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

25 Citations (Scopus)

Abstract

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
Volume65
DOIs
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed

Fingerprint

Ecosystems
Industry
Visualization
Decision making

Keywords

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

Cite this

Basole, Rahul C. ; Huhtamäki, Jukka ; Still, Kaisa ; Russell, Martha G. / Visual decision support for business ecosystem analysis. In: Expert Systems with Applications. 2016 ; Vol. 65. pp. 271-282.
@article{06073fec4ea74a72855e77364447c883,
title = "Visual decision support for business ecosystem analysis",
abstract = "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.",
keywords = "information visualization, decision support, business ecosystem, cognitive fit theory, data complexity",
author = "Basole, {Rahul C.} and Jukka Huhtam{\"a}ki and Kaisa Still and Russell, {Martha G.}",
year = "2016",
doi = "10.1016/j.eswa.2016.08.041",
language = "English",
volume = "65",
pages = "271--282",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier",

}

Visual decision support for business ecosystem analysis. / Basole, Rahul C. (Corresponding Author); Huhtamäki, Jukka (Corresponding Author); Still, Kaisa (Corresponding Author); Russell, Martha G. (Corresponding Author).

In: Expert Systems with Applications, Vol. 65, 2016, p. 271-282.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Visual decision support for business ecosystem analysis

AU - Basole, Rahul C.

AU - Huhtamäki, Jukka

AU - Still, Kaisa

AU - Russell, Martha G.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - information visualization

KW - decision support

KW - business ecosystem

KW - cognitive fit theory

KW - data complexity

U2 - 10.1016/j.eswa.2016.08.041

DO - 10.1016/j.eswa.2016.08.041

M3 - Article

VL - 65

SP - 271

EP - 282

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -