A proposed framework for accelerated innovation in data-driven environments. Evidence and emerging trends from China
| DOI | https://doi.org/10.1108/IMDS-11-2017-0542 |
| Pages | 1266-1286 |
| Date | 09 July 2018 |
| Published date | 09 July 2018 |
| Author | Yuanzhu Zhan,Kim Hua Tan,Robert K. Perrons |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
A proposed framework for
accelerated innovation in
data-driven environments
Evidence and emerging trends from China
Yuanzhu Zhan
Management School, University of Liverpool, Liverpool, UK
Kim Hua Tan
The University of Nottingham, Semenyih, Malaysia, and
Robert K. Perrons
Queensland University of Technology Business School, Brisbane, Australia and
Centre for Strategy and Performance, Institute for Manufacturing,
University of Cambridge, Cambridge, UK
Abstract
Purpose –In today’s rapidly changing business environment, the case for accelerated innovation processes
has become increasingly compelling at both a theoretical and practical level. Thus, the purpose of this paper is
to propose a conceptual framework for accelerated innovation in a data-driven market environment.
Design/methodology/approach –This research is based on a two-step approach. First, a set of
propositions concerning the best approaches to accelerated innovation are put forward. Then it offers
qualitative evidence from five case studies involving world-leading firms, and explains how innovation can be
accelerated in different kinds of data-driven environments.
Findings –The key sets of factors for accelerated innovation are: collateral structure; customer involvement;
and ecosystem of innovation. The proposed framework enables firms to find ways to innovate –specifically,
to make product innovation faster and less costly.
Research limitations/implications –The findings from this research focus on high-tech industries in
China. Using several specific innovation projects to represent accelerated innovation could raise the problem
of the reliability and validity of the research findings. Additional research will probably be required to adapt
the proposed framework to accommodate the cultural nuances of other countries and business environments.
Practical implications –The study is intended as a framework for managers to apply their resources to
conduct product innovation in a fast and effective way. It developed six propositions about how, specifically,
data analytics and ICTs can contribute to accelerated innovation.
Originality/value –The research shows that firms could harvest external knowledge and import ideas
across organisational boundaries. An accelerated innovation framework is characterised by a
multidimensional process involving intelligence efforts, relentless data collection and flexible working
relationships with team members.
Keywords NPD, Accelerated innovation, Data-driven, Innovation approaches
Paper type Research paper
1. Introduction
The current state ofbusiness in the world is one of rapid change and companies are opening
up a new front in globalcompetition (McKinsey, 2015;Liu and Jiang, 2016). It centreson what
we call acceleratedinnovation –that is, reengineering innovationprocesses and R&D to make
new product development (NPD) dramatically faster and less costly (Hagel and Brown, 2011;
Williamsonand Yin, 2014). Any company thatwishes to be proactive must masteraccelerated
innovation (Stalk, 1988; Goktan and Miles, 2011). The market share advantages will go to
“first mover”firms in terms of the pioneer’s opportunity to create the rules for subsequent
competition inits favour (McKinsey, 2015). In a highlycompetitive environment, to be firstin
the market demandsshort NPD times. Even to be a successful laterentrant requires relatively
Industrial Management & Data
Systems
Vol. 118 No. 6, 2018
pp. 1266-1286
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-11-2017-0542
Received 23 November 2017
Revised 9 February 2018
5 April 2018
Accepted 14 April 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1266
IMDS
118,6
fast NPD capabilities, to meet customer needs before they change (Ahmad et al., 2013). In
addition, important cost benefits can be achieved by firms that learn to manage accelerated
NPD (Barczak, 2012). Significant advantages accrue because resources are utilised more
creatively and efficiently, costs are reduced and work-in-process bottlenecks are minimised
(Millson et al., 1992; Cooper, 2014; Adner and Kapoor, 2010).
Traditionally, NPD is viewed as a firm-driven activity, with the firm being responsible for
coming up with ideas for new products and deciding which should be commercialised and
developed (Cooper, 2014; Barczak, 2012). Advances in information and communication
technologies are enabling new initiatives to be explored and are transforming NPD (Bharadwaj
and Noble, 2015; Liu and Jiang, 2016). In particular, data from different sources can be captured
and used to improve NPD. IBM (2013) reports that 90 per cent of the data that exists in the world
today was created in the last two years and it is expected the global total of data will reach 35
zettabytes by 2020 (Wong, 2012). This is therefore the era of “big data”(Chan et al., 2016). Firms
now can access a variety sources of data, such as click streams, videos, tweets and other
unstructured sources to extract new ideas or understanding about their products, customers
and markets (Tan et al., 2015; Bharadwaj and Noble, 2015). According to Sanders (2014), data
analytics (i.e. capturing useful information from data, to inform decision making) has given rise
to intelligent product innovation and can help to enhance NPD in many ways.
However, embedding and sustaining accelerated innovation in a data-driven
environment is not easily achieved. Few studies have explicitly explored approaches to
accelerated innovation. Findings from existing studies mainly suggest that most innovation
approaches are based on changing technology in the firm’s environment (Millson et al., 1992;
Liu and Jiang, 2016). In today’s“big data”era, tones of data constitutes an infrastructural
resource that could be used in several ways to produce different products and services
(Wong, 2012; McKinsey, 2011; Sanders, 2014). However, we are unaware of other papers that
attempt to bring together data-driven initiatives on this increasingly important accelerated
innovation approaches. The overwhelming majority of these earlier contributions in the area
of accelerated innovation have sought to identify potential success factors by analysing
relatively large samples and quantitative methodological approaches (Kessler and
Chakrabarti, 1996; Callahan and Moretton, 2001; Swink et al., 2006; Stanko et al., 2012;
Eling et al., 2013); by stark contrast, there has been a relative paucity of investigations in
this area that have used case research, and that have explicitly explored approaches for
accelerated innovation in a data-driven environment. Therefore, a systematic study of the
implications of data-supported accelerated innovation approaches on NPD could greatly
extend knowledge in this respect (Bharadwaj and Noble, 2015).
Moreover, a recent survey revealed that 59 per cent of respondents who described their
organisation as “data-driven”said that their company is more profitable than competitors
(Economist,2015). However, the literatureremains divided with regardsto the specific ways in
which companies should apply data analytics to support accelerated innovation in NPD
processes (Wong,2012). Emerging evidence indicates that acceleratedinnovation has already
delivered a broad range of benefits in the marketplace, including greater opportunities to
incorporate the latest technology, increased market share, the ability to generate higher
returns and more accurate forecasts of customer needs (Hagel and Brown, 2011; Williamson
and Yin, 2014; McKinsey, 2015; Calder et al., 2016). While providing high-level evidence of
these benefits, however, these contributions have failed to systematically investigate the
specific mechanics of how firms can apply data analytics to realise these benefits. These
problems and considerations lead to the following research questions concerning in NPD:
RQ1. What are the best approaches to accelerated innovation?
RQ2. In a data-driven environment, how can data analytics be applied to support
accelerated innovation?
1267
Proposed
framework for
accelerated
innovation
To continue reading
Request your trial