Abstract
Mobile penetration rate is high, so m-government services have a huge potential to spread. Our research aims to investigate how elderly users profit from m-government. Europe is now and will be in 2060 the oldest continent in the world, so elderly is an important target group for future m-government services. There are several m-government initiatives and projects offering various government services, like information sharing, alerting and mHealth services, which provide mobile remote patient monitoring in order to measure vital signs, bio-signals of patients outside hospital environments. Such systems can strongly benefit from improved acceptance by elderly users; therefore, investigation of social and psychological aspects of mobile adoption is an emerging field of research. The goal of our research is to find a way to map the needs of the elderly and set guidelines for the design of m-government systems. We apply IGUAN framework [11] for usability investigation, which we developed, in our earlier research as a guideline for improving the usability of e-government systems. We constructed a scenario in usability investigation, a search for medical treatment. Our research follows a user-driven method and uses the data acquired on usability of m-government by the elderly from Germany and Hungary. The main contribution of this paper is the assessment of the requirements for m-government systems’ development for elderly, by investigating the factors, which lead to a low acceptance of m-government by this group.
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Acknowledgement
“This paper has been written with the support of the National University of Public Service in the framework of the priority project KÖFOP-2.1.2-VEKOP-15-2016-00001 titled “Public Service Development for Establishing Good Governance” - Ludovika Digital Governance Research Group”.
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Molnár, T., Kő, A., Mátyus, B. (2017). Exploring Usability and Acceptance Factors of m-Government Systems for Elderly. In: Kő, A., Francesconi, E. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2017. Lecture Notes in Computer Science(), vol 10441. Springer, Cham. https://doi.org/10.1007/978-3-319-64248-2_13
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DOI: https://doi.org/10.1007/978-3-319-64248-2_13
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