Visual decision support for business ecosystem analysis

Rahul C. Basole (Corresponding Author), Jukka Huhtamäki (Corresponding Author), Kaisa Still (Corresponding Author), Martha G. Russell (Corresponding Author)

    Research output: Contribution to journalArticleScientificpeer-review

    57 Citations (Scopus)


    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.
    Original languageEnglish
    Pages (from-to)271-282
    JournalExpert Systems with Applications
    Publication statusPublished - 2016
    MoE publication typeA1 Journal article-refereed


    • information visualization
    • decision support
    • business ecosystem
    • cognitive fit theory
    • data complexity


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