Paper Type

Complete

Paper Number

PACIS2025-1309

Description

Lead users (LUs) identify emerging needs before the market and actively drive innovation. While LU theory has been widely validated in offline industries, Digital Lead Users (DLUs) have become key drivers in online ecosystems. However, existing research primarily applies LU theory with selective digital characteristics, lacking a comprehensive framework for DLUs' unique behaviors. To address this gap, this study conducts a systematic literature review to examine the characteristics of DLUs and the methods used to identify them. We propose the TIS framework, which conceptualizes DLUs through Technological Embeddedness (T), Individual Innovativeness (I), and Social Networkability (S), exploring their dynamic interactions. Additionally, we compare traditional and data-driven identification methods across different digital contexts, evaluating their strengths and limitations. By providing a structured framework for understanding and identifying DLUs, this study advances theoretical understanding and provides practical insights for companies seeking to engage DLUs in digital innovation and product development.

Comments

Innovation

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COinS
 
Jul 6th, 12:00 AM

Identifying Digital Lead Users: A Systematic Review and Theoretical Framework

Lead users (LUs) identify emerging needs before the market and actively drive innovation. While LU theory has been widely validated in offline industries, Digital Lead Users (DLUs) have become key drivers in online ecosystems. However, existing research primarily applies LU theory with selective digital characteristics, lacking a comprehensive framework for DLUs' unique behaviors. To address this gap, this study conducts a systematic literature review to examine the characteristics of DLUs and the methods used to identify them. We propose the TIS framework, which conceptualizes DLUs through Technological Embeddedness (T), Individual Innovativeness (I), and Social Networkability (S), exploring their dynamic interactions. Additionally, we compare traditional and data-driven identification methods across different digital contexts, evaluating their strengths and limitations. By providing a structured framework for understanding and identifying DLUs, this study advances theoretical understanding and provides practical insights for companies seeking to engage DLUs in digital innovation and product development.