Reduce Walking and Texting Accidents

A derivative one-line ideation brief from Goucher-Lambert and Cagan (2019), adapted from the longer campus pedestrian-safety brief used by Miller, Bailey, and Kirlik (2014).

See Text Problem Catalog for the text family index.

Quick Facts

Field	Value
Problem ID	`ideation_walking_texting_accident_reduction`
Problem Family	text
Implementation	`TextProblem`
Capabilities	`citation-backed`, `prompt-packet`, `statement-markdown`
Study Suitability	`human-subjects-ready`, `ideation-friendly`
Tags	`text`, `human-subjects`, `ideation`, `safety`, `consumer`

Taxonomy

Formulation: textual_prompt
Is Dynamic: no
Orientation: safety-design
Objective Mode: qualitative
Constraint Nature: open
Tags: text, human-subjects, ideation, safety, consumer
Deliverable Type: concepts
Timebox Hint (Minutes): 20
Participants: individual
Evaluation Mode: idea_generation

Statement

A way to minimize accidents from people walking and texting on a cell phone.

Prompt Profile

Field	Value
Deliverable Type	concepts
Timebox Hint (Minutes)	20
Participants	individual
Evaluation Mode	idea_generation

Sources

Key	Summary
`goucher_lambert_cagan_2019`	Goucher-Lambert and Cagan (2019). Crowdsourcing inspiration: Using crowd generated inspirational stimuli to support designer ideation. Design Studies, 61, 1-29.
`miller_bailey_kirlik_2014`	Miller, Bailey, and Kirlik (2014). Exploring the Utility of Bayesian Truth Serum for Assessing Design Knowledge. Human-Computer Interaction, 29(5-6), 487-515.

Raw Citation Records

@article{GOUCHERLAMBERT20191,
title = {Crowdsourcing inspiration: Using crowd generated inspirational stimuli to support designer ideation},
journal = {Design Studies},
volume = {61},
pages = {1-29},
year = {2019},
issn = {0142-694X},
doi = {https://doi.org/10.1016/j.destud.2019.01.001},
url = {https://www.sciencedirect.com/science/article/pii/S0142694X19300018},
author = {Kosa Goucher-Lambert and Jonathan Cagan}
}

Miller, Scarlett R., Brian P. Bailey, and Alex Kirlik (2014). Exploring the Utility of Bayesian Truth Serum for Assessing Design Knowledge. Human-Computer Interaction, 29(5-6), 487-515. DOI: 10.1080/07370024.2013.870393.