Visual decision support for business ecosystem analysis

被引:42
|
作者
Basole, Rahul C. [1 ,2 ]
Huhtamaki, Jukka [3 ]
Still, Kaisa [4 ]
Russell, Martha G. [5 ,6 ]
机构
[1] Georgia Inst Technol, Tennenbaum Inst, 85 Fifth St NW, Atlanta, GA 30332 USA
[2] Georgia Inst Technol, Sch Interact Comp, 85 Fifth St NW, Atlanta, GA 30332 USA
[3] Tampere Univ Technol, Dept Math, Tampere, Finland
[4] VTT Finland, Espoo, Finland
[5] Stanford Univ, mediaX, Stanford, CA 94305 USA
[6] Stanford Univ, H STAR, Stanford, CA 94305 USA
关键词
Information visualization; Decision support; Business ecosystem; Cognitive fit theory; Data complexity; COGNITIVE FIT; EVALUATING VISUALIZATIONS; PRESENTATION FORMATS; USER; REPRESENTATIONS; NETWORK; COMPLEXITY; SYSTEM; ONLINE; MODEL;
D O I
10.1016/j.eswa.2016.08.041
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:271 / 282
页数:12
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