Unlocking the power of big data in new product development

Yuanzhu Zhan (), Kim Hua Tan (), Yina Li () and Ying Kei Tse ()
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Yuanzhu Zhan: Nottingham University Business School
Kim Hua Tan: Nottingham University Business School
Yina Li: South China University of Technology
Ying Kei Tse: University of York

Annals of Operations Research, 2018, vol. 270, issue 1, No 29, 577-595

Abstract: Abstract This study explores how big data can be used to enable customers to express unrecognised needs. By acquiring this information, managers can gain opportunities to develop customer-centred products. Big data can be defined as multimedia-rich and interactive low-cost information resulting from mass communication. It offers customers a better understanding of new products and provides new, simplified modes of large-scale interaction between customers and firms. Although previous studies have pointed out that firms can better understand customers’ preferences and needs by leveraging different types of available data, the situation is evolving, with increasing application of big data analytics for product development, operations and supply chain management. In order to utilise the customer information available from big data to a larger extent, managers need to identify how to establish a customer-involving environment that encourages customers to share their ideas with managers, contribute their know-how, fiddle around with new products, and express their actual preferences. We investigate a new product development project at an electronics company, STE, and describe how big data is used to connect to, interact with and involve customers in new product development in practice. Our findings reveal that big data can offer customer involvement so as to provide valuable input for developing new products. In this paper, we introduce a customer involvement approach as a new means of coming up with customer-centred new product development.

Keywords: Big data; Customer involvement; New product development; Case study (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (19)

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DOI: 10.1007/s10479-016-2379-x

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