Using Traditional Research Methods in Contemporary UX Surveying: Case Studies Applying Mental Model Research, Participant Observation, Projective Techniques

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Abstract

In my paper I write about three methods which are generally used and accepted in social sciences and market research, and can be effectively applied – not in a standard way, but innovatively – during the development of online interfaces, too. At the beginning of the paper I present how user interface research has become an indispensable component of web development, considering popular demand for ergonomic, user-friendly web pages and applications. The topic is also relevant from a business point of view, considering how web developments involving users prove to yield superior results. There are several methods available for user surveys and researching. The appropriate choices and following through with the research (methodology, detail, depth) are subject to available funding and professional standards. There are many ways to do research, and even the most rudimentary research or survey always produces results. In the following paper after a brief methodology overview (qualitative – quantitative, attitude – behavior, context and history, graphs and charts baser on C. Rohrer and McCrindle Research), I will go on to describe some of the common and lesser-used methods in UX research, even some which are not included in the chart but useful nonetheless. The latter account for innovative methods. It is these methodologies my study focuses on: participant observation, mental model research and various projective tests. For each method I describe its (standard) offline and its (innovative) online applications, and drawing on my own practice, I also describe their safe and reliable application to online surfaces. I present each method through case studies.

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Herendy, C. (2020). Using Traditional Research Methods in Contemporary UX Surveying: Case Studies Applying Mental Model Research, Participant Observation, Projective Techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12423 LNCS, pp. 123–132). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60114-0_8

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