Use of Future-Oriented Information in User-Centered Product Concept Ideation

Salovaara, Antti; Mannonen, Petri

doi:10.1007/11555261_58

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…The challenges for this step, for coming up with plausible or compelling visions, are well recognised in HCI research [55]. Numerous concept design and brainstorming methods have been developed for this, along with explicitly future-oriented approaches such as design fiction [39] and use of trends as inspirational springboards [59]. User-involving methods, in turn, include participatory design [62], co-design [29], acting out [50,54], and lead-user studies [35,67,68].…”

Section: Evaluation As a Time Machinementioning

confidence: 99%

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Evaluation of Prototypes and the Problem of Possible Futures

Salovaara

Oulasvirta

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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There is a blind spot in HCI's evaluation methodology: we rarely consider the implications of the fact that a prototype can never be fully evaluated in a study. A prototype under study exists firmly in the present world, in the circumstances created in the study, but its real context of use is a partially unknown future state of affairs. This present-future gap is implicit in any evaluation of prototypes, be they usability tests, controlled experiments, or field trials. A carelessly designed evaluation may inadvertently evaluate the wrong futures, contexts, or user groups, thereby leading to false conclusions and expensive design failures. The essay analyses evaluation methodology from this perspective, illuminating how to mitigate the present-future gap.

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Section: Evaluation As a Time Machinementioning

confidence: 99%

“…The forecasts are based on trends that have been cross-validated across multiple sources, to guard against overconfidence [60]. Instead of using trends only as an inspiration [59], taking an approach Near future Far future Figure 5. Levels of uncertainty involved with future scenarios.…”

Section: Defining Intended Futuresmentioning

confidence: 99%

Evaluation of Prototypes and the Problem of Possible Futures

Salovaara

Oulasvirta

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…User-centered concept development can be described as early phase exploratory process in product development aiming to create new product innovations [10]. The motivation for user-centeredness is to introduce information about real users to the product development cycle as early as possible.…”

Section: Characteristics Of Concept Developmentmentioning

confidence: 99%

Design Perspectives: Sampling User Research for Concept Development

Mannonen¹,

Nieminen²

2007

Engineering Psychology and Cognitive Ergonomics

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In user-centered design user research usually produces detailed description of the users, analysis of relevant actions and the specifics of the environment and artefacts thereof. However additional step of sampling from different viewpoints is required to more efficiently kick-start user-centered product concept development. Design perspectives, abstracted cross-category samples of user research results, are introduced as means to increase the usefulness of analyzed data during user-centered product concept development. Defining additional layer of entities in addition to conventional user research results helps to migrate from the pedant data-gathering phase to the more creative concept development phase. This paper describes definitions of entities involved in constructing design perspectives in a user research method independent manner with examples from a project developing new concepts for mobile and distributed team work.

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“…In addition to distance-based algorithms and text mining techniques presented in the previous paragraphs, trend-based idea elicitation could be applied. Besides, trends relevant to user-demanded products are applicable to generate ideas [22]. Yet, finding ideas from untraceable weak trends that could yield a competitive advantage is like finding a needle in a haystack [23].…”

Section: Introductionmentioning

confidence: 99%

Adapting CRISP-DM for Idea Mining

Ayele¹

2020

IJACSA

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Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate knowledge transfer, reuse of best practices, and minimize knowledge requirements. On the other hand, to unlock the potential of ever-growing textual data such as publications, patents, social media data, and documents of various forms, digital innovation is increasingly needed. Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. The processing of unstructured textual data to generate new and useful ideas is referred to as idea mining. Existing literature about idea mining merely overlooks the utilization of standard data mining process models. Therefore, the purpose of this paper is to propose a reusable model to generate ideas, CRISP-DM, for Idea Mining (CRISP-IM). The design and development of the CRISP-IM are done following the design science approach. The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. The adapted CRISP-IM can be used to guide the process of identifying trends using scholarly literature datasets or temporally organized patent or any other textual dataset of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left for future study.

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Use of Future-Oriented Information in User-Centered Product Concept Ideation

Cited by 11 publications

References 10 publications

Evaluation of Prototypes and the Problem of Possible Futures

Evaluation of Prototypes and the Problem of Possible Futures

Design Perspectives: Sampling User Research for Concept Development

Adapting CRISP-DM for Idea Mining

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