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14 pages, 740 KB  
Article
Towards an Integrated Maturity Model of System and E-Business Applications in an Emerging Economy
by Alejandro Cataldo, César A. Astudillo, Jimmy H. Gutiérrez-Bahamondes, Luis González-Martínez and Robert McQueen
J. Theor. Appl. Electron. Commer. Res. 2020, 15(2), 1-14; https://doi.org/10.4067/S0718-18762020000200102 - 1 May 2020
Cited by 9 | Viewed by 1574
Abstract
Although there is a great number of maturity models proposed for Information Systems, most of them have three limitations: (1) they are focused on a single or small subset of companies; (2) they do not address the evolution of enterprise systems and e-business [...] Read more.
Although there is a great number of maturity models proposed for Information Systems, most of them have three limitations: (1) they are focused on a single or small subset of companies; (2) they do not address the evolution of enterprise systems and e-business applications, simultaneously; (3) they are only focused on developed countries and do not consider emerging economies. We developed a maturity model of Information Systems that addresses these limitations through a data mining approach. The results showed that clustering analysis was an effective method for discovering similar groups of companies according to the set of enterprise systems and e-business applications that they adopted. Two major conclusions can be outlined: Unlike previous models, it has been shown that companies can be grouped only in three stages of maturity. Furthermore, the evolutionary pattern of systems adopted by companies follows a path oriented to obtain greater efficiencies at the expense of those that strengthen the relationship with customers. The results are relevant to practitioners, researchers and policy makers in emerging economies. Full article
17 pages, 14743 KB  
Article
Visual Multi-Metric Grouping of Eye-Tracking Data
by Ayush Kumar, Rudolf Netzel, Michael Burch, Daniel Weiskopf and Klaus Mueller
J. Eye Mov. Res. 2017, 10(5), 1-17; https://doi.org/10.16910/jemr.10.5.10 - 14 Feb 2018
Cited by 14 | Viewed by 280
Abstract
We present an algorithmic and visual grouping of participants and eye-tracking metrics derived from recorded eye-tracking data. Our method utilizes two well-established visualization concepts. First, parallel coordinates are used to provide an overview of the used metrics, their interactions, and similarities, which helps [...] Read more.
We present an algorithmic and visual grouping of participants and eye-tracking metrics derived from recorded eye-tracking data. Our method utilizes two well-established visualization concepts. First, parallel coordinates are used to provide an overview of the used metrics, their interactions, and similarities, which helps select suitable metrics that describe characteristics of the eye-tracking data. Furthermore, parallel coordinates plots enable an analyst to test the effects of creating a combination of a subset of metrics resulting in a newly derived eye-tracking metric. Second, a similarity matrix visualization is used to visually represent the affine combination of metrics utilizing an algorithmic grouping of subjects that leads to distinct visual groups of similar behavior. To keep the diagrams of the matrix visualization simple and understandable, we visually encode our eyetracking data into the cells of a similarity matrix of participants. The algorithmic grouping is performed with a clustering based on the affine combination of metrics, which is also the basis for the similarity value computation of the similarity matrix. To illustrate the usefulness of our visualization, we applied it to an eye-tracking data set involving the reading behavior of metro maps of up to 40 participants. Finally, we discuss limitations and scalability issues of the approach focusing on visual and perceptual issues. Full article
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