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The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects: A Case Example of “QPress”

The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects: A Case Example of “QPress”

Jonathan Bishop
Copyright: © 2016 |Pages: 21
ISBN13: 9781466698406|ISBN10: 1466698403|EISBN13: 9781466698413
DOI: 10.4018/978-1-4666-9840-6.ch066
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MLA

Bishop, Jonathan. "The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects: A Case Example of “QPress”." Big Data: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 1452-1472. https://doi.org/10.4018/978-1-4666-9840-6.ch066

APA

Bishop, J. (2016). The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects: A Case Example of “QPress”. In I. Management Association (Ed.), Big Data: Concepts, Methodologies, Tools, and Applications (pp. 1452-1472). IGI Global. https://doi.org/10.4018/978-1-4666-9840-6.ch066

Chicago

Bishop, Jonathan. "The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects: A Case Example of “QPress”." In Big Data: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1452-1472. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9840-6.ch066

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Abstract

The current phenomenon of Big Data – the use of datasets that are too big for traditional business analysis tools used in industry – is driving a shift in how social and economic problems are understood and analysed. This chapter explores the role Big Data can play in analysing the effectiveness of crowd-funding projects, using the data from such a project, which aimed to fund the development of a software plug-in called ‘QPress'. Data analysed included the website metrics of impressions, clicks and average position, which were found to be significantly connected with geographical factors using an ANOVA. These were combined with other country data to perform t-tests in order to form a geo-demographic understanding of those who are displayed advertisements inviting participation in crowd-funding. The chapter concludes that there are a number of interacting variables and that for Big Data studies to be effective, their amalgamation with other data sources, including linked data, is essential to providing an overall picture of the social phenomenon being studied.

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