An Exploratory Examination of Antecedents to Software Piracy: A Cross-Cultural Comparison

Setterstrom, Andrew J.; Aleassa, Hasan M.

doi:10.1109/hicss.2012.100

Cited by 5 publications

(2 citation statements)

References 40 publications

(50 reference statements)

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“…This is important because the psychometric properties from the samples must be demonstrated to have the same structure to establish that the groups had similar interpretations of our instrument's items. Failure to establish measurement invariance would suggest that we measured different phenomena across the groups, therefore making comparison between groups meaningless [35]. To assess measurement invariance, we used the component-based CFA in SmartPLS 2 [32] to conduct factor analysis for each group of data and retained items that had factor loadings of at least .5 [18] in all the groups (and dropped for all groups items with loadings less than .5), thereby establishing configural invariance.…”

Section: Multi-group Comparisonmentioning

confidence: 99%

Tailoring persuasive health games to gamer type

Orji

Mandryk

Vassileva

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

184

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Persuasive games are an effective approach for motivating health behavior, and recent years have seen an increase in games designed for changing human behaviors or attitudes. However, these games are limited in two major ways: first, they are not based on theories of what motivates healthy behavior change. This makes it difficult to evaluate why a persuasive approach works. Second, most persuasive games treat players as a monolithic group. As an attempt to resolve these weaknesses, we conducted a large-scale survey of 642 gamers' eating habits and their associated determinants of healthy behavior to understand how health behavior relates to gamer type. We developed seven different models of healthy eating behavior for the gamer types identified by BrainHex. We then explored the differences between the models and created two approaches for effective persuasive game design based on our results. The first is a one-sizefits-all approach that will motivate the majority of the population, while not demotivating any players. The second is a personalized approach that will best motivate a particular type of gamer. Finally, to make our approaches actionable in persuasive game design, we map common game mechanics to the determinants of healthy behavior.

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Section: Multi-group Comparisonmentioning

confidence: 99%

Tailoring persuasive health games to gamer type

Orji

Mandryk

Vassileva

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

184

View full text Add to dashboard Cite

show abstract

“…Using the criteria from Chin (1998) and Fornell and Larcker (1981), indicator reliability can be assumed because Cronbach's  and the composite reliability that analyzes the strength of each indicator's correlation with their variables are all higher than the threshold value of 0.7. Convergent and discriminate validity can be assumed as all constructs have an Average Variance Extracted (AVE) (which represents the variance extracted by the variables from its indicator items) above the recommended threshold of 0.5 and greater than the variance shared with other variables (Setterstrom et al, 2012). The measurement models yielded an acceptable value of all indices for PLS model validity and reliability.…”

Section: The Measurement Modelmentioning

confidence: 99%

Factors Influencing User�s Attitude to Secondary Information Sharing and Usage

Iyilade

Orji

Vassileva

2015

CIT

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The increasing availability of enormous data about users online, along with availability of sophisticated tools and technology to store, aggregate, and analyze data for secondary use has raised concerns about how to balance the opportunity for secondary use of data with the need to protect the user privacy that may result from harmful use. To develop a privacy protection mechanism that is useful and meets the expectations and needs of the user, it is important to understand user's attitude to privacy and secondary information sharing and usage of his/her data. While several studies have investigated factors influencing user's attitude to privacy in primary data collection context, none of the existing studies have provided an understanding of user perception and attitude to privacy in secondary context. To fill this gap, this work has identified five factors that are important in a secondary usage context and carried out a study on their influence on user's perception with respect to how their data is shared for secondary use. The main contribution of this paper is an understanding of factors influencing user decisions about privacy in secondary context, which can assist both technology designers and policy makers in the development of appropriate privacy protection that meets the needs and expectations of the user.

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