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Figure 2: Final Model TK Gender Age Education PIE PH PERUSE .165 (2.296) -.224 (3.145) -.229 (2.670) Electronic Commerce and Data Privacy 225 PRIVCON -.056 (.804) ACTUAL .424 (6.195) -.129 (1.804) .264 (3.589) .153 (2.060) IMPPP REGPREF One of the two parts to H7 was supported. H7a was supported (coefficient = .333, t = 4.332), indicating that if an individual has experienced a privacy invasion experience, he or she will be more concerned with the protection of their personal data. H7b was not supported (coefficient = -.038, t = .522), indicating that even a privacy invasion experience will not keep an individual from participating in ecommerce over the Inter<strong>net</strong>. Only one of the two-part H8 was supported. H8a (coefficient = -.011, t = .141) was not supported. Increased technology knowledge did not have a significant effect on privacy concerns. However, higher levels of technology knowledge do tend to significantly increase actual e-commerce use (H8b; coefficient = .185, t = 2.681). H9a was partially supported. Age and education had a significant effect on the level of privacy concerns (coefficient = .170, t = 2.154; coefficient = -.170, t = 2.130; respectively). Gender did not significantly impact the level of privacy concern (coefficient = .066, t = .884). H9b was also partially supported. Education does not have a significant impact on the actual level of e-commerce activity (coefficient = .107, t = 1.506). Age and gender did impact the level of actual e-commerce activity (coefficient = -.145, t = 2.035; coefficient = .166, t = 2.475; respectively). Younger Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
226 Henderson, Snyder and Byrd Table 1:Correlation Matrix with Standard Deviations TK GENDER AGE EDU PIE PH PERUSE PRIVCON ACTUAL IMPPP REGPREF TK (1.496) GENDER .271** (.485) AGE -.220** -.061 (.687) EDU .110 .046 .276** (.591) PIE .015 .058 .194** .304** (.431) PH .171* .101 .131* .215** .194** (2.184) PERUSE .067 .136* .041 .024 -.099 .176* (1.110) PRIVCON -.041 .051 .164* -.011 .310** -.014 -.168* (3.769) ACTUAL .346** .255* -.113 .171* .029 .480** .265** -.130* (5.089) IMPPP .040 -.189** .102 -.050 .128* -.146* -.206** .421** -.197** (2.574) REGPREF -.059 .085 .010 .069 .149* -.016 -.222** .285** .061 .212** (1.774) Note: Standard deviations enclosed in parentheses. * Correlation significant at the 0.05 level; ** Correlation significant at the 0.01 level. Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
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Acquisition Editor: Mehdi Khosrow-P
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The Economic and Social Impacts of
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vi Preface E-commerce is not new, t
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viii the e-commerce’s experience
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x In the sixth chapter, Rick Gibson
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E-Commerce and Executive Informatio
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SMEs in South Africa 127 • Contex
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SMEs in South Africa 131 • Proble
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SMEs in South Africa 133 Cloete, H.
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Key Indicators for Successful Inter
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