Driving Factors of Generative AI Adoption in New Product Development Teams from a UTAUT Perspective

Abstract

Recent new product development (NPD) teams apply various generative AI (GenAI) tools in the development process, yet it is not fully understood about the factors affecting teams’ adoption of these tools. This research identifies factors driving the use and attitudes toward GenAI in NPD tasks based on the Unified Theory of Acceptance and Use of Technology (UTAUT). We interviewed nine GenAI users in NPD teams and conducted a survey study with 309 participants. By exploratory factor analysis and hierarchical regressions, we identified a composite factor of performance expectancy and anthropomorphism as the strongest positive predictor of attitudes, and task-tool fitness as the strongest positive predictor of behavioral intention. Besides, we also identified significant predictors including several other factors in UTAUT and individual differences in AI self-efficacy. The findings can be used for developing UTAUT models and designing GenAI tools specific to NPD purposes.

Keywords:

• New product design
• generative AI
• technology acceptance
• UTAUT

Ethics statement

The Central Government of the People’s Republic of China has issued a Circular on the Measures for Ethical Review, which covers the scope of ethical review that is not involved in our manuscript.

(https://www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm)

Align with this circular, the authors’ institution (East China University of Science and Technology) only offers ethical reviews for physiological experiments (e.g., eye tracking and EEG experiments), and does not offer ethical reviews for questionnaires and interviews in this article. Therefore, our study was not ethically involved based on university requirements and did not require ethical approval.

The subject informed consent form (translated from Chinese into English) used in the survey study is attached below.

Informed Consent Form

We appreciate your participation in this questionnaire survey. Before you decide whether to participate in this study, please read the following carefully.

This survey will be used for academic research to identify factors driving the use and attitudes toward generative AI tools in new product development tasks. Your answers will provide essential data for this study and contribute to the future design of generative AI tools.

The survey will be anonymous and have no impact or risk on your daily life. Your personal information for participating in this study will be kept strictly confidential. Any information that could reveal your identity will not be disclosed to members outside the study group.

In addition, upon completion of the study, you can enter a draw for a chance to win cash prizes in return if you are interested.

This survey is voluntary. If you have decided to agree to participate in this study and pledge to follow the study procedures as closely as possible, please continue. You can exit the survey at any time.

(Note: Since we collect data on the WJX Survey Platform, we were incapable to collect the participants’ signature.)

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [Grant Number 72301104].

Notes on contributors

Yan Xia

Yan Xia is a Master’s student in Design Science at the East China University of Science and Technology (ECUST). She received her B.A. degree in Visual Communication Design from ECUST in 2022. She is interested in workplace communication and has presented her work at the HCI International Conference.

Yue Chen

Yue Chen is an assistant professor at the School of Art Design and Media, East China University of Science and Technology. She received her B.S. and Ph.D. degrees from Tsinghua University and also worked there as a postdoctoral researcher. Her research focuses on human factors in communication technologies.