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User Adoption of Big Data Analyticsin the Public Sector
Linnaeus University, Faculty of Technology, Department of Informatics.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The goal of this thesis was to investigate the factors that influence the adoption of big data analytics by public sector employees based on the adapted Unified Theory of Acceptance and Use of Technology (UTAUT) model. A mixed method of survey and interviews were used to collect data from employees of a Canadian provincial government ministry. The results show that performance expectancy and facilitating conditions have significant positive effects on the adoption intention of big data analytics, while effort expectancy has a significant negative effect on the adoption intention of big data analytics. The result shows that social influence does not have a significant effect on adoption intention. In terms of moderating variables, the results show that gender moderates the effects of effort expectancy, social influence and facilitating condition; data experience moderates the effects of performance expectancy, effort expectancy and facilitating condition; and leadership moderates the effect of social influence. The moderation effects of age on performance expectancy, effort expectancy is significant for only employees in the 40 to 49 age group while the moderation effects of age on social influence is significant for employees that are 40 years and more. Based on the results, implications for public sector organizations planning to implement big data analytics were discussed and suggestions for further research were made. This research contributes to existing studies on the user adoption of big data analytics. 

Place, publisher, year, edition, pages
2019. , p. 61
Keywords [en]
adoption intention, big data, big data analytics, Cronbach’s alpha, probit regression, public sector, public policy, statistical significance, unified theory of acceptance, use of technology (UTAUT)
National Category
Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-86641OAI: oai:DiVA.org:lnu-86641DiVA, id: diva2:1337189
Educational program
Master Programme in Information Systems, 60 credits
Examiners
Available from: 2019-08-01 Created: 2019-07-12 Last updated: 2020-06-01Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf