Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.791433
Title: A framework for accelerated product innovation in a big data environment
Author: Zhan, Yuanzhu
ISNI:       0000 0004 8502 2646
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2017
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Abstract:
This dissertation is concerned with the best approaches for accelerated product innovation in a big data environment. It describes the development and examining of a framework consisting of three sets of different phases to support managers to attain accelerated product innovation in high-tech industries. This research also investigates the roles of big data in facilitating new product development, and the factors for successful implementation of big data. Accelerated product innovation has become increasingly important for both theory and practice in today's rapidly changing business environment. The phenomenon is reinforced by the increasing amounts of data available to business and the associated big data efforts in innovation by new information and communication technologies, as well as by new business models and organisational forms. There are two important issues associated with accelerated product innovation. Firstly, there is an underlying question as to which specific approaches for accelerated product innovation will be successful for a particular company. That is, even as more and more firms begin to acknowledge the significance of accelerated product innovation, they still suffer from a lack of knowledge about how to attain it. Secondly, how do companies apply big data to support accelerated product innovation in new product development? The specific benefits of accelerated product innovation may be summarised as: greater opportunity to incorporate the latest technology; increased market share; higher value; and more accurate forecasts of customer needs. Although previous studies have pointed out that firms can facilitate their product innovation by leveraging the huge potential value of big data, no studies have systematically investigated how firms can apply big data to facilitate accelerated product innovation. The research was carried out in two stages. Stage one proposed a set of approaches for accelerated product innovation based on the literature studies. The approaches identified were categorised into four innovation phases. Then, the phases were refined from empirical research. The refined phases were further examined in three cases to develop a framework. During the second stage, a set of propositions were established according to the best approaches identified from the framework. The propositions were examined in five in-company case studies, in which qualitative data collection was applied. As well as this, the qualitative investigation through multiple case studies of diverse companies were executed to explore and compare key elements of big data in the context of product innovation, and more specifically in different phases of new product development. The primary outcome of this research has been the development and examination of a framework for accelerated product innovation in a big data environment. The approaches identified from the framework demonstrated a high utility in practice. The traditional role of innovation in competitive success has been redefined to reflect a time-based requirement. Accordingly, accelerated innovation is associated with maximisation of the product success rates, higher profitability and competitive advantage. All five companies in the present case study were applying approaches in product development for accelerating NPD, better understanding of customers' needs, higher revenue growth, and faster launch of new products to market. The empirical findings also show that the role of big data in product innovation is highly dependent on the ability to understand a specific objective or problem, and to examine whether using big data is the right approach for solving that problem. There is a prerequisite for securing distinct resources and organisational capabilities to succeed with implementing big data into new product development. Other important factors that need to be well considered by organisations when forming an implementation strategy are organisations' data maturity and effective change management, especially if the organisation is utilising more traditional innovation processes. However, novel methods rely heavily on extensive and varied data which translates in an adoption urgency to sustain competitive advantage and secure responsive innovation. The main contributions of this research is that it usefully extends the accelerated product innovation literature by clearly defining the concept of accelerated product innovation, and by developing a conceptual framework with six propositions about how, specifically, big data and ICTs can contribute to accelerated product innovation. Then, it offers qualitative evidence from five case studies involving world-leading firms, and explaining how product innovation can most appropriately be accelerated in a big data environment.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.791433  DOI: Not available
Keywords: HF Commerce
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