Open Innovation

Open Innovation

Our research in open innovation is motivated by the emergence of open source software (OSS) development as a bona fide alternative to proprietary software development within organizations.  Our early work in this area focused on open source software development and then broadened to encompass other forms of open innovation including crowd-based open innovation (e.g., crowdsourced new product development, innovation from open volunteer work communities) and crowd-based models and platforms for fostering innovation and entrepreneurship (e.g., crowdfunding), and then to general innovation ecosystems. 

This stream of work is centered around two broad questions:

  1. Understanding when, how and why open innovation initiatives (e.g., OSS projects, crowdsourced product development projects, crowdfunding projects) can be successful despite the disadvantageous conditions these project inherently face (e.g., lack of face-to-face contact between participants, lack of formal coordination mechanisms, lack of monetary rewards for labor, etc.), and
  2. How the structural properties of the innovation ecosystem (e.g., open innovation platforms) influence the scale, scope and speed of innovation creation. 

Open Source Software Development

In the context of OSS development, we have conducted several projects to better understand the dynamics of OSS development — e.g., how the network of OSS developers influence how new OSS project teams are formed.  Prior to that work, all of the work on self-organized OSS teams have focused on existing / established teams.  However, how these teams are formed in the first place has never been investigated.  Much of the prior literature paints the picture (at least implicitly) that new teams are formed as a result of common interests (e.g., develops who want to work on a particular type of software would somehow get together and collaborate).  Our work contributes to the literature by showing that the structure of the existing network of developers has a significant influence on how new collaborative teams are formed – developers exhibit status and coherence seeking tendencies when deciding which OSS projects to join. 

Follow-up work investigated the user community surrounding the OSS projects as an important environmental feature that makes OSS different from proprietary software development.  We investigated the user community surrounding the OSS projects to see how participants within these communities can continue to fuel the innovative activities within OSS projects. 

With the growing sophistication of platforms for hosting and collaborating on OSS projects (e.g., GitHub), our attention shifted to understanding how the design and structure of these collaboration platforms would influence the practice and outcomes of OSS projects — e.g., how structural properties of developer networks impact software development trajectories, how the design of open source development platforms influence innovation processes and outcomes, how developers’ work styles (e.g., programming style) influence how they work together and influence project outcomes. 

Open Collaborative Work Communities

One of the revelations from our research on OSS projects and communities was that the open model of innovation creation relying on self-organized volunteers was not confined to software development but could be applied to a broader range of knowledge work activities (e.g., crowdsourced contributions of articles/knowledge to Wikipedia).  We developed theoretical / computational models of open collaborative work communities to investigate how different approaches to project team maintenance (i.e., leadership style) impact sustained innovation contributions and how different contextual factors moderated the efficacy of different leadership styles.  Similar to the work on proprietary IS development teams (see projects on information systems development), we conducted empirical research using data of crowdsourced new product development on to investigate whether and how different types of experience portfolios (i.e., deep specialists or broad generalists as a result of different experience trajectories) have differential impacts on new product development performance, and whether crowd members exhibiting different patterns of experience portfolios would engage in more exploitative or explorative innovation activities. 

Digital Platform Ecosystems

Another stream of work in open innovation focused on the ecosystem-level dynamics of digital innovation platforms.  With new software architectures (e.g., RESTful API architectures) allowing products to be built via recombination of functionalities developed by external parties, the pace of innovation has accelerated tremendously.  Companies are now offering software products and services that integrate in real time functionalities of other companies – e.g., the Uber ride hailing app can be built by integrating the Google Maps API for the mapping functionalities, the PayPal API for payment processing, etc.  As a result, understanding how the structure of such open ecosystems of innovation creation work become critical to understanding how market dynamics change. 

We have recently been involved in a number of research projects studying various aspects of innovation ecosystems.  In one of the projects, we are investigating the dynamics between the demand-side (users) and the supply-side (developers) of digital innovation ecosystems.  Although there exists multiple studies on two-sided platforms (and digital innovation platforms are indeed two-sided platforms consisting of users on the demand side and software developers on the supply side), prior work has focused on the platform level (e.g., comparing two platforms and why one platform might outperform another) but has neglected the within platform dynamics that lead to some digital products outperforming other ones within the same platform.  Our work on the R platform ecosystem evolution studies the interplay between developers’ remix of other developer’s functionalities and market performance (as indicated by user adoption).  

When companies engage in open collaborative innovation activities, they must balance the paradoxical impact of choosing an innovation partner that is not too similar (in terms of existing knowledge base) with themselves so as to offer knowledge diversity, but not too different from themselves (in terms of organizational structure and processes) either as too much differences may impede with collaboration efficiency.  We have also developed theoretical / computational models of digital platform ecosystems which can be used to study the dynamics of competition among firms.