Visual decision support for business ecosystem analysis
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||12|
|Journal||Expert Systems with Applications|
|Publication status||Published - 15 Dec 2016|
|Publication type||A1 Journal article-refereed|
This study comparatively evaluates the effectiveness of three visualization methods (list, matrix, network) and the influence of data complexity, task type, and user characteristics on decision performance in the context of business ecosystem analysis. We pursue this objective using an exploratory study with 14 prototypical users (e.g. executives, analysts, investors, and policy makers). The results show that in low complexity contexts, decision performance between visual representations differ but not substantially. In high complexity contexts, however, decision performance suffers significantly if visual representations are not appropriately matched to task types. Our study makes several theoretical and practical contributions. Theoretically, we extend cognitive fit theory by investigating the impact of business ecosystem task type and complexity. Managerially, our study contributes to the relatively underexplored, but emerging area of the design of business ecosystem intelligence tools and presentation of business ecosystem data for the purpose of decision making. We conclude with future research opportunities.
- Business ecosystem, Cognitive fit theory, Data complexity, Decision support, Information visualization