Structural equation modeling was
employed to test the hypotheses, and Table 6
reports its results. The goodness of fit indices for
the revised model (model 2) are robust. The
chi-square value is 6.16 with a significance of
p = .162. The chi-square degrees of freedom
ratio value of less than 2 (χ2/df = 1.23) is considered to show a very good fit (Marcoulides and

NGUYEN, NEWBY, AND MACAULAY

9


Table 2
Factor Loadings of Rotated Component Matrixa
Item
Ext_ITC01
Ext_ITC02
Ext_ITC03
Ext_ITC04
Ext_ITC05
Ext_ITC06
Int_ ITR01
Int_ ITR02

Int_ ITR03
Int_ ITR04
Int_ ITR05
Int_ ITR06
Int_ ITR07
Int_ ITR08
Int_ ITR09
Int_ ITR10
NR02
NR06
NR07
NR08
NR09
OR01
OR02
OR03
OR04
OR05
OR06
OR07
OR08
NR01
NR03
NR04
NR05
VEb
Eigen

Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

0.742
0.770
0.648
0.811
0.673
0.760
0.634
0.679
0.634
0.653
0.696
0.794
0.662
0.746
0.809
0.724
0.671
0.769
0.727
0.678
0.684
0.725

0.649
0.806
0.724
0.715
0.802
0.763
0.682

27.42
9.05

9.95
3.28

8.02
2.65

5.21
1.72

0.850
0.778
0.786
0.621
4.68
1.55

Extraction method: principal component analysis.
Rotation method: varimax with Kaiser normalization.
a

Rotation converged in eight iterations.
b
Variable explained in percentage.

Hershberger 1997). This is supported by other
strong fit indices (comparative fit index = 0.984,
Tucker Lewis Index = 0.935, normal fit index =
0.972, root mean square error of approximation
[RMSEA] = 0.047), signifying a good-fitting
model (Tabachnick and Fidell 2007).

10

In Table 6, the original model (model 1) also
shows a reasonable fit with chi-square value of
10.52 but it is significant (p = .006), indicating
the fit is not as good. The indices also show a
strong fit but not as good as the revised model.
In addition, the value of the RMSEA is too high

JOURNAL OF SMALL BUSINESS MANAGEMENT


Table 3
Items of New Extracted Factor—Customer Relations
Customer Relations
Sharing Commercial Information with Our Customers
Sharing Technical Information with Our Customers
Customers’ Feedback Contributes to the IT Development
Customer’s Feedback Contributes to the Improvement Business Process

IT, information technology.

Table 4
Variables in IT Adoption in Small Businesses
Independent Variable
External IT Consultants

Internal IT Resources

Supplier Relationsb

Organizational

Customer Relationsb

Measure
•
•
•
•
•
•
•
•
•
•
•
•
•
•

•
•
•
•
•
•
•
•
•
•
•

Seek opinion before acquiring new IT application
Benefit from consultants’ experience
Contribution of knowledge to IT implementation
Decision confirmation on IT application
Usefulness of consultants
Planning of IT
IT investment (infrastructure and resources)
IT investment (training and skill development)
IT resources (skills)
IT resources (infrastructure)
Management involvementa
Management commitmenta
Collaboration with suppliers
Knowledge sharing among suppliers (commercial information)
Knowledge sharing among suppliers (technical information)
Benefit from suppliers’ feedback
Knowledge sharing among employees
Management support and involvement (overall business process)

Employees involvement and contribution
Management and employees awareness of changes
Management and employees awareness of overall business
process
Collaboration with customers
Benefit from customers’ feedback
Knowledge sharing among customers (both commercial and
technical information)
Response to customers’ needs

a

Originally hypothesized under the Organizational factor.
Originally hypothesized under Network Orientation factor.
IT, information technology.
b

NGUYEN, NEWBY, AND MACAULAY

11


Figure 4
Revised Information Technology (IT) Adoption Research Model
Drivers to Adoption
-Internal forces
- External forces

IT Adoption

Adoption Environment
Organizational
- Management
- People and culture
- Knowledge management

Supplier Relations
- Network relationship
- Collaboration
- Knowledge & information

Customer Relations
- Network relationship
- Collaboration
- Knowledge and information

H1

H2

H2

IT
Success
Implementation

H3

External IT Consultant
- Experience

- Recommendations

H4

Internal IT Resources
- Abilities
- Capacities
- Capabilities

(RMSEA = 0.103). This indicates that the revised
IT adoption model (model 2) is a better fit.
H1 predicted a significant and positive relationship between the organizational factor and a
successful implementation. This hypothesis was
supported with a t-value of 5.07 (p < .001). H2
predicted a significant and positive relationship
between network orientation and a successful
implementation. The outcomes of the factor
analysis differentiated orientations between customers and suppliers, which constructed two
factors, one for customers and the other for
suppliers, both hypothesized to be directly and
positively related to successful implementation.

12

Original factor(s)
New extracted factor(s)

Both factors are significantly related to a successful outcome with a t-value of 5.86 (p < .001)
for supplier relations and a t-value of 9.44
(p < .001) for customers relations. H3 predicted

a significant and positive relationship between
external IT consultants and a successful implementation. This hypothesis was supported with
a t-value of 6.72 (p < .001). Finally, H4 predicted
a significant and positive relationship between
internal IT resources and a successful implementation. This too was supported with a
t-value of 9.40 (p < .001).
In summary, all hypotheses (H1, H2, H3,
and H4) are supported, which suggest that a

JOURNAL OF SMALL BUSINESS MANAGEMENT


Table 5
Descriptive and Correlation Matrix
Variable

(1)
(2)
(3)
(4)
(5)

External IT Consultants
Internal IT Resources
Supplier Relations
Organizational
Customer Relations

Meana

S.D.b

(1)

3.28
3.50
3.28
3.78
3.50

0.89
0.69
0.78
0.74
0.74

0.60**
0.51**
0.33**
0.35**

(2)

(3)

0.46**
0.32**
0.35**

0.38**

0.37**

(4)

Cronbach’s
alpha
0.85
0.87
0.81
0.80
0.70

0.51**

a

Calculated by summation and then divided by the number of items for each respective measure.
Standard deviation.
**Correlation is significant at p < .01.
IT, information technology.
b

Table 6
Parameter Estimate Goodness of Fit for IT Adoption Model
Parameters

Standardized
Estimate

Original Model (Model 1)

H1: Organizational → Success Implementation
H2: Network Orientation → Success Implementation
H3: External IT Consultants → Success Implementation
H4: Internal IT Resources → Success Implementation
Revised Model (Model 2)
H1: Organizational → Success Implementation
H2: Supplier Relations → Success Implementation
H2: Customer Relations→ Success Implementation
H3: External IT Consultants → Success Implementation
H4: Internal IT Resources → Success Implementation

t-Value (p)

0.106
0.119
0.110
0.143

5.27
8.25
5.97
6.70

(p < .001)
(p < .001)
(p < .001)
(p < .001)

0.114
0.138

0.151
0.156
0.124

5.07
5.86
9.44
6.72
9.40

(p < .001)
(p < .001)
(p < .001)
(p < .001)
(p < .001)

Goodness of Fit Indicators
Model 1

Model 2

10.520
0.006
2.104
0.939
0.877
0.916
0.103

6.160

0.162
1.232
0.984
0.935
0.972
0.047

χ2
p<
χ2/df
CFI
TLI
NFI
RMSEA

IT, information technology; CFI, comparative fit index; TLI, Tucker Lewis Index; NFI, normed fit
index; RMSEA, root mean square error of approximation.

NGUYEN, NEWBY, AND MACAULAY

13


Table 7
IT Adoptiona

Discussions and Implications

clear indication of why they adopted IT in the
first place, as failure to do so could result in

disconnection between IT adoption and implementation. The results for drivers/reasons to
adopt IT adoption (Table 7) indicate that the
top reason that the respondents’ firms adopted
IT was to meet customers’ requirements (61.9
percent). This suggests that customers are a
driving force that many organizations are
beginning to recognize. Mazurencu-Marinescu,
Mihaescu, and Niculescu-Aron (2007) contend
that customers are becoming more demanding
(e.g., for the convenience of IT for purchasing,
ordering, checking status, and ease of returning
items), so even a small company has to be able
to meet or exceed these expectations to be able
to compete or survive in the market. This supports Reinartz, Krafft, and Hoyer (2004) who
suggest that the current competitive market has
forced firms to move closer to their customers.
The second most cited reason to adopt IT is
growth (61.6 percent). Many researchers have
indicated the positive relationships between IT
and innovation and growth (Bruque and
Moyano 2007; Devaraj and Kohli 2003). As suggested by Atherton (2003), one of the necessary
resources to invest in growth and innovation is
technology. This is in line with Dibrell, Davis,
and Craig’s (2008) study, which suggests that
there is a strong correlation between IT adoption and business growth. Quality improvement
of products and services came third (52
percent), followed by industry requirement
(46.7 percent), and IT investment (42.9
percent). Surprisingly, cost control came last on
the list with only 34.9 percent say “yes” for this

reason. The findings suggest that adopting IT is
not necessarily to control the cost of running
their businesses, but it is an investment as a
means to improve the quality of products and
services, to expand businesses and, more
importantly, to meet and/or exceed their customer’s expectation, which is a requirement
that small enterprises must meet in the current
competitive market.

Drivers/Reasons to Adopt IT
Previous research showed that small businesses are risk adverse (Nguyen 2009); hence,
IT adoption occurs for a reason, or reasons, not
just for the desire to change. This could be to
satisfy customer requirements, industry standards, quality improvement, cost reduction, or
efficiency (Andries and Debackere 2006;
Bhagwat and Sharma 2007; Corso et al. 2003).
Bull (2003) contends that firms should have a

Factors Affecting IT Adoption
Environment and Related to a
Successful Implementation
From the SEM analysis of the revised model,
it can be seen that all five factors, organizational, internal IT resources, external IT expertise, supplier relations, and customer relations,
make a positive contribution to the success of
the IT implementation. Examination of the

Drivers/Reasons

Yes

No

Neutral

Customer
Requirement
Business Expansion
Quality Improvement
Industry Requirement
Investment (Every
Two to Five Years)
Cost Control

61.9

14.3

23.8

61.6
52.0
46.7
42.9

13.4
26.0
21.9
24.8

25.0

22.1
31.4
32.4

34.9

29.1

35.9

a

Measured in percentage.
IT, information technology.

successful implementation of IT in small businesses depends upon the organization, its customers and suppliers, and both internal and
external IT resources. However, as shown in
Table 6, according to the results from the
revised model, the factor that contributes the
most is external IT consultants (standardized
estimate [SE] = 0.156). This is followed by customer relations (SE = 0.151).

Reasons to Adopt IT
The results from drivers to adoption show
the different rationale between the firms in
terms of IT adoption orientation (see Table 7).
The majority of firms are likely to adopt IT to
improve the quality of their products and services or to meet their customer requirements.
Business expansion is another driver to IT
adoption in small businesses, followed by

quality improvement, industry requirement,
and investment. Cost control is last on the list
of the drivers with only 34.9 percent saying
“yes,” but 35.9 percent remaining “neutral.”

14

JOURNAL OF SMALL BUSINESS MANAGEMENT


regression coefficients (standardized estimates
in Table 6) for the revised model shows that
these factors all have a similar influence in
terms of significance. There would seem to be
underlying reasons for the significance of the
contribution of each of these factors.
The organizational factor is directly and
positively related to a successful implementation of IT. It would appear that once the system
is implemented, the organizational relationships can affect its success. This means that for
an IT implementation to be successful, it must
be supported by both management and
employees; in addition, their involvements and
contributions to the change through knowledge
sharing among themselves contribute to a successful implementation. This factor goes hand
in hand with the internal IT resources of the
firm. These resources represent the firm’s IT
capabilities, abilities, and capacities; hence,
they need to be adequate, appropriate, helped
by employees’ knowledge, and attitude and
more so by a positive attitude from owners or

top management. The distinguishing characteristic of management does not simply lie on the
owner/manager’s characteristics, but it is their
commitment and support of the new IT adoption and implementation. Hence, this finding
does not support Thong (1999), which suggests
that owner/manager’s characteristics do not
have a direct effect on the extent of IT adoption. However, it is in line with Anderson and
Huang (2006), Näslund and Newby (2005), and
Igbaria et al. (1997) that the involvement and
commitment of both management and employees contribute to the success of an IT adoption.
As shown in Table 3, “management commitment” and “management involvement” were
extracted to be part of the internal IT resources,
and this factor, representing the IT capabilities,
abilities, and capacities of the firm, is directly
and positively related to the successful implementation. Hence, it is essential to be aware of
the role of the project champion leading the IT
adoption project (Näslund and Newby 2005),
what resources are available (Acar et al. 2005),
teamwork and acceptance (Phelps, Adams, and
Bessant 2007), and knowledge sharing and
training (Zahra, Neubaum, and Larrañeta 2007).
In addition to the internal IT resources, the
use of external IT consultants is very common
in small businesses (Bull 2003; Shin 2006). This
is because many small enterprises initially do
not have expertise; therefore, seeking external
IT skills and knowledge is one of the first steps

in IT adoption. Our findings show that this
factor is directly and positively related to a
successful outcome of an implementation and

is highly correlated to the internal IT resources
(see Table 5). This could be because consultants are independent from the business, and
besides possessing knowledge and experience,
they can provide unbiased recommendations
(Izushi 2005). They often stay on even after
business has enough expertise of its own.
Moreover, the owners/managers feel more
comfortable with consultants as people who
know their system, and they trust them.
New IT that is to be adopted within an
organization should be integrated with suppliers IT, not only for the compatibility of the
technology, but also for the knowledge and
learning opportunities, which could lead to
greater efficiency (Rosenfeld 1996). Small firms
can get assistance from their suppliers, as they
are usually larger and have more resources—if
they can cooperate more efficiently, then it will
improve their own profitability (Au and
Enderwick 2000).
Results from the factor analysis identified
customers as another factor that contributes
positively to the IT adoption environment,
which in turn relates directly to the success of
the implementation. SEM results show that
separating customers and suppliers within the
relationship orientation results in a model with
a better fit. This suggests that the respondents
see relationships with customers differently
than relationships with business partners or
suppliers. The distinctive characteristics of customer relationships in the adoption environment signify the crucially important role of

customers in these small businesses. This
means that small firms should take their customers into consideration when it comes to
changes in IT communication in their daily
business operation, quite apart from their supplier requirements. This finding is in line with
Levy, Loebbecke, and Powell’s (2003) study,
which argued that collaborating with customers
can facilitate the improvement/enhancement of
the products and/or services. Results from the
drivers to adoption (see Table 7) indicate that
the top driver or reason for an organization to
adopt IT is their customers. This result reinforces other views that many small businesses
have become more customer oriented
(Bhagwat and Sharma 2007; Özgener and I˙raz
2006; Reinartz, Krafft, and Hoyer 2004). Firms
should be able to meet and/or exceed their

NGUYEN, NEWBY, AND MACAULAY

15


Figure 5
Revised Framework for Small and Medium-Sized Enterprises (SME)
Information Technology Adoption

Adoption environment

- Network relationship
- Knowledge and
information

Customer
relations

- Management
- People and culture
- Absorptive capacity
of firm

- Network relationship
- Collaboration
- Knowledge and
information
Supplier
relations

Organizational

Market-pull
Innovative

Life cycle/
Maturity

Information
technology
adoption

External
force

Internal
force

Technologypush/
Competitive

Growth
stages
External
IT
consultants

Internal
IT
resources
- Abilities
- Capacities
- Capabilities

- Experience
- Recommendations

IT success
implementation

Drivers
Original factor(s)
New extracted factor(s)
Outcome

customers’ expectation by understanding
their customers’ needs (Gummesson 2004;
Homburg, Wieseke, and Bornemann 2009).
Hence, including the customers as part of the
adoption process would subsequently lead to a
greater chance of a successful implementation
outcome.
This research agrees with studies that have
suggested that IT can provide a wide range of
benefits to small businesses. More importantly,
it suggests a framework (see Figure 5), which
firms can use to assess and evaluate their IT

16

environment, which contributes to a successful
IT adoption outcome. The revised framework
builds on the Nguyen (2009) IT adoption
framework and manifests the important role of
customers within the adoption process. The
results of this study have implications for IT
adoption in small business: first, the study highlights the importance of drivers/reasons for IT
to be adopted. Small business owners/
managers must understand the purpose of the
IT to be adopted; the goals, aims, and objectives must be clear. Second, firms must be

JOURNAL OF SMALL BUSINESS MANAGEMENT


able to assess the factors that are directly

related to the adoption environment, which
can, in turn, contribute to a successful implementation. These factors are (1) the organization, which includes the management, staff,
their knowledge, acceptance, commitment, and
contribution; (2) the internal IT resources,
which are the firm’s IT abilities, capabilities,
and capacities; (3) the external IT consultants,
who can contribute their knowledge and expertise to develop strong IT; (4) the suppliers, who
can provide their assistance for greater efficiency; and (5) the customers, who are the
driving force of the firm. Hence, firms must
engage with each of these factors or risk
failure.

Further Research and
Limitations
The findings from this study extend the
understanding of IT adoption in small business
and help in building a greater understanding of
the factors surrounding the adoption of IT, but
like most empirical research, this study has
limitations. First, the sample size was relatively
small (only 105), although it is within the range
100–150 subjects agreed to be the minimum
satisfactory sample size (Ding, Velicer, and
Harlow 1995 cited in Schumacker and Lomax
2004). Replication of this study using a random
national sample would be of interest: a larger
sample size study would have stronger statistical power, which could be generalized to the
entire population of small enterprises with
greater confidence. Second, the industries
focused on were in manufacturing, retail, and

financial services and were geographically specific to Los Angeles County and Orange County
in Southern California. Finally, only one
respondent was surveyed from each firm. As
this study was specific to Los Angeles County
and Orange County in Southern California,
future research should now be undertaken to
test the model by applying it in other small
business contexts (e.g., different location and
industry), particularly as different countries
(e.g., the United States and United Kingdom)
define small businesses in slightly different
terms.
Despite these relatively minor limitations,
the study discussed in this paper has a number
of important implications. Far from IT adoption
being inappropriate for small businesses, as
suggested by a range of authors previously
identified here, this study suggests that IT

adoption can be extremely beneficial as long as
firms take a broad view of what is needed for
success. This study clearly demonstrates that
there is no single explanatory factor for the
adoption and failure rates of IT in small businesses. Indeed, there are a number of interconnected factors that can clearly be identified as
predictors of success (organization, internal IT
resources, external IT consultants, supplier
relations, and customer relations). These
factors are particularly important in addressing
the tension at the heart of IT adoption in
small businesses: the necessity of improving

customer relations from within a (well documented) culture of risk adversity. Understanding the factors identified in this study can
enable small businesses to minimize the risks
inherent in IT adoption by utilizing planned
strategies for adoption.

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