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An Empirical Examination of Factors Affecting Adoption of an Online Direct Sales Channel By Small and Medium-Sized Enterprises. By Xiaolin Li . Economic Contributions of SMEs. UK, 70% of the workforce (Notman 1998). Ireland, 99.4% of all enterprises (Forfas 1999).
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By Xiaolin Li
Source: US Small Business Administration Office of Advocacy, 2006
What are the factors that affect the adoption and use of E-commerce among SMEs? In particular, what are the drivers of the adoption and use of ODSC among SMEs?
Figure 1: Paradigm of The Adoption of An Innovation by an Individual Within a Social System
Source: Rogers, 1962, p306
“What an individual/organization is determines what it does”
e.g., industry, age, firm size, expertise, experience, resources, attitude
“What the tech. offers determines an individual/organization’s intention to use it”
e.g., usefulness, ease of use, relative advantage, risks, security, cost
“Where an individual/organization is in determines what it does”
e.g., institutional influence, competitive pressure, influences from suppliers, resellers, and customers
Similar to environment but DC emphasizes the situation shaped by adoption-relevant factors
Behavioral Intention to Adopt
Figure 1: The Classification Model of IS Adoption Factors
DE: Decision Entity
DO: Decision Object
DC: Decision Context
Classification Model of IS Adoption Factors (con’t)
Ease of Use
Behavioral Intention Toward ODSC
Model of ODSC Adoption Among SMEs
Generating Item Pool under Dimensions
(Lit Review & Interviews)
Purifying Survey Items
(Expert Reviews & Interviews)
Pre-Testing and Revision of the online version of the Instrument
Instrument Creation & Refinement Phase
Revision Based on feedbacks of Pilot Study
Figure 3: An overview of phases of the Study
Statistical Analysis and Hypotheses Testing
Data Analysis Phase
Phases of Study
NNFINon-Normed Fit Index,aka Tucker-Lewis Index
< 0.85 indicate unacceptable fit,
0.85-0.89 mediocre fit, (model could be improved substantially)
0.90-0.95 acceptable fit,
0.95-0.99 close fit and
=1, exact fit;
RMSEA: Root Mean Square Error of Approximation
√[(c2/df - 1) /(N - 1)]
where N the sample size and df the degrees of freedom of the model. (If c2 is less than df, then RMSEA is set to zero). Models whose RMSEA is .10 or more have poor fit.
AIC: Akaike Information Criterion
c2 + k(k - 1) - 2df
where k is the number of variables in the model and df is the degrees of freedom of the model. The AIC penalize every additional parameter estimated. The focus is on the relative size, the model with the smaller AIC is preferred.