Evaluating User eXperience as a Means to Reveal the Potential Adoption of Innovative Ideas

Pallot, Marc; Pawar, Kulwant S.; Krawczyk, Piotr; Topolewski, Marcin; Lecossier, Adrien; Razek, Abdul Rahman Abdel

doi:10.1109/ice/itmc49519.2020.9198368

Cited by 5 publications

(7 citation statements)

References 32 publications

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“…UX works to coordinate these elements to allow for the best possible interaction by users". The source [7] additionally mentions that "UX is multidimensional and multifaceted due to the many different types of experience, including social and empathic experiences, that users can live when using a product". Also, UX is closely related to UCD (User Experience Design) and Usability, which is defined by ISO/IEC 9241 as "the extent to which a system, product or service can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use".…”

Section: Background and Related Workmentioning

confidence: 99%

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Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems

Nikiforova

Zabiniako

Kornienko

et al. 2021

Applied Computer Systems

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Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and effic iency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define dependency between these data and desired output, which can help to apply Machine Learning / graph algorithms to user activity records.

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Section: Background and Related Workmentioning

confidence: 99%

“…In [7], "… the following facets are composed of the following UX properties: Economical facet: (i) Entertaining; (ii) Pleasantness; (iii) Productivity; (iv) Usefulness… ".…”

Section: Target Data Definition Based On Ux/cx Evaluation Heuristicsmentioning

confidence: 99%

Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems

Nikiforova

Zabiniako

Kornienko

et al. 2021

Applied Computer Systems

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show abstract

“…In 2009, the Living Lab approach was described as a kind of open innovation ecosystem where users/citizens co-create value [19]. User-Centered Design (UCD) methods [20,21] shifted the priority towards co-creating value with users in order to ensure a higher rate of technology acceptance and product/service adoption [22], which is the ultimate goal of innovation.…”

Section: A User Driven Innovationmentioning

confidence: 99%

“…However, it requires multiple stakeholders, especially users, to be engaged along the iterative XD process (Fig. 2) for: (1) co-creating innovative ideas; (2) exploring alternative usage scenarios; (3) experimenting how alternative solutions could properly support usage scenarios; (4) evaluating usage scenarios through UX quality measurement [6,21,22]. These four activities (Fig.…”

Section: Experience Designmentioning

confidence: 99%

“…These value elements are turned into UX properties and grouped within UX facets. [6,21,22] This brings us to the central premise and core rationale of our validated UX-based adoption model relying on the main assumption that the higher the measured score of each UX facet, the more likely users would adopt the corresponding innovative solution [22,35].…”

Section: Experience Designmentioning

confidence: 99%

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Investigating the Impact of Visual Representations during Ideation: Towards Immersive eXperience Design

Pallot

Dupont

Fleury

et al. 2021

2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)

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While there is a plethora of publications devoted to the benefits of visualization; its drawbacks, apparently, did not receive the same attention. Nonetheless, a list of potential drawbacks exists in the current body of knowledge, which, unfortunately, do not directly address the facilitation of ideas generation by different design stakeholders, including users. Beside introducing the longer-term goal of a series of planned studies, this paper presents a first empirical study for setting the baseground on immersiveness. The results show that the choice of a visual representation type must be made wisely as it has an impact on the way participants imagine a facility space and its furnishing layout. They also unveil that, for the same facility space, the layout suggested by the participants is likely to be different depending on whether they visualize a top, perspective or sectional view, or even an inspirational photo of the space.

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A fractional 0–1 decision making approach for process variant management

Schmidtner

Doering

Hilpoltsteiner

et al. 2021

2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)

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Evaluating User eXperience as a Means to Reveal the Potential Adoption of Innovative Ideas

Cited by 5 publications

References 32 publications

Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems

Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems

Investigating the Impact of Visual Representations during Ideation: Towards Immersive eXperience Design

A fractional 0–1 decision making approach for process variant management

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