Abstract
The past decade has produced much research investigating online market entry, both by blended traditional and by virtual sellers.
The MIT Center for Digital Business and the Columbia Institute for Tele-Information provided support during the time this paper was revised. Helpful comments were provided by participants in seminars at Columbia University and Curtin University. Bill Greene was generous in his assistance on the econometric method. We are grateful to Warren Kimble and Aaron Morey for excellent research assistance. The authors are responsible for all remaining errors.
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- 1.
The survey field work is conducted by the Interviewer Quality Control Australia (IQCA) quality accredited market research firm McGregor Tan Research. The telephone interview software used to initiate the contact with respondents is the CATI (computer aided telephone interviewing system). The sample units are selected at random from the Telstra Yellow Pages. Three screening questions are asked prior to the conduct of the survey. Funding for survey is provided by Australian Research Council Large Grant No. A00105943. The questionnaire contains 59 questions. The questionnaire is comprised of the sections: (a) Respondent and Firm Profile; (b) Reasons for Entering Online Markets and Initial Investment; (c) Initial Online Market Outcomes; and (d) Online Market Futures.
- 2.
BUSINESS is comprised of the ANZIC single-digit divisions: “Finance and Insurance”; “Property and Business Services”; and “Wholesale Trade.”
- 3.
These variables are often considered by economists as objectives of agent optimization. Steinfield et al. (2002) argue that innovation is based on the search for synergistic opportunity. That is, aligning goals across physical and virtual channels suggests that the “parent” firm benefits from sales stemming from either channel. Higher revenues can arise from geographic and product market extension, thus adding revenue streams otherwise not feasible from physical outlets. Synergistic benefits also arise from lower costs (savings may occur through improved labor productivity, and reduced inventory, advertising and distribution costs).
- 4.
Although the firms comprising the sample are “small”, the potential for scale economies arises as the employee range is [1, 200]. Also, 400 firms are located within (the large cities) of Melbourne and Sydney. Finally, only 72.4 % of the sample firms operate a single site.
- 5.
Maddala (1983: 122) states that the parameters of the second equation are not identified if there are no exclusion restrictions on the exogenous variables. Wilde (2000) demonstrates, for multiple equation probit models with endogenous dummy regressors, that no restrictions are needed if there is sufficient variation in the data, viz., each equation contains at least one varying exogenous regressor.
- 6.
For all probabilities to be positive requires \( 0 \,< \mu_{0} < \mu_{1} < \mu_{2} < \mu_{3} \) and \( 0 < \lambda_{0} < \lambda_{1} < \lambda_{2} < \lambda_{3} . \)
- 7.
The standard errors of the coefficients for the bivariate model are not correct because of the scaling effect.
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Randolph Beard, T., Madden, G., Azam, M.S. (2014). Blended Traditional and Virtual Seller Market Entry and Performance. In: Alleman, J., Ní-Shúilleabháin, Á., Rappoport, P. (eds) Demand for Communications Services – Insights and Perspectives. The Economics of Information, Communication, and Entertainment. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-7993-2_5
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