Generative AI, Innovation, and Trust

Our paper explores the integration of generative artificial intelligence (GenAI) into organizations’ innovation and new product development processes, focusing on when and how to trust AI-generated outcomes in this context. We propose a framework to assess the level of trust required based on task-specific needs and the distinction between general and expert AI models. While inaccuracies in GenAI outputs can foster creativity during ideation, higher accuracy, and trust are essential for tasks requiring domain-specific expertise. The paper concludes by discussing the necessary human capabilities and organizational strategies for effectively deploying GenAI in innovation management.

Authors

Institutions

Publication Details

Journal
The Journal of Applied Behavioral Science
Published
2024-09-30
DOI
https://doi.org/10.1177/00218863241285033
Citations
20
Primary Topic
Innovation, Sustainability, Human-Machine Systems
Type
article
Field-Weighted Citation Impact
22.64

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generative AI, Innovation, and Trust

Frank Thomas Piller, Tucker J. Marion, Mahdi Srour
20 citations
The Journal of Applied Behavioral Science
Innovation, Sustainability, Human-Machine Systems
22.64
article

Generative AI, Innovation, and Trust

Frank Thomas Piller, Tucker J. Marion, Mahdi Srour
article en
20 citations

Abstract

Our paper explores the integration of generative artificial intelligence (GenAI) into organizations’ innovation and new product development processes, focusing on when and how to trust AI-generated outcomes in this context. We propose a framework to assess the level of trust required based on task-specific needs and the distinction between general and expert AI models. While inaccuracies in GenAI outputs can foster creativity during ideation, higher accuracy, and trust are essential for tasks requiring domain-specific expertise. The paper concludes by discussing the necessary human capabilities and organizational strategies for effectively deploying GenAI in innovation management.

The Journal of Applied Behavioral ScienceVol. 60(4)
Northeastern University (US), RWTH Aachen University (DE)
Deutsche Forschungsgemeinschaft, Directorate for Engineering
Openalex Percentile: Top 1%
Innovation, Sustainability, Human-Machine Systems
22.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.