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Factors Influencing Firm Success with Open Innovation: An Investigation of Relational Proclivity and Supplier Relationships
Sanjay R. Sisodiya, University of Idaho
November 9, 2012
New Product Development Pressures and Problems � Procter & Gamble � New product development problems ◦ Costly in general ◦ R&D ◦ Slow and cumbersome ◦ Ineffective
Sanjay R. Sisodiya, University of Idaho
New Product Development Perspectives � Traditional perspectives – Closed innovation
� Alternative perspectives – Open innovation
� Understanding Open innovation – seems intuitively appealing, but little is known beyond anecdotal. Is it a good idea? Does it work?
Sanjay R. Sisodiya, University of Idaho
Research Projects
Research Development
The Market
Source: Chesbrough (2003)
Boundary of the firm
Closed Innovation
Sanjay R. Sisodiya, University of Idaho
Research Development
Source: Chesbrough (2003)
Boundary of the firm
Open Innovation
Sanjay R. Sisodiya, University of Idaho
Research Projects
Research Development
Source: Chesbrough (2003)
Boundary of the firm
The Market
Open Innovation
Sanjay R. Sisodiya, University of Idaho
Open Innovation � A deliberate, sustained and systematic practice of
engaging in external search for new product inputs such as knowledge, IP, technology, information, ideas, etc.
� An explicit and purposive new product development approach as opposed to an incidental occasional use of external knowledge or technologies.
� Underpinned by complex routines built to seek, access, and deploy external sources of ideas, knowledge, and technologies, and combine such externally sourced new product inputs with the firm’s extant R&D and IP (e.g., Chesbrough 2003; Narasimhan, Rajiv, and Dutta 2006)
Sanjay R. Sisodiya, University of Idaho
Research Questions � What is open innovation in practice?
� Does open innovation improve performance?
� What else needs to be in place to improve success?
Sanjay R. Sisodiya, University of Idaho
Qualitative Study � Not much empirical evidence to support the
benefits of open innovation
� Different perspectives on what open innovation is and how it is measured
� Interviews with managers ◦ Are you familiar with open innovation? ◦ What do you perceive the advantages are? ◦ What are the risks? ◦ What enables firms to be successful? ◦ Do the performance metrics need to change?
Sanjay R. Sisodiya, University of Idaho
How to measure performance? � Firm level, program level, project level?
� Perceptual measures?
� Objective measures?
� Financial performance measures?
� Does innovativeness matter?
Sanjay R. Sisodiya, University of Idaho
Theoretical Model
Sanjay R. Sisodiya, University of Idaho
Relational Proclivity
Proactive Market Orientation
Open Innovation Financial
Performance
Innovativeness
Network Centrality
H7 (+)
Network Density
H8 (+)
H1 (+)
H2 (+)
H3 (+) H4 (+)
H5 (-) H6 (-)
Theoretical Model
Sanjay R. Sisodiya, University of Idaho
Relational Proclivity
Proactive Market Orientation
Open Innovation Financial
Performance
Innovativeness
Network Centrality
H7 (+)
Network Density
H8 (+)
H1 (+)
H2 (+)
H3 (+) H4 (+)
H5 (-) H6 (-)
Theoretical Model
Sanjay R. Sisodiya, University of Idaho
Relational Proclivity
Proactive Market Orientation
Open Innovation Financial
Performance
Innovativeness
Network Centrality
H7 (+)
Network Density
H8 (+)
H1 (+)
H2 (+)
H3 (+) H4 (+)
H5 (-) H6 (-)
Theoretical Model
Sanjay R. Sisodiya, University of Idaho
Relational Proclivity
Proactive Market Orientation
Open Innovation Financial
Performance
Innovativeness
Network Centrality
H7 (+)
Network Density
H8 (+)
H1 (+)
H2 (+)
H3 (+) H4 (+)
H5 (-) H6 (-)
Theoretical Model
Sanjay R. Sisodiya, University of Idaho
Relational Proclivity
Proactive Market Orientation
Open Innovation Financial
Performance
Innovativeness
Network Centrality
H7 (+)
Network Density
H8 (+)
H1 (+)
H2 (+)
H3 (+) H4 (+)
H5 (-) H6 (-)
Firm Performance
� Performance is multi-dimensional (Griffin and Page 1993)
� Long-term oriented investments combined with pressures to highlight marketing activities (Griffin and Page 1993; Doyle 2000; Webster, Malter and Ganesan 2005) ◦ Financial performance ◦ Product innovativeness
� Both can be indicators of the performance of NPD program
Sanjay R. Sisodiya, University of Idaho
Theoretical Model
Sanjay R. Sisodiya, University of Idaho
Relational Proclivity
Proactive Market Orientation
Open Innovation Financial
Performance
Innovativeness
Network Centrality
H7 (+)
Network Density
H8 (+)
H1 (+)
H2 (+)
H3 (+) H4 (+)
H5 (-) H6 (-)
Methods - Data � Context: High Tech (US) – pressures for new product
development, information rich (Narasimhan, Rajiv, and Dutta 2006; Sorescu, Chandy, and Prabhu 2007).
� Data Collection
� Data validation checks ◦ Response bias ◦ Key-informant qualification checks
� Secondary data – CRSP database
Sanjay R. Sisodiya, University of Idaho
Measure Validation AVE Composite
Reliability
Relational Proclivity .717 .882
Proactive Market Orientation .576 .800
Network Centrality .688 .868
Network Density .824 .933
Open Innovation .662 .854
Financial Performance .919 .958
Innovativeness .702 .876
� CFA: χ2 of 7.87 (d.f. = 2 and p = .02), NNFI = .933, CFI = .978, SRMR = .036, and GFI = .981.
� Discriminant validity ◦ Reflective measures – square root of AVE > squared correlation in all cases (Fornell and Larcker
1981). ◦ Formative – Correlations significantly different than 1; most in .2 range, secondary and controls in .3
range with few higher. � Common method bias check - Harmon’s one-factor test (Podsakoff and Organ 1986); the
largest component extracted accounted for only for 18.1% of the total variance Sanjay R. Sisodiya, University of Idaho
Relational Proclivity, Proactive Market Orientation, and Control Variables Main Effects � Relational Proclivity path estimate .193***
(Support for H1) � Proactive Market Orientation path
estimate = .152** (support for H2) � Control variables – age (-), sales (-),
employees(+), and technological turbulence (+)
Sanjay R. Sisodiya, University of Idaho
Interaction terms � H3: Network centrality positively moderates
the effect of relational proclivity on open innovation. (Path estimate = -.145*** ; No Support)
� H4: Network centrality positively moderates the effect of proactive market orientation on open innovation. (Path estimate = .155*** ; Support)
� H6: Network density negatively moderates the effect of proactive market orientation on open innovation. (Path estimate = -.129* ; Support)
Sanjay R. Sisodiya, University of Idaho
Open Innovation on Firm Performance � Open innovation on financial performance
path estimate = .119** (Support for H7)
� Open innovation on innovativeness path estimate = .220*** (support for H8)
� Control variables ◦ Financial performance: sales (+) ◦ Innovativeness: age (-) and technological
turbulence (+) Sanjay R. Sisodiya, University of Idaho
Recap & Implications � Expected that that open innovation would
lead to firm level success. à Results suggest that firms following open
innovation outperform firms not following open innovation
� Anticipated the pivotal element would be
firm capabilities and interfirm relationships in order to seek out inbound inputs for innovation
à Support for relational proclivity and proactive market orientation
Sanjay R. Sisodiya, University of Idaho
Recap & Implications � Network centrality moderation is not
supported ◦ Access to inputs for innovation may not be
enough to enhance success ◦ Firms might need to be able to effectively seek
out and integrate these inputs
� Support for the importance of low network
density, through the management of weak ties to identify inputs for innovation
Sanjay R. Sisodiya, University of Idaho
Discussion � These findings suggest that firms can benefit
while following open innovation. � More importantly, ◦ Firms must be willing and able to connect to
external inputs of innovation ◦ firms can achieve superior outcomes when
positioned within networks of relationships or even research communities.
� The challenge managers may face is how to develop these capabilities and relationships
Sanjay R. Sisodiya, University of Idaho
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