HOW TO MANAGE NEW PRODUCT DEVELOPMENT IN A BIG DATA ENVIRONMENT?

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Should the agile approach or the stage-gate approach be used to manage the new product development process in a big data environment? Although the stage-gate approach has been the dominant approach to managing new product development, the agile approach literature suggests that the stage-gate approach experiences difficulties when dealing with response uncertainty. The agile approach facilitates information processing for customer feedback by developing and deploying minimum viable products. Conflicting views on the effectiveness of these two approaches centre on their capabilities of dealing with uncertainty through information processing. The diffusion of digital technologies offers new ways to process information, but not many studies have investigated how the effectiveness of the agile and stage-gate approaches varies with different information-processing capabilities in a data-rich environment. This study categorises information-processing strategies into those that use internal or external big data and explores how these strategies interact with the stage-gate and agile approaches. To verify the research framework, we conducted an on-site survey to collect data. The sample covers 261 new product development projects. We adopted a multivariable linear regression model for the analysis. The regression results show that the agile approach should be integrated with internal rather than external big data utilisation and that the stage-gate approach should be integrated with external rather than internal big data utilisation. This finding deepens our understanding of innovation process management by exploring the integrating effects of the two approaches with the big data utilisation strategy.