The Development of the Attitudes toward Computer Usage Scale

Popovich, Paula M.; Hyde, Karen R.; Zakrajsek, Todd; Blumer, Catherine

doi:10.1177/0013164487471035

Cited by 113 publications

(39 citation statements)

References 6 publications

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“…Overall, computer user attitude scores were high for all respondents. This is consistent with recent studies and different from what was reported in earlier studies [34], suggesting an upward change in user's attitude toward computers over the years. Table 3 provides pre-and post-training scores for computer self-efficacy and Internet self-efficacy for all respondents.…”

Section: Data Analysis and Resultssupporting

confidence: 87%

A contingency model of computer and Internet self-efficacy

Torkzadeh

Chang

Demirhan

2006

Information & Management

129

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Section: Data Analysis and Resultssupporting

confidence: 87%

A contingency model of computer and Internet self-efficacy

Torkzadeh

Chang

Demirhan

2006

Information & Management

129

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“…Previously, the opposite might have been the case. Popovich, Hyde, Zakrajsek, and Blumer (1987) found that women's attitudes toward computers were significantly more negative than men's on most aspects of computer usage. Terlecki and Newcombe (2005), on the other hand, report more recent Web statistics showing that women marginally surpass men in their Internet usage; but they also found that women lag behind men in using computers in spatial activities.…”

Section: Discussionmentioning

confidence: 92%

Computerizing the Mental Rotations Test: Are gender differences maintained?

Monahan

Harke

Shelley

2008

Behavior Research Methods

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422Linn and Petersen (1985) separated spatial ability tests into three categories based on effect size. One category of ability was spatial visualization, the ability to manipulate complex spatial information when several stages are needed to produce the correct solution. Gender differences in spatial visualization had a small effect size and were statistically nonsignificant. Another category of ability was spatial perception, the ability to determine spatial relations despite irrelevant information. Gender differences in spatial perception had a medium effect size and were statistically significant. The third category of ability was mental rotation, the ability to rotate two-or three-dimensional figures quickly and accurately in imagination and to compare them with other similar figures. Mental rotation was the only spatial ability category to yield a large gender difference effect size and was also statistically significant.More recently, Voyer, Voyer, and Bryden (1995) conducted a meta-analysis of the three categories of spatial abilities and found similar results. Spatial visualization tasks showed significant gender differences only for participants who were over 18 years old; for younger participants, there were no significant gender differences. Spatial perception tasks showed a significant gender difference for participants who were 13 and older. Mental rotation tasks showed gender differences for participants of all ages. For all three spatial ability categories, there was a significant linear increase in effect size with increasing age, possibly indicating that sexual differentiation is important in gender differences in spatial ability. They further found that the largest effect was found for the Mental Rotations Test (MRT; Vandenberg & Kuse, 1978).Wraga, Duncan, Jacobs, Helt, and Church (2006) report that, although traditional gender gaps in cognitive performance have diminished over many years, mental rotation tasks have consistently yielded large and reliable gender differences of about 1 standard deviation with no significant reduction. At least one study, however, found that, under certain circumstances, gender differences in the MRT did not hold.Goldstein, Haldane, and Mitchell (1990) administered the MRT under two different sets of instructions. In Experiment 1, participants were allowed the standard 3 min of time for each 10-item half of the test. The standard method of scoring the MRT is to count correct only those items for which both correct alternatives are marked (maximum possible score of 20). Using the standard scoring method, the probability of scoring a point by chance is .16. Instead of using the standard method of scoring, Goldstein et al. calculated scores in two alternate ways. One way was to count the number of correct alternatives chosen (maximum possible score of 40). With this method, the probability of scoring a point by chance is .5 for the first guess of an item. Goldstein et al.'s second scoring method was to derive the ratio of the number of correct alternatives chosen ...

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“…The first questionnaire was aimed at assessing the dimensions of attitude toward art (4 items; Stokmans 1999) and attitude toward technology (6 items;Popovich et al 1987). The second questionnaire was aimed at assessing their experience in using the multimedia guide along diverse dimensions (all as Likert scales).…”

Section: Assessing the Adaptive Multimedia Guide Based On Like-o-metermentioning

confidence: 99%

Adaptive, intelligent presentation of information for the museum visitor in PEACH

Stock¹,

Zancanaro²,

Busetta³

et al. 2007

User Model User-Adap Inter

148

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The study of intelligent user interfaces and user modeling and adaptation is well suited for augmenting educational visits to museums. We have defined a novel integrated framework for museum visits and claim that such a framework is essential in such a vast domain that inherently implies complex interactivity. We found that it requires a significant investment in software and hardware infrastructure, design and implementation of intelligent interfaces, and a systematic and iterative evaluation of the design and functionality of user interfaces, involving actual visitors at every stage. We defined and built a suite of interactive and user-adaptive technologies for museum visitors, which was then evaluated at the Buonconsiglio Castle in Trento, Italy: (1) animated agents that help motivate visitors and focus their attention when necessary, (2) automatically generated, adaptive video documentaries on mobile devices, and (3) automatically generated post-visit summaries that reflect the individual interests of visitors as determined by their behavior and choices during their visit. These components are supported by underlying user modeling and inference mechanisms that allow for adaptivity and personalization. Novel software infrastructure allows for agent connectivity and fusion of multiple positioning data streams in the museum space. We conducted several experiments, focusing on various aspects of PEACH. In one, conducted 123 258 O. Stock et al.with 110 visitors, we found evidence that even older users are comfortable interacting with a major component of the system.

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The Development of the Attitudes toward Computer Usage Scale

Cited by 113 publications

References 6 publications

A contingency model of computer and Internet self-efficacy

A contingency model of computer and Internet self-efficacy

Computerizing the Mental Rotations Test: Are gender differences maintained?

Adaptive, intelligent presentation of information for the museum visitor in PEACH

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