Paper Number
ECIS2026-2873
Paper Type
CRP
Abstract
As firms seek to leverage technological innovations and data to improve new product development (NPD), Data-Driven Product Design (DDPD) is gaining importance. Yet, little is known about what drives its adoption. Using the technology–organization–environment (TOE) framework, this study performs a systematic literature review to identify the technological, organizational, and environmental factors that drive DDPD adoption. Using PLS-SEM, the antecedents are validated in a cross-industry survey of German companies. The findings indicate that organizational governance and human capabilities are more closely linked to DDPD than technological factors such as data quality and infrastructure for analytics. Results further show that DDPD is positively associated with NPD performance. The quantitative operationalization of DDPD provides an instrument shifting the focus from technology to organizational levers. For practitioners, findings suggest that progressing toward DDPD hinges less on analytics tools and more on fostering data proficiency, implementing robust data governance, and showcasing case studies.
Recommended Citation
Schober, Dominic, "Exploring The Adoption Of Data-Driven Product Design: Identification and Quantitative Validation Of Antecedents" (2026). ECIS 2026 Proceedings. 15.
https://aisel.aisnet.org/ecis2026/bus_analytics/bus_analytics/15
Exploring The Adoption Of Data-Driven Product Design: Identification and Quantitative Validation Of Antecedents
As firms seek to leverage technological innovations and data to improve new product development (NPD), Data-Driven Product Design (DDPD) is gaining importance. Yet, little is known about what drives its adoption. Using the technology–organization–environment (TOE) framework, this study performs a systematic literature review to identify the technological, organizational, and environmental factors that drive DDPD adoption. Using PLS-SEM, the antecedents are validated in a cross-industry survey of German companies. The findings indicate that organizational governance and human capabilities are more closely linked to DDPD than technological factors such as data quality and infrastructure for analytics. Results further show that DDPD is positively associated with NPD performance. The quantitative operationalization of DDPD provides an instrument shifting the focus from technology to organizational levers. For practitioners, findings suggest that progressing toward DDPD hinges less on analytics tools and more on fostering data proficiency, implementing robust data governance, and showcasing case studies.
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