Eine empirische Studie zur Vertrauenswürdigkeit von Datenschutzrichtlinien österreichischer Online-Shops

Translated title of the contribution: An empirical study on trustworthiness of privacy policies of Austrian online shops

Julia Eder

Research output: ThesisMaster's / Diploma thesis

Abstract

Many companies collect a large amount of personal information about customers and that generates concerns about the protection of this personal information. The empirical study on trustworthiness of privacy policies of Austrian online shops should provide information on whether students have confidence in published data protection guidelines. The study was conducted to investigate whether a person's trust in a company is influenced by the information in a privacy policy, whether positive or negative. Factors, that could be stated in privacy policies and could affect the trustworthiness, were identified. Using the eight identified variables, a model was set up and a questionnaire was developed. These eight variables / factors are the data type of the collected data, the method of data collection, the mentioned security measures, the mention of data is passed on to third parties, the options for sending out advertising material (opt-out / opt-in), the mention of the data retention period, the comprehensibility or clarity of the Privacy Policy and the ease of the Privacy Policy. Students were questioned by means of a questionnaire or by internet-based survey (web survey). The results of the questionnaire were statistically analysed with the binomial logistic regression analysis. First, a reduced model was formed and this fitted model was compared with the complete model, by comparing the pseudo coefficient of determination according to McFadden and a subsequent likelihood-ratio test.
Translated title of the contributionAn empirical study on trustworthiness of privacy policies of Austrian online shops
Original languageGerman (Austria)
Supervisors/Reviewers
  • Schrefl, Michael, Supervisor
  • Schütz, Christoph Georg, Co-supervisor
Publication statusPublished - Jan 2019

Fields of science

  • 102 Computer Sciences
  • 102010 Database systems
  • 102015 Information systems
  • 102016 IT security
  • 102025 Distributed systems
  • 102027 Web engineering
  • 102028 Knowledge engineering
  • 102030 Semantic technologies
  • 102033 Data mining
  • 102035 Data science
  • 502050 Business informatics
  • 503008 E-learning

JKU Focus areas

  • Digital Transformation

Cite this