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• W2038681061 abstract "Abstract Predictors of shopping online were compared for men and women using a sample of 365 college students. For the men, the only predictor of purchasing products online was the number of hours they spent online. For women, the predictors of making purchases online included anxiety about using computers and attitudes toward money, in addition to the number of hours spent online. The results were examined using OLS and MLE (logit and probit) regressions techniques, and the results from each technique compared and contrasted. Notes 1 There is much discussion about whether online shopping in the Internet age represents a paradigm shift, see Rosenbloom (Citation2002). Nonetheless, it is hard to refute the fact that shopping online is an acquired lifestyle which has not been around for two decades yet. Most importantly, it is commonly recognized that the digital economy, which is defined by the changing characteristics of information, computing, and communication, is the driving force behind economic growth and social change (Brynjolfsson and Kahin, Citation2000). 2 In addition, according to Martin (Citation1998), the literature based on studies from several countries offers ample evidence that boys tend to dominate classroom computer activities, reveal more positive attitudes toward computers, and achieve higher scores in computer science and computer literacy (DeRemer, Citation1989; Clarke, Citation1990; Durnell et al., Citation1995; Kinnear, Citation1995; Pryor, Citation1995; Reinen and Plomp, Citation1997; Whitley, Citation1997). 3 It is not a conclusive phenomenon that males were more likely than females to perceive computer use as fun. For instance, a recent poll by Logitech of Fremont, California found that females are more likely to think that computers are fun compared to males (Kaplan, Citation1994). Female students in Zhang's (Citation2002) study showed more positive attitudes than did male students. 4 The opposite was observed in a single 4th grade classroom in which students were paired up with the same sex partners (Martin, Citation1998). Among these 4th-graders, girls were found to display greater enthusiasm for Internet-based activities than did their male classmates. Overall, the female pairs generally required less teacher intervention than the male pairs. 5 It should be noted that this conclusion does not apply to youngsters. In a study of 910 7th, 9th and 11th grade students (King et al., Citation2002), investigation revealed a small but significant difference in computer anxiety between males and females, with males being more anxious than females. 6 However, there are studies which have found that males report no greater success when searching the Web than did females (Morahan-Martin, Citation1998). The discrepancy in the findings may depend on the purpose of the searching and the content of the Web site. Questionnaire design may play some role too. 7 This trend may continue for a while because only a minority of teenage girls were found to be interested in working in technology after graduation (Cheskin Research and Cyberteens.com, Citation1999). 8 For definition and example of different attitudes toward money and credit cards, see the sub-section of questionnaire design in the third section. 9 See Chapter 17 in Wooldridge's (Citation2003) and especially Chapter 20 in Greene's (Citation1990) discussions about these three specifications for discrete choice models. 10 For a detailed discussion for the probit and logit models, see Greene (Citation1990), Wooldridge (Citation2003), or Ramanathan (Citation2002). 11 It should be noted that some peculiar estimates resulted from the MLE that would not confirm the condition of probability response being restricted to the zero-one interval. More details will be presented in the Discussion section. 12 This is because LPM assumes homoscedasticity, thereby resulting in a higher possibility of statistical significance for the estimates. 13 Ramanathan (Citation2002) labels this approach as the ‘Hendry/LSE approach of modelling from ‘general to small’’ – LSE denotes the London School of Economics for, apart from Hendry, other econometricians who advocate this approach are from LSE. In practice, ‘one starts with a general dynamic model which is overparametrized, that is, has more lags and variables than one would normally start with, and then carries out a data-based simplification through a number of [diagnostic tests] to see if either model or methodology can be improved’. 14 It should be noted that parsimony might not always refer to minimal parametrization. For instance, Harless and Camerer (Citation1994) used parsimony to denote ‘the number of patterns a theory allows’. 15 See Greene (Citation1990) or Wooldrige (2003) for a good example of calculating the marginal effects for the probit and logit estimators. 16 See Greene (Citation1990) for detailed discussion and application of the approximation." @default.
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• W2038681061 date "2005-10-10" @default.
• W2038681061 modified "2023-10-18" @default.
• W2038681061 title "Gender differences in e-commerce" @default.
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• W2038681061 doi "https://doi.org/10.1080/00036840500293292" @default.
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