Big Data Analysis as a New Approach for Usability Attributes Evaluation of User Interfaces: An Automotive Industry Context

Orlovska, Julia; Wickman, Casper; Söderberg, Rikard

doi:10.21278/idc.2018.0243

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…All automotive domain experts in our study agree that there is a lack of implicit feedback through field user interaction data in UX development. These findings coincide with the work of Orlovska et al [36,37]. Additionally, our results support the disparity presented in Section 2.3 that in comparison to the automotive domain, digital domains are far ahead in exploring the potentials of field user interaction data.…”

Section: Discussionsupporting

confidence: 93%

“…This allows considering the complex interactions between driver, car, and environment without the costs and bias introduced by simulator experiments. This is in line with the findings made in earlier work on this topic [36,37]. However, to provide the biggest possible value for practitioners, all automated methods should provide an explanatory component and be triangulated with qualitative user feedback.…”

Section: Differences To Digital Companies (Rq4)supporting

confidence: 85%

“…They also found that software platforms of Original Equipment Manufacturers (OEMs) are not designed for user behavior logging and that methods need to be developed that enable compliance with data protection regulations. Additionally, they present a case study [36] on how field user interaction data can benefit the overall usability assessment. However, this study exclusively deals with a specific system of a single manufacturer.…”

Section: Related Workmentioning

confidence: 99%

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The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective

Ebel

Brokhausen

Vogelsang

2020

12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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We are interested in the role of field user interaction data in the development of In-Vehicle Information Systems (IVISs), the potentials practitioners see in analyzing this data, the concerns they share, and how this compares to companies with digital products. We conducted interviews with 14 UX professionals, 8 from automotive and 6 from digital companies, and analyzed the results by emergent thematic coding. Our key findings indicate that implicit feedback through field user interaction data is currently not evident in the automotive UX development process. Most decisions regarding the design of IVISs are made based on personal preferences and the intuitions of stakeholders. However, the interviewees also indicated that user interaction data has the potential to lower the influence of guesswork and assumptions in the UX design process and can help to make the UX development lifecycle more evidence-based and user-centered. CCS CONCEPTS • General and reference → Surveys and overviews; • Humancentered computing → HCI design and evaluation methods; Empirical studies in HCI.

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Section: Discussionsupporting

confidence: 93%

Section: Differences To Digital Companies (Rq4)supporting

confidence: 85%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective

Ebel

Brokhausen

Vogelsang

2020

12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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“…The correct selection of variables and attributes for the analysis is fundamental in using the data. In this case, the classification of attributes proposed by Orlovska et al (2018) is expected to analyse the attributes and divide them into three main groups: User performance, system performance, and user perception. The classification of the attributes is shown in Figure 4.…”

Section: Data-driven Design Model Application Framework For Evaluatinmentioning

confidence: 99%

Creativity Forward: A Framework That Integrates Data Analysis Techniques to Foster Creativity Within the Creative Process in User Experience Contexts

Gómez

2021

Creativity Studies

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The latest technological advancements allow users to generate a large volume of data related to their experiences and needs. However, the absence of an advanced methodology that links the big data and the creative process prevents the effective use of the data and extracting all its potential and knowledge in this context, which is crucial in offering user-centred solutions. Incorporating data creatively and critically as design material can help us learn and understand user needs better. Therefore, design can bring deeper meaning to data, just as data can enhance design practice. Accordingly, this work raises a reflection on whether designers could appropriate the workflow of data science in order to integrate it into the research process in the creative process within a framework of user experience analysis. The proposed model: data-driven design model, enhances the exploratory design of problem space and assists in the creation of ideas during the conceptual design phase. In this way, this work offers an integrated vision, enhancing creativity in industrial design as an instrument for the achievement of the proper and necessary balance between intuition and reason, design, and science.

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“…interpreting the phenomena based on the meaning that a particular user or a user group brings to them. Qualitative methods usually focus on gathering subjective impressions regarding the system usage, rather than targeting specific user tasks or identifying the variables that cause specific user behavior (Orlovska et al, 2018). Qualitative approaches have a well-established methodology, based on usability evaluation methods (UEMs).…”

Section: Iced19mentioning

confidence: 99%

Mixed-Method Design for User Behavior Evaluation of Automated Driver Assistance Systems: An Automotive Industry Case

Orlovska

Novakazi

Wickman

et al. 2019

Proc. Int. Conf. Eng. Des.

Self Cite

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Automotive systems are changing rapidly from purely mechanical to smart, programmable assistants. These systems react and respond to the driving environment and communicate with other subsystems for better driver support and safety. However, instead of supporting, the complexity of such systems can result in a stressful experience for the driver, adding to the workload. Hence, a poorly designed system, from a usability and user experience perspective, can lead to reduced usage or even ignorance of the provided functionalities, especially concerning Adaptive Driver Assistance Systems.In this paper, the authors propose a combined design approach for user behavior evaluation of such systems. At the core of the design is a mixed methods approach, where objective data, which is automatically collected in vehicles, is augmented with subjective data, which is gathered through in- depth interviews with end-users. The aim of the proposed methodology design is to improve current practices on user behavior evaluation, achieve a deeper understanding of driver's behavior, and improve the validity and rigor of the named results.

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Big Data Analysis as a New Approach for Usability Attributes Evaluation of User Interfaces: An Automotive Industry Context

Cited by 6 publications

References 16 publications

The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective

The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective

Creativity Forward: A Framework That Integrates Data Analysis Techniques to Foster Creativity Within the Creative Process in User Experience Contexts

Mixed-Method Design for User Behavior Evaluation of Automated Driver Assistance Systems: An Automotive Industry Case

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