The perceived strategic value of e-commerce in the face of natural disaster: e-commerce adoption by small businesses in post-Katrina New Orleans.
Kwun, Obyung ; Nickels, David ; Alijani, Ghasem S. 等
INTRODUCTION
The impact of Hurricane Katrina has resulted in serious
implications for small businesses in New Orleans, leaving those
businesses as one of the biggest commercial casualties. With the instant
complete loss of markets due to this natural disaster, the greatest
challenge for these small businesses by far has been the need for
displaced residents to return. Because small businesses rely so much on
the local economy, they have not been able to recover at the same rate
as larger businesses and corporations. Hurricane Katrina has made these
businesses modify the way that they sell, market and produce goods;
technology has also called for businesses to transition the way they do
business (Bring New Orleans Back, 2008).
Among the barriers faced in the post-Katrina era by small
businesses in New Orleans are the reduced rate of tourism to the city
and the inability to reach consumers outside the bayou. This is where
e-commerce can be an effective tool in rebuilding the small businesses
of New Orleans. A phenomenon that is emerging rapidly among businesses
all over the world, E-commerce can be described as the process of
buying, selling, or exchanging products, services, or information via
computer networks, including the Internet (Chaffey, 2004; Turban et. al,
2006). The benefits of e-commerce to an organization include access to
new global customer markets, the creation of new selling channels, and
reduced costs of doing business (Chaffey, 2004; Saloner & Spence
2002; Chaudhury & Kuilboer, 2002; Turban et. al, 2006).
Despite the benefits of e-commerce, its adoption by small
businesses has remained limited (Gallup, 2008). Although some research
has been conducted on the adoption of e-commerce in small businesses
(e.g., Grandon & Pearson, 2003; Mirchandani & Motwani, 2001),
the post-Katrina impact on the small business environment in New Orleans
has produced a unique case in e-commerce adoption among small business.
Also, much of the research in e-commerce studies has focused on
e-commerce consumers rather than on small business owners who fund and
make the ultimate decision on in e-commerce adoption of their
organizations. The purpose of this research is to identify the factors
that influenced perceived strategic value of e-commerce adoption by
small business in post-Katrina New Orleans.
LITERATURE REVIEW
The Technology Acceptance Model (TAM) has been widely used to
explain adoption of e-commerce. TAM was originally developed as an
information systems theory by Davis (1989) to model how users come to
accept and use a technology. Specifically, TAM proposes perceived ease
of use (PEOU) and perceived usefulness (PU) as influencing factors for
technology acceptance. Since its introduction, TAM has provided a basis
for explaining adoption behavior in various contexts (Venkatesh et. al.,
2003).
Another popular theory that has been used to explain technology
adoption is that of diffusion of innovation. According to Rogers (2003),
diffusion of innovation is the study of how, why, and at what rate new
ideas and technology spread through cultures. Diffusion of innovation
theory proposes that decision makers (i.e., management and owners)
within a business or organization will evaluate an innovation's
characteristics such as relative advantage, compatibility, complexity,
trialability, and observability and that their perceptions of these
characteristics will determine whether the organization or business will
adopt this innovation.
PERCEPTION OF STRATEGIC VALUE IN E-COMMERCE
The concept of strategic value in this study is defined as the
summation of perceived benefits from e-commerce minus the summation of
perceived costs of e-commerce over a period of time. The potential
benefits of e-commerce implementation include an increased number of new
customers, better service to loyal customers, and increase in profit and
market share. The strategic value of e-commence can be illustrated as a
value-driver model with four factors driving value creation of
e-commerce: transaction efficiency, complementarities, lock-in, and
novelty (Amit & Zott, 2001). The paper agrees that the adoption of
e-commerce is primarily determined by the owner's/manager's
perception of how much strategic value an innovation can bring to the
organization.
ORGANIZATIONAL READINESS
In relation to organizational readiness for e-commerce adoption,
Mirchandani and Motwani (2001) identified factors that differentiate
e-commerce adopters from non-adopters. The factors include advantage
perceived from e-commerce, the knowledge of the company's employees
about computers, enthusiasm of the top management, and compatibility of
e-commerce with the work of the company. In a study executed by Zhu,
Kramer and Xu (2002), an organization's size has also been
identified as an adoption facilitator, where larger firms were found to
be more likely than small businesses to adopt e-business because the
larger firms (1) tend to have more slack resources to facilitate
adoption, (2) are more likely to achieve economies of scale, (3) are
more capable of bearing the high risk associated with early stage
investment in e-business, and (4) have more market power to influence
trading partners to adopt e-commerce technology. Grandon & Pearson
(2004) found that compatibility with the company's work
environment, enthusiasm of top management, perceived advantage from
e-commerce and knowledge of the company's employees about computers
were significant factors that differentiated between adopters and
non-adopters of e-commerce. Therefore, small businesses considering the
adoption of e-commerce should have top managers who are willing and
ready to implement innovation.
ENTREPRENEURIAL MINDSET
Another factor potentially impacting the adoption of e-commerce is
entrepreneurial mindset. An entrepreneurial mindset has been described
as "a way of thinking about your business that captures the
benefits of uncertainty" (McGrath & MacMillan, 2000, p. 1).
Owners/managers have been found to exert a strong influence when it
comes to an organization adopting e-commerce (Seyal & Rahman, 2003).
Thus, it is reasonable to hypothesize that a small business owner's
entrepreneurial mindset is expected to influence attitude toward
innovations.
INDUSTRY COMPETIVENESS
Another issue encountered in the technology adoption literature is
external factors. An external factor that has been recognized as a
positive force for e-commerce adoption is degree of competitiveness
within the industry (Corbitt & Tanasankit, 2002; Lertwongsatien
& Wongpinunwatana, 2003). This supports an assertion that businesses
adopt e-commerce not only to achieve best practices, gain operational
efficiencies, and to obtain strategic value, but also to cope with
competitive forces within the industry.
Based on the preceding discussion of issues potentially influencing
decisions on e-commerce adoption, this study proposes that the perceived
strategic value of e-commerce is influenced by organizational readiness,
entrepreneurial mindset, and degree of competitiveness within the
industry. The following hypotheses were developed for this study, as
illustrated in Figure 1:
[FIGURE 1 OMITTED]
RESEARCH METHOD
INSTRUMENT
The instrument used for data collection in this study contained two
initial filter questions to assure that, first, the businesses were from
New Orleans and, second, that the business would be properly classified
as a small business. Four demographic questions were next used to
collect information about the participants, which included gender, age,
education, and level of familiarity with e-commerce. These questions
were followed by four general questions about the organization. The
remaining questions were those adapted or modified from previous
research and used to measure the topical constructs of this study:
organizational readiness, entrepreneurial mindset, and external factors.
PARTICIPANTS
For this study, owners/managers of small businesses were targeted
throughout New Orleans. According to the Seyal & Rahman (2003)
study, small business characteristics include small management teams and
a strong owner influence. Different agencies and businesses use
different criteria to determine whether a business is small, such as the
number of employees, annual income earned and relative dominance in
their industry. Different ranges of employee size (size standard) for
small businesses are encountered in the literature, depending on the
source this number can fall anywhere between 50 and 500 employees. For
the purpose of this study, the number of employees was used as the
determining factor for classification as a small business: firms that
employed 100 or less individuals were considered as small businesses.
DATA COLLECTION
Data was gathered by utilizing an Internet-based survey that was
created at www.freeonlinesurveys.com. The web address of the survey was
sent to small business email addresses collected from a local telephone
directory and The Yellow Pages.com, an online business directory.
Following the initial email request, two e-mail reminders were sent to
the selected businesses asking them to complete the survey for the
research.
CHARACTERISTICS OF THE SAMPLE
There were 198 respondents in this study: 66 indicated that they
were e-commerce adopters and 132 indicated that they were not adopters
(see Table 1). Of the 198 respondents, 60.6 percent are male, 51 percent
are in the age range of 30-39, and 55.6 percent reported holding a
bachelor's degree. The majority of the e-commerce adopters were in
retail and service businesses (40.9% and 28.8%, respectively), while
among the non-adopters the majority were in service and construction
businesses (39.4% and 34.8%, respectively). The majority (67.2%) of
total respondents indicated that their PCs are used for clerical work.
It is of interest to note that the majority (58.3%) of non-adopters do
not have a website. The overwhelming majority of all respondents (86.4
percent) indicate they are either very familiar or somewhat familiar
with e-commerce.
DATA ANALYSIS
Partial Least Squares (PLS) analysis was used to test the proposed
research model. PLS is a multiple regression-based technique for testing
a research model with multiple-item constructs and direct and indirect
paths. It has been considered appropriate for exploratory study and
testing predictive models. PLS, as a structural equation modeling
technique, recognizes two parts of model testing: a measurement model
and a structural model (e.g., Barclay et al., 1995; Fornell &
Larcker, 1981). In order to test a research model, the measurement model
first has to be evaluated, and then the structural model has to be
tested. The assessment of both models was conducted for this study using
SmartPLS 2.0.
The measurement model addresses the relationship between the
constructs and the items used to measure them. The test of the
measurement model consists of the estimation of the convergent and
discriminant validities of the measurement instrument. However,
reflective and formative measures should be treated differently.
Formative items are considered to form or cause the construct to
measure. Thus, these items are not expected to correlate or show
internal consistency (Chin, 1998). For this reason, the item weights for
formative measures have been used to test the relevance of the items to
the constructs (Barclay et al., 1995; Wixom and Watson, 2001). Table 2
shows the relationship between the constructs and the items in this
study. Perceived Strategic Value, which is only dependent construct, was
considered Reflective.
RESULTS
MEASUREMENT MODEL
Although formative and reflective constructs are treated
differently, the loadings are used for interpretive purpose and for the
calculation of reliabilities. However, it has been suggested that an
absolute value of factor loadings of .30 is considered to meet the
minimal level, loadings of 0.40 are considered more significant, and
loadings of 0.50 or greater are considered very significant (Hair et.
al., 1998). Average variance extracted (AVE) of 0.50 or above has also
been used to support the convergent validity of the constructs (Fornell
& Larcker, 1981).
Table 3 shows individual item loadings and associated weights for
the related construct. All of the composite reliabilities exceed 0.7.
For the reflective construct of perceived strategic valuethe loading on
some items may be considered weak, but none of the items shows a loading
of lower than absolute value of 0.4. This shows some evidence for
internal consistency. Table 5 shows an AVE of 0.48, which is below the
acceptable level. For the formative constructs, some of the items show
negative weights. Formative items are considered to form or contribute
to the construct. The negative weights indicate a contradiction to the
original expectation. The items with negative weights are OR3, OR5, OR9,
EM4, and EM7.
Discriminant validity is adequate when the average variance
extracted from the construct is greater than the variance shared between
the construct and other constructs. Table 5 shows correlations between
constructs and square root of AVE. The square root of AVE for the
perceived strategic value variable is greater than the correlations with
other constructs. Also, the cross loadings in Table 4 show that items
for perceived strategic value are loaded higher on that construct than
on other constructs. This indicates some evidence for discriminant
validity.
STRUCTURAL MODEL
In order to improve the validity of the results, the items with
negative weights were removed when the structural model was tested. As a
result, OR3, OR5, OR6, OR9, EM4, and EM7 were dropped to estimate the
structural model. Figure 2 shows the significance and the strength of
the relationships between the constructs and R2, which indicates the
predictive power of the model. As hypothesized, organizational
readiness, entrepreneurial mindset, and industry competitiveness were
positively associated with perceived strategic value of e-commerce with
path coefficients of 0.42, 0.21, and 0.22 respectively (H1, H2, &
H3). And 45% of variance of perceived strategic value of e-commerce was
explained by the three proposed constructs. Organizational readiness
shows the highest positive path coefficient with perceived strategic
value. Table 6 summarizes the results of the hypotheses in this study.
[FIGURE 2 OMITTED]
CONCLUSIONS
This study investigated factors that influenced e-commerce adoption
among small businesses in post-Katrina New Orleans. The demographic and
organizational characteristics data collected from the participants
paints a very clear picture of the adopters: all of the adopters were
either very familiar or somewhat familiar with e-commerce and over
ninety-five percent of the adopters indicated their level of education
to be at least four years of college. In addition, forty percent of the
adopters worked in organizations doing business in the retail industry.
The results of this study show that organization readiness,
entrepreneurial mindset, and industry competitiveness influenced the
participants' perceptions of the perceived strategic value of
e-commerce. Previous research has indicated that small businesses show a
low rate of e-commerce adoption compared to larger corporations. In
order to promote e-commerce adoption by small business, especially those
small businesses that play a major role in New Orleans'
post-Katrina recovery efforts, government agencies aiding those
businesses need to emphasize the potential importance of these factors.
A major finding of this study is that improving the organizational
readiness of small businesses for e-commerce is a key to successfully
promoting its adoption.
LIMITATIONS
There are several limitations to this study. Because of the weak
validity of the items to measure the constructs in the model,
interpretation of the results requires some caution. In addition, the
wordings of questions on the survey instrument create the possibility of
ambiguity considering the respondents' characteristics. Because the
respondents to the instrument used in this study were business
owners/managers and not customers who are the typical users of
e-commerce, this might have misled the respondents in responding to some
of the survey questions. Finally, while three constructs were considered
in the study, there are many other possible factors previously
recognized by other studies.
IMPLICATIONS FOR ADDITIONAL RESEARCH
For future research on this topic, a replication of the basic
premise of this study with a refinement of the survey instrument could
be done in another city that has experienced similar problems.
Additional research efforts could be made to examine other previously
recognized factors such as computer literacy, types of industry, etc.
Finally, it would be also interesting to expand the findings of this
study by examining the impact of the variables on the level of adoption
of electronic commerce.
Appendix A: List of Items
Constructs Items Description
Perceived strategic Implementing e-commerce would:
value of e-commerce
PSV1 Enable my organization to generate
new business opportunities.
PSV2 increase the availability of our
products or services to our
customers
PSV3 help my organization to reach new
customers
PSV4 enable my organization to provide
better service to my customers
PSV5 enable my organization to process
transactions at a lower cost
PSV6 enable my organization to reduce the
cost of doing business
PSV7 enable my organization to expand its
market share
PSV8 Provide my customers with a more
satisfying shopping experience.
PSV9 Enable my organization to increase
sales to existing customers.
Entrepreneurial Mindset EM1 How entrepreneurially oriented is
your organization?
EM2 Compared to your immediate
competitors, how entrepreneurially
oriented do you think your
organization is?
EM3 How well does your organization find
new business or markets to target?
EM4 How well does your organization
enter new markets before your
immediate competition.
EM5 How well does your organization
introduce new products or services
before your competitors do.
EM6 How well does your organization
strive to lower costs faster than
your competitors.
Agree or disagree with the following
statements:
EM7 The risk of missing an opportunity
is just as important as the risk of
failure.
EM8 I must be willing to accept at least
a moderate level of risk of
significant losses.
Organizational Readiness E-commerce is compatible:
OR1 with the needs of our business
OR2 with other systems my organization
uses
OR3 with the culture of our organization
My organization has:
OR4 The financial resources to implement
e-commerce.
OR5 The financial resources to support
e-commerce.
OR6 The technological resources to
implement e-commerce.
OR7 The technological resources to
support e-commerce.
OR8 The logistics necessary to support
e-commerce.
OR9 The personnel to implement
e-commerce.
OR10 The personnel to support e-commerce.
Industry Competitiveness IC1 Competition will make it necessary
for our organization to implement
e-commerce.
IC2 In order to be leader in my
organization's industry, we need to
implement e-commerce.
IC3 Competition is forcing my
organization to implement
e-commerce.
IC3 The government provides incentives
for my organization to implement
e-commerce.
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Obyung Kwun, Southern University at New Orleans
David Nickels, University of North Alabama
Ghasem S. Alijani, Southern University at New Orleans
Adnan Omar, Southern University at New Orleans
Table 1: Characteristics of the Sample
Sample Characteristics Adopters Non-Adopters All
Questions N=66 N=132 N=198
No No (%) No (%)
Gender
Male 45 68.2 75 56.8 120 60.6
Female 21 31.8 57 43.2 78 39.4
Age
20-29 28 21.2 40 20.2
30-39 12 18.2 59 44.7 101 51.0
40-49 42 63.6 33 25.0 45 22.7
50-59 12 18.2 12 9.1 12 6.1
Education
High School 19 14.4 19 9.6
Technical College 3 4.5 56 42.4 59 29.8
4-Year College 56 84.8 54 40.9 110 55.6
Masters Degree 7 10.6 3 2.3 10 5.1
Type of Business
Manufacturing 5 7.6 2 1.5 7 3.5
Wholesale 8 12.1 1 0.8 9 4.5
Retail 27 40.9 27 20.5 54 27.3
Construction 5 7.6 46 34.8 51 25.8
Service 19 28.8 52 39.4 71 35.9
Other 2 3.0 4 3.0 6 3.0
PC use
Clerical Support 32 48.5 133 67.2
Process/production support 18 27.3 101 76.5 44 22.2
Decision making support 13 19.7 26 19.7 18 9.1
Strategic support 3 4.5 5 3.8 3 1.5
Website
Have 66 100.0 55 41.7 121 61.1
No 77 58.3 77 38.9
Website Done
In-house 39 59.1 14 10.6 33 27.3
Outsourced 27 40.9 41 31.1 88 72.7
Familiar with e-commerce
Very familiar 21 31.8 13 9.8 34 17.2
Somewhat familiar 45 68.2 92 69.7 137 69.2
Not familiar -- -- 27 20.5 27 13.6
Table 2. Measurement Model
Constructs Model Relationship
Organizational Readiness (OR) Exogenous Formative
Entrepreneurial Mindset (EM) Exogenous Formative
Industry Competitiveness (IC) Exogenous Formative
Perceived Strategic Value (PSV) Endogenous Reflective
Table 3. Weights and Loadings
Variables Weights Loadings
Organizational Readiness Composite Reliability = 0.79
OR1 0.60 0.75
OR2 0.10 0.51
OR3 -0.24 0.08
OR4 0.43 0.57
OR5 -0.11 0.24
OR6 0.01 0.12
OR7 0.31 0.52
OR8 0.15 0.28
OR9 -0.13 0.24
OR10 0.21 0.61
Entrepreneurial Mindset Composite Reliability = 0.71
EM1 0.41 0.66
EM2 0.41 0.72
EM3 0.30 0.64
EM4 -0.01 0.40
EM5 0.13 0.35
EM6 0.24 0.39
EM7 -0.21 -0.02
EM8 0.36 0.30
Industry Competiveness Composite Reliability = 0.75
IC1 0.74 0.90
IC2 0.30 0.58
IC3 0.16 0.58
IC4 0.19 0.37
Perceived Strategic Value Composite Reliability = 0.87
PSV1 0.08 0.44
PSV2 0.14 0.62
PSV3 0.09 0.47
PSV4 0.15 0.64
PSV5 0.22 0.81
PSV6 0.20 0.78
PSV7 0.19 0.71
PSV8 0.15 0.68
PSV9 0.24 0.70
Table 4. Cross Loadings
OR EM IC PSV
OR1 0.75 0.27 0.23 0.45
OR2 0.51 0.26 0.21 0.31
OR3 0.08 0.28 0.14 0.05
OR4 0.57 0.04 0.31 0.34
OR5 0.24 0.31 0.36 0.15
OR6 0.12 0.26 0.24 0.07
OR7 0.52 0.28 0.41 0.31
OR8 0.28 0.11 0.14 0.17
OR9 0.24 0.15 0.13 0.14
OR10 0.61 0.21 0.43 0.37
EM1 0.12 0.66 0.26 0.28
EM2 0.25 0.72 0.36 0.30
EM3 0.07 0.64 0.22 0.27
EM4 0.11 0.40 0.19 0.17
EM5 -0.02 0.35 0.05 0.15
EM6 0.11 0.39 0.12 0.16
EM7 0.02 -0.02 0.00 -0.01
EM8 0.10 0.30 0.05 0.13
IC1 0.32 0.33 0.90 0.44
IC2 0.35 0.09 0.58 0.28
IC3 0.34 0.41 0.58 0.28
IC4 0.21 0.18 0.37 0.18
PSV1 0.28 0.03 0.14 0.44
PSV2 0.37 0.16 0.23 0.62
PSV3 0.22 0.09 0.17 0.47
PSV4 0.34 0.26 0.28 0.64
PSV5 0.46 0.38 0.46 0.81
PSV6 0.48 0.25 0.39 0.78
PSV7 0.43 0.37 0.34 0.71
PSV8 0.37 0.22 0.24 0.68
PSV9 0.50 0.47 0.47 0.70
Table 5. Average Variance Extracted and Correlations
OR EM IC AVE(SQRT)
PSV 0.60 0.42 0.49 0.48 (0.66)
Table 6. Hypotheses Tests
t-
Hypotheses Statistic Results
H1: Organizational readiness has a positive 5.54 Supported
effect on perceived strategic value of
e-commerce.
H2: Entrepreneurial mindset has a positive 2.38 Supported
effect on perceived strategic value of
e-commerce.
H3: Industry competitiveness has a positive 2.78 Supported
effect on perceived strategic value of
e-commerce.
