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Visual decision support for business ecosystem analysis

Tutkimustuotosvertaisarvioitu

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

julkaisussa: Expert Systems with Applications, Vuosikerta 65, 15.12.2016, s. 271-282.

Tutkimustuotosvertaisarvioitu

Harvard

Basole, RC, Huhtamäki, J, Still, K & Russell, MG 2016, 'Visual decision support for business ecosystem analysis', Expert Systems with Applications, Vuosikerta. 65, Sivut 271-282. https://doi.org/10.1016/j.eswa.2016.08.041

APA

Basole, R. C., Huhtamäki, J., Still, K., & Russell, M. G. (2016). Visual decision support for business ecosystem analysis. Expert Systems with Applications, 65, 271-282. https://doi.org/10.1016/j.eswa.2016.08.041

Vancouver

Basole RC, Huhtamäki J, Still K, Russell MG. Visual decision support for business ecosystem analysis. Expert Systems with Applications. 2016 joulu 15;65:271-282. https://doi.org/10.1016/j.eswa.2016.08.041

Author

Basole, Rahul C. ; Huhtamäki, Jukka ; Still, Kaisa ; Russell, Martha G. / Visual decision support for business ecosystem analysis. Julkaisussa: Expert Systems with Applications. 2016 ; Vuosikerta 65. Sivut 271-282.

Bibtex - Lataa

@article{a2bf36c0563943749557e928b08acb35,
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 = "Business ecosystem, Cognitive fit theory, Data complexity, Decision support, Information visualization",
author = "Basole, {Rahul C.} and Jukka Huhtam{\"a}ki and Kaisa Still and Russell, {Martha G.}",
year = "2016",
month = "12",
day = "15",
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",

}

RIS (suitable for import to EndNote) - Lataa

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 - 2016/12/15

Y1 - 2016/12/15

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 - Business ecosystem

KW - Cognitive fit theory

KW - Data complexity

KW - Decision support

KW - Information visualization

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 -