ITM Web Conf.
Volume 41, 2022International Conference on Exploring Service Science (IESS 2.2)
|Number of page(s)||14|
|Published online||08 February 2022|
Design science masterplan in improving online investing decision support services
Faculty of Business and Economics, University of Lausanne, CH-1015 Lausanne, Switzerland
* Corresponding author: firstname.lastname@example.org
Online services supporting consumer investing have proliferated during the last three decades. Empirical research has discovered that overperforming investors started to underperform after switching to online investing services. Recent studies have revealed that individuals ignore the decision support provided by incumbent investing platforms, relying on their instincts instead. In response, a research project was launched, with the main goal to define technology, acceptance and service ecosystem originated reasons for not meeting investor expectations. A 'design science master plan' process was developed to identify and filter critical developments from a large number of suggested improvements. The ultimate goal was to enable a self-directed investor to manage investing assets with a performance of a professional, but with better control than delegated or fully automated investing services allow. A sample of live online investing platforms was used for problem discovery. In remote-controlled experiments, ten investors representing five investor personas evaluated the selected services. Identified problems and opportunities were analysed and conceptualised, after which design evaluations were made. Participants prioritised the defined development initiatives in terms of relevance and expected personal acceptance. A suggestion for more modular and specialised service architecture was made.
Key words: Design science research / online investing services / service modularisation / decision support systems / retail investor
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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