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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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slide1

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
Economic Contributions of SMEs
  • UK, 70% of the workforce (Notman 1998).
  • Ireland, 99.4% of all enterprises (Forfas 1999).
  • EU as a whole, over 90% of the total number of EU businesses, 25% of EU turnover; more than 90% of the total European enterprise population (16 million businesses) are of very small size employing fewer than 10 people (Dutta and Evrard, 1999).).
  • China, 87% industrial enterprises are SMEs, produced 41% of GDP (National Bureau of Statistics of China, 2003).
  • Asia-Pacific region as a whole, nearly 72% of all private sector enterprises are micro-enterprises representing 20% of private sector employment.
  • UN, SMEs account for more than 90% of all jobs, sales, and value-added in developing countries; in developed countries, they account for over 50% of these measures (UN, 1992).
economic contributions of smes us
Economic Contributions of SMEs--US
  • Represent 99.7% of all employer firms.
  • Employ about 50% all private sector employees.
  • Pay more than 45% of total U.S. private payroll.
  • 60-80% of net new jobs annually over the last decade.
  • Create more than 50% of nonfarm private GDP.
  • Supplied over 23% of total value of federal prime contracts in 2005
  • Produce 13-14 times more patents per employee than large patenting firms. These patents are twice as likely as large firm patents to be among the 1% most cited.
  • Are employers of 41% of high tech workers (such as scientists, engineers, and computer workers).
  • Made up 97% of all identified exporters and produced 28.6% of the known export value in FY 2004.

Source: US Small Business Administration Office of Advocacy, 2006

e commerce unprecedented opportunities for smes
E-commerce--Unprecedented Opportunities for SMEs
  • E-commerce levels playing field and makes it possible for SMEs to overcome their constraints in size, business scope, and budget, and to compete with larger firms.
  • Benefits brought by E-commerce to SMES
    • Inexpensive and convenient information gathering Dewan (2000)
    • Enhanced market position (Lohrke, Franz, Franklin, and Frownfelter-Lohrke, 2006)
    • Global competitiveness (Hamill and Gregory, 1997; Lituchy and Rail, 2000; Nieto, and Fernández, 2006)
research problem
Research Problem
  • Despite the opportunities brought to SME by e-commerce technologies, penetration rate of e-commerce among SMEs is still low (March and Ngai, 2006).
  • Majority of SMEs uses Internet simply for information gathering purpose (Kula and Tatoglu, 2003)
  • 51% of SMEs owns business websites only about 15% of SMEs sells on the Internet. (Dholakia and Kshetri, 2004)
  • Website adoption within SMEs is widespread, the number offering e-commerce activities is still declining or static (Houghton and Winklhofer, 2004)
  • Adoption disparity (Hawkins and Prencipe 2000, in Beach).

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?

research objectives
Research Objectives
  • To examine the overall level of adoption and usage of ODSC among SMEs in the US
  • To propose a Classification Model of IS Adoption Factors
  • To propose and empirically test a behavioral model of ODSC adoption by SMEs
theoretical foundation
Theoretical Foundation

Figure 1: Paradigm of The Adoption of An Innovation by an Individual Within a Social System

Source: Rogers, 1962, p306

rogers paradigm
Rogers’ Paradigm
  • Encompasses a robust adoption factor classification model.
  • The adoption of an innovation by an individual contains three divisions
    • Antecedents (factors present in the situation prior to the introduction of an innovation)
      • actor’s identity
      • perceptions of the situation
    • Process (information sources as stimuli)
      • perceived characteristics of the innovation
    • Results (adoption or rejection of the innovation).
classification model of is adoption factors
Classification Model of IS Adoption Factors
  • Three dimensions of adoption factors
    • Decision Entity (DE)

“What an individual/organization is determines what it does”

e.g., industry, age, firm size, expertise, experience, resources, attitude

    • Decision Object (DO)

“What the tech. offers determines an individual/organization’s intention to use it”

e.g., usefulness, ease of use, relative advantage, risks, security, cost

    • Decision Context (DC)

“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

slide10

DC

Behavioral Intention to Adopt

DE

DO

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)

model of odsc adoption among smes
Model of ODSC Adoption Among SMEs
  • DE factors
    • Expertise in the Internet
    • Resource Slack
    • Risk Propensity
  • DO factors
    • Perceived relative advantage
    • Perceived ease of use
  • DC factor
    • Perceived Competitive pressure
research hypotheses
Research Hypotheses
  • Hypothesis 1a: Resource slack will positively affect an SME’s perceived ease of use of ODSC.
  • Hypothesis 1b: Resource slack will positively affect an SME’s behavioral intention to adopt or continue to use ODSC.
  • Hypothesis 2: Perceived expertise in the Internet will positively affect an SME’s perceived ease of use of the online direct sales channel.
  • Hypothesis 3a: An SME’s risk propensity will positively affect its perception of relative advantage of the ODSC.
  • Hypothesis 3b: An SME’s risk propensity will positively affect its perceived ease of use the ODSC.
  • Hypothesis 3c: An SME’s risk propensity will positively affect its intention to adopt the ODSC.
research hypotheses con t
Research Hypotheses (con’t)
  • Hypothesis 4: Perceived channel advantage will positively affect an SME’s intention to adopt or continue to use the ODSC.
  • Hypothesis 5: Perceived ease of use will positively affect an SME’s perception of relative advantage of the ODSC.
  • Hypothesis 6a: Perceived competitive pressure will positively affect an SME’s perception of relative advantage of the ODSC.
  • Hypothesis 6b: Perceived competitive pressure will positively affect an SME’s intention to adopt or continue to use the ODSC.
slide15

DO Factors

Perceived

Ease of Use

DE Factors

H5

H2

Expertise

Perceived

Relative Advantage

H1a

H3b

Resources Slack

H3a

H4

H1b

Risk Propensity

H3c

Behavioral Intention Toward ODSC

H6a

H6b

DC Factor

Perceived

Competitive Pressure

Model of ODSC Adoption Among SMEs

research methods
Research Methods
  • Data collection method: web-based survey among SMEs
  • Sample: SMEs in the State of Ohio
  • Data collection procedures
    • Generate a list of business organizations
    • Telephone the leadership of the organizations
    • Email those who agree to help an pre-composed invitation message and request them to use it to invite their members/clients to participate in the survey
    • Two days later, contact the business organizations again to check status
    • One week later, send a pre-composed reminder email message to the business organizations and request them to send it to their clients/members
    • Two months later, ask the organizations to send a third reminder message to their clients
slide18

Specifying Construct Domain & Dimensions

(Lit Review)

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

Instrument

Development

Pilot Study

Revision Based on feedbacks of Pilot Study

Pilot Phase

Figure 3: An overview of phases of the Study

Large-Scale Survey

Large-Scale

Survey Phase

Data Collection

Statistical Analysis and Hypotheses Testing

Data Analysis Phase

Statistical

Analysis

Phases of Study

scales and measures
Scales and Measures
  • Relative Advantage
    • PU1--Selling online will increase our overall sales revenues.
    • PU2-- Selling online will bring us additional profits.
    • PU3--Selling online will help improve our ordering process.
  • Perceived Ease of Use
    • PEU1-- Obtaining an e-commerce website to sell our products/services will be easy
    • PEU2-- Training competent personnel to support an e-commerce system will be easy.
    • PEU3--Maintaining an e-commerce website will be easy for our firm.
  • Expertise
    • Scale: 1=Novice, 4=Competent, 7=Expert.
    • Managers: 1234567
    • All Other Employees 1234567
  • Resources
    • RESO1--Our firm already has a pretty good business website.
    • RESO2--We have the resources necessary to build an e-commerce website.
    • RESO3--We have the IT personnel necessary to maintain an e-commerce website.
  • Risk Propensity
    • RP1--Our firm is usually willing to take risks
    • RP2--Our senior managers are willing to take risks
  • Competitive Pressure
    • COMP1--Most of our competitors sell online.
    • COMP2--Our main competitors are already selling successfully online
    • COMP3--Our main competitors are seizing our market share.
  • Behavioral Intention to Adopt ODSC
    • BI1--Our firm intends to sell products/services on the Internet within the next two years.
    • BI2--I predict my firm will start to sell products/services on the Internet within the next two years.
    • BI3--Our firm plans to sell products/services on the Internet within the next two years
methods of statistical analysis
Methods of Statistical Analysis
  • Structural Equation Modeling (SEM) will be used for data analysis. SEM is a powerful analytical tool that combines several statistical techniques, including factor analysis, path analysis, and multiple regression. It is more powerful than multiple regression because it takes into account the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, multiple latent independents each measured by multiple indicators, and one or more latent dependents also each with multiple indicators (Garson, 2007).
slide21

Measurement & Structural Model

H5

H2

H1a

H3b

H4

H3a

H1b

H6a

H3c

H6b

statistical analyses
Statistical Analyses
  • Validation of Instrument
    • Construct validity (confirmatory factor analysis)
      • Convergent validity
      • Discriminant validity
    • Internal Consistency Reliability
      • Cronbach Alpha
  • Testing of Research Model
    • Goodness of fit
    • Testing of hypotheses proposed
slide23

Goodness of Fit Statistics to be Analyzed

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.

contributions of dissertation
Contributions of Dissertation
  • the classification model provides a simple but robust framework for categorizing existing factors identified in previous IS adoption studies. It will also be useful for guiding the identification of new factors in future IS adoption studies.
  • research model on the adoption of ODSCamong SMEs, which is proposed and empirically tested in this dissertation, will not only enhance our knowledge of the pattern of SMEs’ adoption of ODSC, but also improve our understanding of SMEs’ adoption and use of IS innovations in general.
  • The examination of ODSC adoption among SMEs provides empirical evidence regarding what drive the adoption and use of ODSC among SMEs, which in turn, will help facilitate better decision-making by managers of electronic market service providers, e-commerce system developers, and policy-makers of relevant governmental agencies to stimulate the use of ODSC among SMEs.
  • The study will also enhance SMEs’ knowledge of what other SMEs are thinking about and doing with ODSC, which will eventually influence their own decision in the future on the use of ODSC.