Governance in Digital Open Innovation Platforms
Summary and Keywords
Open innovation is defined as an approach to innovation that encourages a broad range of participants to engage in the process of identifying, creating, and deploying novel products or services. It is open in the sense that there is little to no restriction on who can participate in the innovation process. Open innovation has attracted a substantial amount of research and widespread adoption by individuals and commercial, nonprofit, and government organizations. This is attributable to three main factors. First, open innovation does not restrict who can participate in the innovation process, which broadens the access to participants and expertise. Second, to realize participants’ ideas, open innovation harnesses the power of crowds who are normally users of the product or service, which enhances the quality of innovative output. Third, open innovation often leverages digital platforms as a supporting technology, which helps entities scale up their business.
Recent years have witnessed a rise in the emergence of a number of digital platforms to support various open innovation activities. Some platforms achieve notable success in continuously generating innovations (e.g., InnoCentive.com, GitHub), while others fail or experience a mass exodus of participants (e.g., MyStarbucksIdea.com, Sidecar). Prior commentaries have conducted postmortems to diagnose the failures, identifying possible reasons, such as overcharging one side of the market, failing to develop trust with users, and inappropriate timing of market entry. At the root of these and other challenges that digital platforms face in open innovation is the issue of governance. In the article, governance is conceptualized as the structures determining how rigidly authority is exerted and who has authority to make decisions and craft rules for orchestrating key activities. Unfortunately, there is no comprehensive framework for understanding governance as applied to open innovation that takes place on digital platforms. A governance perspective can lend insight on the structure of how open innovation activities on digital platforms are governed in creating and capturing value from these activities, attracting and matching participants with problems or solutions, and monitoring and controlling the innovation process.
To unpack the mystery of open innovation governance, we propose a framework by synthesizing and integrating accreted knowledge from the platform governance literature that has been published in prominent journals over the past 10 years. Our framework is built around four key considerations for governance in open innovation: platform model (firm-owned, market, or community), innovation output ownership (platform-owned, pass-through, or shared), innovation engagement model (transactional, collaborative, or embedded), and nature of innovation output (idea or artifact). Further, we reveal promising research avenues on the governance of digital open innovation platforms.
Open innovation is an approach to the process of identifying, creating, and deploying novel products or services. It is open in the sense that there are little to no restrictions on who can participate in the innovation process. This is in contrast to traditional approaches to innovation that have typically remained the purview of experts or an organization’s research and development (R&D) department (West & Bogers, 2014). The value proposition of open innovation is that it expands the number of participants in the innovation process and, in so doing, facilitates exposure to a greater breadth of knowledge and perspectives. Consistent with the idea of the wisdom of crowds, the expectation is that involvement of a greater number of participants enhances the odds of discovering novel products or services that are valued in the marketplace (Surowiecki, 2004). Organizations see open innovation as “the use of purposive inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation, respectively” (Chesbrough, Vanhaverbeke, & West, 2006, p. 1).
While open innovation has been in practice for some time—for instance in the form of open-source software development—its modern manifestation grew to prominence following the emergence of Web 2.0, which provided the digital infrastructure for laypeople to dynamically generate content. An important precursor was the emergence of online communities, “a virtual organizational form supported by digital platforms in which knowledge collaboration can occur in unparalleled scale and scope” (Faraj, Jarvenpaa, & Majchrzak, 2011, p. 1224). Online communities generally focus on exchange of knowledge on topics of mutual interest to participants as an end in itself (Butler, 2001; Faraj et al., 2011, 2016; Gu, Konana, Rajagopalan, & Chen, 2007). The emergence and growth of this organizational form demonstrated an appetite for knowledge exchange on a vast scale, laying the groundwork for more coordinated activity directed specifically toward innovation. Open innovation directs the exchange of knowledge toward identification and creation of novel products and services. Digital open innovation platforms provide the digital space for people to gather and exchange ideas from any place with an Internet connection. Since this development, there has been tremendous growth in the number of organizations embracing open innovation and in the breadth of domains in which it is being applied. A number of incumbent organizations (e.g., General Electric, Starbucks, IBM) have launched their own open innovation platforms. As an example, IBM established the OpenPOWER Foundation and opened its enterprise-leading architecture to a broader ecosystem of innovators. It brought IBM higher levels of industry collaboration and greater competitiveness in fulfilling the market’s growing need for high-performance hardware (King, 2019). Other organizations (e.g., Threadless, Minted.com, InnoCentive) have developed their entire business model around open innovation. For instance, InnoCentive’s business model is built around hosting contests in which participants compete to develop the best solutions to scientific problems posted by its client organizations. InnoCentive imposes few, if any, restrictions on who can participate in their contests.
Digital open innovation platforms are appealing for three main reasons. First, the digital nature of such platforms combined with widespread Internet access offers organizations vast reach to expertise beyond their existing employees. Many organizations see open innovation platforms as a cost-effective approach to get exposure to a breadth of ideas on their innovation objectives. With open innovation, organizations are able to approach R&D as an open process without incurring significant human capital costs (West & Bogers, 2014)
Second, digital open innovation platforms provide a channel for organizations to overcome information asymmetries about customer preferences. Participants have better information about market demand for products and services because they are usually customers of the organization (Hwang, Singh, & Argote, 2019; Lakhani, Lifshitz-Assaf, & Tushman, 2013). Through open innovation platforms, participants are able to actively engage in the innovation process, sharing their preferences and gaining a sense of ownership over the products and services generated (Dahl, Fuchs, & Schreier, 2015; Zheng, Xu, Zhang, & Wang, 2018).
Third, digital open innovation platforms enable organizations to scale up the innovation process. The digital infrastructure underlying such open innovation platforms supports millions of interactions in real time, organizing them around multiple innovations. The infrastructure maintains digital archives of who contributed to the innovation as well as what knowledge artifacts were created. Support for such scaling facilitates the emergence of network effects—whereby the value of participating in innovation activities on the platform increases as the number of participants increases—creating self-reinforcing feedback loops that scale the participant base with minimal effort or investment (Parker, Van Alstyne, & Choudary, 2016). Utilizing monitoring and management tools provided by digital platforms, organizations can orchestrate open innovation activities at scale. Organizations using open innovation are also able to regulate the use of intellectual property (IP) at scale through the use of IP systems (Bauer, Franke, & Tuertscher, 2016).
As appealing as it may sound to organizations to engage in open innovation through digital platforms, it is not without drawbacks. First, as with online communities, open innovation supported by digital platforms has permeable boundaries and there is little to no friction preventing participants from leaving at any time (Maruping, Daniel, & Cataldo, 2019; Zhang, Hahn, & De, 2013). This leaves organizations vulnerable to the whims of participants, who may choose to disengage from innovation efforts that they do not find appealing. Unlike with internal R&D departments, on open innovation platforms there is little predictability regarding whether there will be sufficient engagement in the innovation process since participation is voluntary and there are no contractual obligations on the part of participants. Second, large-scale orchestration of open innovation is challenging. With hundreds of thousands of participants interacting every day, it is difficult for organizations to channel the focus and direction of innovation activities (Di Gangi, Wasko, & Hooker, 2010). This can create problems when innovations generated by participants are not in line with the organization’s preferred strategy or resource base (Majchrzak & Malhotra, 2016). Efforts to manage innovation activities in real time at scale are highly resource intensive. Third, opening the innovation process to external participants while profiting from the ideas they generate can raise major concern about exposing organizations to costly IP ownership considerations and long-term incentives for participants to share their ideas.
Platform governance has been identified as a key undertaking to manage exchange activities in large-scale ecosystems geared toward co-creation (Tiwana, Konsynski, & Bush, 2010). In one of the early studies on platform governance, Tiwana et al. (2010) defined platform governance as “who makes what decisions on a platform” (p. 679). We add to this conception of platform governance the articulation and enactment of rules that determine how participants interact on the platform. Such governance choices can have profound implications for the engagement of participants and the output of innovation processes. Organizations using digital open innovation platforms inherently face a governance challenge common to most platform ecosystems: striving to maintain sufficient control over the innovation process to ensure its integrity and achieve objectives while at the same time being hands-off enough to allow participants on the platform to engage in the innovation process without feeling constrained (Huber, Kude, & Dibbern, 2017; Tiwana et al., 2010). Huber et al. (2017) noted that, along with this fundamental challenge, platform owners also face a dyadic governance tension whereby platform owners experience significant governance costs in trying to micromanage and locally adapt co-creation activities at large scale but risk losing control of co-creation activities if they adopt a less costly arm’s-length approach.
The purpose of this article is twofold. The first objective is to synthesize accreted knowledge on governance in digital open innovation platforms. While a robust body of empirical work has developed on open innovation platforms, with few exceptions there has been little effort to synthesize the findings with regard to governance (Felin & Zenger, 2014). Further, Felin and Zenger (2014) focused on open innovation in general and their framework is not aimed at providing an in-depth explication of governance in digital platforms specifically. The second objective is to develop a broad framework of governance trade-offs in digital open innovation platforms. The article contends that different digital open innovation platform arrangements require contextualized consideration of governance trade-offs. The literature in this domain up to the early 21st century lacks an overarching framework that cuts across different digital platform models (firm-owned, market, or community), innovation output ownership (platform-owned, pass-through, or shared), innovation engagement models (transactional, collaborative, or embedded) and the nature of innovation outputs (idea or artifact). The article concludes by outlining promising future directions for research on governance of open innovation on digital platforms.
The article has four main sections. The next section provides an overview of open innovation, how it works, and the role that digital platforms play. The second section synthesizes the accreted knowledge on governance in digital open innovation platforms. The third section introduces a proposed framework for governance in digital open innovation platforms. The concluding section highlights promising avenues for future research in this domain.
How Open Innovation Works
Open innovation is a multifaceted process involving collaboration among multiple participants to produce innovative products or services. It begins with an initiator of the open innovation process who rallies participants to join, which can take different forms. For instance, in open-source software development, it begins with an initiator’s creating a repository and developing some initial code to which participants can contribute (Medappa & Srivastava, 2019; Stewart, Ammeter, & Maruping, 2006). In innovation contests, this takes the form of an open call describing the problem that the desired solution is intended to address (Koh, 2019; Lee, Ba, Li, & Stallaert, 2018). In user innovation communities, it is an ongoing open call for customers to ideate new products and services (Bayus, 2013; Di Gangi et al., 2010; Nambisan & Baron, 2010). Once the open innovation process is initiated, participants contribute to it by providing their own inputs, modifying others’ inputs, or commenting and providing feedback on others’ inputs.
Open innovation processes differ with regard to the initiator’s implicit and explicit expectations about the capacity in which participants are involved (Malhotra & Majchrzak, 2014). Many open innovation engagements invite participation at the ideation phase (Bayus, 2013; Di Gangi et al., 2010). In such arrangements participants’ contributions take the form of idea generation (Malhotra & Majchrzak, 2014). For example, participants on Dell’s IdeaStorm platform developed the idea of offering a product configuration that included pre-installation of the Linux operating system. In such open innovation engagements, the determination of which ideas will be implemented in the form of new products or services is made by an entity separate from the participant community (e.g., an organization’s innovation team; Kallinikos & Tempini, 2014). Other open innovation engagements expect participants to contribute to both the generation of new ideas and the selection of ideas that should receive closer attention (Hutter, Füller, Hautz, Bilgram, & Matzler, 2015; Malhotra & Majchrzak, 2014). This is commonly the case in open innovation engagements where the number of ideas generated can become prohibitively large for a smaller entity to process. Participants in the community contribute by reducing the number of ideas to a manageable set that a separate entity can analyze. Yet other open innovation engagements require participants to contribute either in part or in whole to the development of the actual solution. For instance, Kaggle requires participants to develop the actual predictive models in their innovation contests (Lee et al., 2018). Similarly, participants in TopCoder innovation contests are expected to develop software or wireframe designs as their contributions to the innovation process (Zhang, Singh, & Ghose, 2019).
As noted in the introduction, the process is open insofar as anyone is welcome to participate. The boundaries that delineate who is a participant in a particular innovation engagement versus who is not are highly permeable, enabling participants to freely join or leave the innovation process as they please (Faraj & Shimizu, 2018).There are two immediate consequences of such openness. One is that the number of participants in the innovation process tends to grow to a large scale. Participation in such engagements generally tends to follow a power law distribution, whereby a small number of participants make a majority of the contributions while a majority of participants make minor contributions (Howison & Crowston, 2014; Johnson, Faraj, & Kudaravalli, 2014). This may create a bias toward generating fewer ideas, as innovative outputs are mostly made by a small group of participants who are more experienced or knowledgeable, which crowds out novice participants over time. Another consequence is that participants tend to represent a diverse set of backgrounds, from technical experts to lay consumers (Dahlander & Frederiksen, 2012). For instance, Hwang et al. (2019) studied open innovation by customers of a telecommunications company and found that customers with deep technical knowledge had a higher likelihood of developing innovative solutions—that is, solutions that are novel, popular, and feasible—when they interacted with nontechnical customers. It is notable that the presence of participants with diverse backgrounds is viewed as essential to successful innovation. The Ocean Spill Recovery Institute posted an innovation contest on the digital open innovation platform InnoCentive. The innovation contest was for a solution to recovering frozen oil in barges. The winning solution was contributed by a participant with expertise outside of the oil industry (Lakhani, 2009).
In sum, while open innovation aims to achieve one objective—to generate new products or services by attracting the contributions of a large and diverse community of participants—the process of doing so can take different forms and involve participants in different capacities. As open innovation almost exclusively takes place through digital platforms, it is important to understand the role that such platforms play in shaping the innovation process The next section provides an overview of the role of digital platforms in open innovation.
Role of Digital Platforms in Open Innovation
Digital open innovation platforms serve as the meeting space in which participants collaborate to generate their innovations (Constantinides, Henfridsson, & Parker, 2018). In addition to serving this core function, digital platforms also provide several support functions for open innovation. Notable among these are an infrastructure for curating innovation artifacts and opportunities, design mechanisms to incentivize desired participant contribution behavior, and innovation toolkits for participants. Together, these functions enable open innovation to unfold fluidly and efficiently at large scale (Parker et al., 2016). They facilitate flexibility in the instantiation and execution of innovation activities. Participants on digital platforms can collaborate in open innovation activities regardless of time and location as long as they have access to the Internet. Further, digital platforms match organizations who seek innovative solutions with individuals who are enthusiastic about contributing to innovation activities without going through the traditional recruitment process, thus reducing human capital costs (West & Bogers, 2014). The support functions that digital platforms provide are elaborated next, as this will be foundational to understanding how open innovation is governed at scale in such environments.
Curation is the process of selecting and organizing vast collections of information and artifacts. It has long been recognized that digital platforms provide an important organizing infrastructure for large-scale collaboration. For instance, Butler and Wang (2012) emphasized the role of directories and hierarchies as an organizing infrastructure for participants in online communities to understand where to contribute content on specific topics. Participants wanting to contribute to specific community discussions are guided by such organizing infrastructures from high-level directories down to more granular discussion threads (Butler & Wang, 2012). In much the same way, digital open innovation platforms provide an organizing infrastructure that enables participants to determine what innovation engagements are occurring at a particular point in time and the general problem domains to which they relate (Singh, Tan, & Mookerjee, 2011). These organizing infrastructures contain digital traces of contributed ideas and exchanges between participants. They also contain innovation artifacts, such as software code in open-source repositories or foundries (Dabbish, Stuart, Tsay, & Herbsleb, 2012; Singh et al., 2011). Organizing artifacts like software code also generates resources that can be reused for development of other innovations beyond the current one for which they were created, thus speeding the innovation process (Boudreau, 2012; Haefliger, Von Krogh, & Spaeth, 2008). Organizing infrastructure on digital open innovation platforms is intended to ensure that participants are matched to the innovation opportunities in which they seek to engage (Parker et al., 2016).
In order to direct participants’ contributions to innovation engagements at scale, digital platforms need to embed appropriate incentive mechanisms (Malhotra & Majchrzak, 2014). Failure to embed appropriate incentive mechanisms can result in undesirable contribution behaviors or a lack of desired innovation activity. Digital platforms provide a variety of mechanisms to entice participants to contribute to open innovation engagements. Many digital open innovation platforms create leaderboards that feature participants who contribute the most ideas, comment on the most ideas, or win the most innovation contests (King & Lakhani, 2013; Malhotra & Majchrzak, 2014). Some award stellar participants with badges for their contributions (Dong & Wu, 2015). The expectation is that such incentive mechanisms will tap into recognition and social status markers that participants desire (Dong & Wu, 2015; Jeppesen & Frederiksen, 2006; Malhotra & Majchrzak, 2014). Digital open innovation platforms that require participants to develop innovative solutions tend to offer more substantive recognition. For instance, most innovation contests offer monetary rewards to entice participants to devote expertise and time to develop solutions (Jeppesen &Lakhani, 2010; King & Lakhani, 2013; Koh, 2019; Lee et al., 2018). Other digital open innovation platforms offer named recognition to enable participants to develop a reputation for high-quality contributions (Jeppesen & Frederiksen, 2006; Roberts, Hann, & Slaughter, 2006; Stewart & Gosain, 2006) that can translate into monetary recognition elsewhere (Ågerfalk & Fitzgerald, 2008; Hann, Roberts, & Slaughter, 2013).
Digital platforms provide innovation toolkits to facilitate participation and create an ecosystem for sustainable innovation (Kankanhalli, Ye, & Teo, 2015). Based on participants’ needs, innovation toolkits can be categorized into two types—toolkits for exploring and experimenting with innovative ideas and toolkits for reducing innovation effort (Kankanhalli et al., 2015). Toolkits that support exploratory search help participants find innovation projects of interest. There are also toolkits that support participants in trial-and-error learning and experimentation with innovative products before bringing them to market (Franke & Piller, 2004). To ease participants’ efforts in open innovation activities, digital platforms provide archives of past innovation activities as a reference (Shneiderman, 2007). Digital platforms also provide toolkits for peer communication to save time and effort in online collaboration (Jeppesen, 2005), as well as toolkits for voting to help organizations select the most innovative ideas (Hutter et al., 2015). Toolkit support has been found to positively affect participants who have greater interest in innovation and have experience in innovation activities (Kankanhalli et al., 2015). For open-source software developers, the ability to customize the software with the help of innovation toolkits makes them feel more satisfied, as innovation toolkits can better serve developers’ heterogeneous needs (Franke & Von Hippel, 2003). Therefore, a good understanding and use of toolkits can help organizations motivate participants and coordinate innovation processes at different stages.
In sum, digital open innovation platforms are designed with the necessary incentive mechanisms to govern innovation engagements at large scale. The specific design considerations are informed by the type and extensiveness of contributions that are expected of participants. The next section builds on these themes to synthesize the empirical literature on open innovation.
Synthesis of Research on Open Innovation Platform Governance
The literature on governance in digital platforms is relatively nascent. The earliest systematic discussion of digital platform governance can be traced back to Tiwana et al. (2010), who conceptualized it in terms of who makes what decisions about a digital platform. Research has expanded this conceptualization of platform governance to encompass “all policies and mechanisms through which a platform operator exerts influence over participants on both sides and coordinates operations in the ecosystem” (Song, Xue, Rai, & Zhang, 2018, p. 5). Although not focused specifically on digital platforms, Felin and Zenger (2014) conceptualized governance forms for open innovation as comprising communication channels, incentives, and property rights. Despite being at a nascent stage, a robust body of knowledge is developing on this topic. Some research has identified different types of governance in digital platforms (e.g., Gizaw, Bygstad, & Nielsen, 2017; Oh, Moon, Hahn, & Kim, 2016), other research has identified the antecedents of particular approaches to governance (e.g., Singh & Phelps, 2012; Zheng et al., 2018), and yet others have examined the impact of governance approaches on participants (Oh et al., 2016).
While the literature has explored different types of governance in various application scenarios, it is apparent that it encapsulates several common features. First, governance speaks to authority over decision-making—that is, who gets to make key decisions (Tiwana et al., 2010). Second, governance embodies rules and incentives that are intended to shape behavior (Shaikh & Henfridsson, 2017). The emphasis here is on how rigidly these are defined and how stringently they are enforced. Finally, governance delineates ownership over what comes out of key activities (Felin & Zenger, 2014). On the basis of these commonalities, governance in digital open innovation platforms is broadly conceptualized as encompassing authoritativeness and centralization. Authoritativeness speaks to rules of behavior on the digital platform rigidly defined in a vertical orientation by the owner versus loosely specified and envisioned by participants. Centralization speaks to whether key decisions in the open innovation process on digital platforms are made by the owner versus being delegated down (decentralized) to participants. Governance types can be categorized based on the degree of centralization of decision-making authority. A centralized form of governance allows for a greater control over digital platform standards and provides more opportunity for realizing general economies of scale, while a decentralized form facilitates an increase in local innovation and improves the overall innovativeness of generated outputs (Brown & Grant, 2005). Nevertheless, it is not an either–or choice. Digital platform owners can choose hybrid forms of governance that afford them a certain degree of control while giving participants flexibility for local innovation. For example, in the software development context, Gizaw et al. (2017) defined open generification as a new governance regime that aims to centrally establish generic resources, such as software code and design guidelines, while simultaneously delegating design power to participants, enabling them to influence local innovation. Research has also conceptualized different styles of governance. Oh et al. (2016) conceptualized uniform leader–member exchange as a centralization-prone style with which platform leaders develop a uniform relationship with different community members. They also conceptualized differential leader–member exchange, which is more decentralized and enables leaders to concentrate resources and opportunities in a specific group of community members (Oh et al., 2016).
Research has examined the downstream impacts of different governance choices on the open innovation process and outcomes. Some work has explored the impact of restrictive governance approaches. For example, Huber et al. (2017) found that while closely following ecosystem-wide rules that are predefined by the digital platform owner can reduce governance costs, it also tends to limit co-created value. Conversely, less restrictive rules of the ecosystem to develop a more alliance-like collaboration results in higher levels of co-created value but entails substantially higher governance costs. In the open-source context, decision rights about who determines the rules that govern open innovation are delegated to initiators of innovation engagements. Open-source project initiators determine the license under which the software will be distributed (Singh & Phelps, 2012; Stewart et al., 2006). Restrictive licenses afford an initiator greater control over what can be done with the code. However, such restrictiveness can also impede the kind of experimentation that underlies innovation, as code developed under a restrictive license cannot be combined with code developed under a less restrictive license (Stewart et al., 2006). In contrast, less restrictive licenses permit contributors to combine and make freely available the software code for other participants to use (August, Shin, & Tunca, 2017). While garnering far more contributions from participants (Stewart et al., 2006), projects with restrictive licenses give outside developers a chance to utilize the code for commercial ends, thus undermining the value of the original software (Lerner & Tirole, 2005). Digital platform owners may have the same concern. Karhu, Gustafsson, and Lyytinen (2018) found that an excessive openness of a platform’s core resources through open-source license boundary resources poses a risk to the platform’s control of its resources and makes the platform vulnerable to strategic exploitation behaviors by hostile competitors. Shaikh and Henfridsson (2017) conceptualized governance and coordination as comprising a duality that facilitates the ongoing production and augmentation of open-source products. This duality gives rise to different configurations of coordination processes and adopted forms of authority (Shaikh & Henfridsson, 2017).
In the domain of online innovation contests, decision rights have largely been pushed down to contest initiators. Contest initiators are able to determine the size of the reward and determine whether and how much to facilitate participants’ creative freedom in generating innovative solutions. Empirical findings suggest that when contest initiators intervene with feedback or guidance, they reduce the innovativeness of solutions generated. Koh (2019) and Lee et al. (2018) examined the impact of exemplars provided by contest initiators and found that participants on the digital platform under study tended to imitate the exemplar. However, as the reward size increased, the likelihood that participants followed the exemplar decreased, suggesting that participants put more original creative effort into their contributions (Koh, 2019; Lee et al., 2018). Lee et al. (2018) found that predictive model solutions developed by participants tended to perform more poorly when innovation challenge initiators provided intermitent feedback on their models. Lee et al. (2018) surmised that participants aimed to produce solutions that achieved targets suggested in the feedback rather than aiming for the best solutions possible.
Researchers have investigated factors that contribute to choices of platform governance. For instance, Singh and Phelps (2012) found that when a platform adopter is uncertain about which contractual governance practice (in this case, the license choice) is appropriate, they would imitate the license choice adopted by socially proximate source organizations. This imitation behavior is attenuated by the platform adopter’s experience with particular governance practices. Zheng et al. (2018) found that co-creation in crowdfunding projects can promote sponsors’ psychological ownership, which spurs the sponsors’ perceived control over the project and their intimate knowledge of the project, leading to an increase in sponsors’ commitment to projects.
Despite the fruitful research on digital open innovation platform governance, there remains a lack of comprehensive understanding of governance as applied across various contexts. In light of this opportunity, a governance framework is developed that cuts across key dimensions of open innovation to shed light on how digital platforms can be governed to better orchestrate open innovation activities.
A Digital Open Innovation Platform Governance Framework
Governance in digital platforms can be understood from four perspectives: (a) platform models that shape the role of digital platforms in open innovation activities, (b) ownership structures that describe who has the right to deal with the innovative output, (c) innovation engagement models that depict the way that innovation events proceed, and (d) nature of innovation output that defines the form of the final output generated from the innovation events. Governance choices necessarily involve trade-offs between costs of enactment and impacts on innovation. In the sections that follow, this article delves into detail for each dimension with respect to its characteristics, relevant informing studies, and its implications for the design of platform governance.
Business model is one important perspective from which to understand governance in digital open innovation platforms. Because digital platforms are built on an infrastructure that must be maintained and augmented over time, there is commonly an organization that owns the platform (Constantinides et al., 2018). Despite this commonality, there are distinctive differences in the logic that shapes how the digital platform is organized. For instance, one logic suggests that the digital open innovation platform is geared toward innovating the owner’s products and services; while another logic indicates that such a platform enables the owner to be a facilitator of innovation between other transacting parties. A platform model articulates the core logic that informs open innovation. As is discussed subsequently, such logic also naturally shapes ownership of the innovative outputs generated on digital platforms. Therefore, digital platform model and its governance implications are discussed first, followed by a discussion of innovation output ownership and its implications for governance.
Digital Platform Model
Digital open innovation platforms generally take one of three forms. Firm-owned open innovation platforms are designed and managed by firms that develop products and services for their target markets. Firms establish open innovation platforms to spur innovation in their product and service offerings (Bayus, 2013; Hwang et al., 2019; Nambisan & Baron, 2010). Consequently, such digital open innovation platforms are complementary to the products and services that are the focus of the firm. Examples of such digital open innovation platforms include Dell’s IdeaStorm, Starbucks’ MyStarbucksIdea, and General Electric’s GE Innovation Lab. From a governance perspective, firm-owned platforms maintain authority over the digital platform and centralize the rules of exchange on the platform. Consequently, vertical authoritative structures are commonly adopted in such arrangements. The firm determines the standards for what constitutes appropriate exchange behavior among participants on the platform. Digital platforms are designed with mechanisms to structure exchange—for example, idea submission features, commenting features. Many such platforms incur the costs of employing moderators to monitor and enforce exchanges so that they can extract value from fruitful innovation activity (Di Gangi et al., 2010). Firm-owned open innovation platforms have an inward orientation with regard to the output generated. As such, although some platforms have voting mechanisms for participants, the platform ultimately centralizes the selection of innovative solutions that will be implemented (Bayus, 2013; Gallaugher & Ransbotham, 2010; Hwang et al., 2019). This is because the firm is better informed about how innovative solutions generated by participants fit with its strategy and whether it possesses the necessary resources to bring the innovation to fruition.
Market digital open innovation platforms serve as a meeting space for innovation solution seekers and innovation solution providers. In contrast to firm-owned digital open innovation platforms, the primary function of market digital open innovation platforms is to match diverse expertise with problems in need of innovative solutions (King & Lakhani, 2013). The entire business model is built around this matching function. Such platforms generate their revenues by taking a percentage of transactions that occur on the platform (Parker et al., 2016). Examples of such digital open innovation platforms include InnoCentive, Kaggle, Threadless, and TopCoder. Under such a digital platform model, governance tends be both vertically and horizontally oriented, compared to firm-owned arrangements. Like firm-owned digital open innovation platforms, market digital open innovation platforms centralize some rules of exchange between solution seekers and solution providers. However, they also decentralize other aspects of the rules of exchange, such as determining the level of rewards (Koh, 2019) and whether to intervene with feedback during the innovation engagement (Lee et al., 2018). Owing to their matching role, market open innovation platforms have an outward orientation with regard to the innovations generated. It is no surprise, then, that decision rights about the selection of innovative solutions is delegated to solution seekers (King & Lakhani, 2013; Majchrzak & Malhotra, 2016). Overall, this tends to reduce the monitoring and enforcement costs that market digital open innovation platforms face. These costs tend to be moderate, as the platform owner still needs to ensure that the solution providers on their platform are treated fairly.
Community digital open innovation platforms are a third form of digital platform model . They serve as a meeting space for participants to work on innovations that are of mutual interest. Unlike firm-owned digital open innovation platforms, they are not aimed at complementing the platform owner’s own products and services. Further, unlike market digital open innovation platforms, their primary function is not to generate revenue by matching solution seekers and problem solvers. Rather, they function as a digital space for organic forms of collaboration. Open-source platforms like GitHub are an exemplar of this form of open innovation platform. Community digital platform owners centralize the rules of exchange and implement them through a variety of digital mechanisms (Dabbish et al., 2012). Relative to firm-owned platform models, they have a notable horizontal authoritative structure. Given their community orientation, the rules of exchange are encoded in norms into which participants are socialized (Daniel, Maruping, Cataldo, & Herbsleb, 2018; Maruping et al., 2019; Stewart & Gosain, 2006). The orientation of innovation outputs is toward the needs and preferences of participants rather than the digital platform owner. Consequently, decision rights about selection of appropriate solutions tend to be delegated to participants. Further, as participants are socialized into the rules of exchange through community norms, they engage in self-enforcement through social sanctions and guidance when necessary (Maruping et al., 2019). The digital platform owner incurs low costs of monitoring and enforcement.
Table 1a summarizes the governance approaches informed by different digital platform models. A general observation is that the more the digital platform owner seeks to directly benefit from open innovation activity on the digital platform, the greater the need for centralization of decision-making and the greater the need for rigid rules to shape behavior.
Table 1a. Governance Approaches of Different Platform Models
Rules of exchange
Monitoring and enforcement
Selection of solutions
Orientation of innovation output
Innovation Output Ownership
Ownership of innovative output is a critical component of digital platform governance. It determines whether the innovative output is proprietary to the digital platform owner or is shared by multiple participants (Tiwana et al., 2010). Innovation output ownership reflects the extent to which the digital platform owner has the continued right to use the innovative output (Parker & Van Alstyne, 2018). Some digital platform owners require participants to relinquish their IP rights once their contributions are accepted (Jeppesen & Lakhani, 2010), while others agree to open IP rights to the community for anyone to reuse and create innovative products (Lee & Cole, 2003). Parker and Van Alstyne (2018) suggested that participant retention of ownership rights increases the royalties that they are able to extract. In contrast, the sooner such ownership rights expire, the sooner other participants can build on the innovation to generate other innovations.
Three forms of innovation output ownership are conceptualized: platform-owned, pass-through, and shared. Governance choices regarding ownership of innovation outputs are informed by the objective of the digital platform owner. Platform ownership aims to acquire benefits after the innovation engagement by implementing the innovative ideas generated from participants (Schlagwein & Bjørn-Andersen, 2014). This form of governance is common in firm-owned digital platform models where the orientation is toward fulfilling a firm’s own innovation needs. The firm that owns the digital platform is able to lay exclusive claim to the innovation and its benefits. A potential negative consequence of this governance choice is that it may demotivate participants’ enthusiasm in innovating when organizations have restrictive ownership. Franke, Keinz, and Klausberger (2013) found that the more that terms and conditions favor the organization (e.g., with regard to IP ownership), the less the potential participants’ expectations of distributive fairness, decreasing their willingness to participate in innovation activities.
Decentralizing ownership supports and facilitates collaborative content production, which can benefit the digital platform owner in the long term (Oh et al., 2016). Such decentralization manifests in one of two main forms of innovation ownership. Pass-through ownership represents an open innovation arrangement in which there is a transfer of ownership of innovation output from solution generators to solution seekers. The digital platform owner at no point takes legal ownership of the innovations generated on the platform. The digital platform serves as a staging area for generating and transferring innovations to those seeking them. Market digital open innovation platforms frequently adopt this form of governance for innovation outputs. The expectation is that participants receive fair compensation for their time and effort through the reward that is offered by solution seekers. By putting up the monetary reward, solution seekers claim ownership over the generated innovation output (Koh, 2019; Lee et al., 2018). Some digital open innovation platforms decentralize ownership of IP to participants. For instance, Threadless allows winning contest participants to retain ownership of their designs. These participants are able to sell their designs through the digital platform and the platform owner receives a percentage of each sale (Brabham, 2010). In both cases, the digital platform owner does not retain ownership of innovation outputs.
Another decentralized form of governing innovation outputs is shared ownership. In contrast to pass-through ownership, with shared ownership, there is no transfer of ownership of innovation outputs out of the digital platform. Rather, once innovative outputs have been generated by participants on the digital platform, they remain as an open resource freely available to all participants. For instance, GitHub is an attractive destination for participants looking to contribute to and leverage software innovations because the IP can be freely reused within and outside of the community. This has made GitHub a highly valuable digital open innovation platform (Weinstein, 2018). One trade-off of such a governance choice is that relinquishing ownership to the market may induce hostile competitors to reap the benefits of innovative outputs, enabling them to create similar products, resulting in decreased competitive advantage of the digital platform owner (Karhu et al., 2018).
Table 1b summarizes the governance approaches informed by different forms of innovation output ownership. It is clear that the question of governance of IP ownership is essentially a trade-off between deriving maximal monetary benefit from generated innovations and stimulating robust participation in innovation engagements. For digital platform owners who would like to facilitate the generation of highly innovative outputs, decentralizing IP ownership appears to be ideal, so that participants have greater flexibility in using resources to create innovative ideas, designs, or artifacts. For digital platform owners who want to retain the ongoing development rights of the innovative output and acquire benefits instantly, centralization of ownership decision rights appears to be an ideal approach so that they have full control of innovative outputs.
Table 1b. Governance Approaches Across Different Innovation Output Ownership Types
Profits acquired from the output
Number of generated innovations
Instant and continuous
Participants seeking solutions, participants generating solutions
Instant and one-time
Note. Grayed-out cells are not applicable.
Innovation Engagement Models
Innovation engagement models describe who is engaged in open innovation events and how the innovation process proceeds. Based on the extent to which participants collaborate or compete with each other, innovation engagement models can be divided into three types: embedded, collaborative, and transactional.
In embedded innovation engagement models, norms play a key role in socializing participants to appropriate ways of engaging in innovation activities. Participants become embedded into a network of innovation activity with other participants. This model is highly collaborative in the sense that participants share resources and work with each other closely in the innovation process at various stages. Collaboration features are designed around the innovative contributions (e.g., commenting and feedback features, up-voting and down-voting). In addition, a sense of community is fostered by building mechanisms that enable contributors to socially connect with each other directly, much as is done on social media platforms (e.g., follow, direct message). Such an orientation toward social embeddedness enables digital platform owners to adopt governance approaches that are decentralized. Open-source software development platforms like SourceForge and GitHub are classic examples whereby community members, such as code developers and software users, are socialized in how to contribute to the source code or provide comments and feedback to improve the functionality of the software product (Medappa & Srivastava, 2019). Digital platform owners are less authoritative in articulating and enforcing rules of engagement and instead decentralize governance through delegating decision-making power to software development teams so that they can decide which code contributions and feedback will be accepted. Another example is Wikipedia, where the digital platform owner empowers community members with the latitude to create and enforce community norms (Kane & Ransbotham, 2016).
In the collaborative innovation engagement model, participants contributing innovative ideas and solutions are given means to work together and build upon each other’s input. As with the embedded innovation engagement model, collaboration features are designed around the innovative contributions. However, the emphasis in the collaborative innovation engagement model is not on developing social connections in the open innovation process. Thus, social features are typically not designed into such digital platforms. Firm-owned digital platform models adopting a platform-owned innovation ownership approach tend to favor this mode of innovation engagement. Authoritative, centralized forms of governance are commonly adopted, which gives the digital platform owner enough control over the innovation process and selection and output criteria. An example can be seen in a study by Jeppesen and Frederiksen (2006). In the organization-hosted user community Propellerhead (the digital platform owner) uses centralized and authoritative governance by employing user communities to create innovative sound products and monitoring users’ innovation activities (Jeppesen & Frederiksen, 2006). In another example, ill-structured organizational problems were broadcast to the digital platform participants. Organizations then enacted centralized governance by soliciting selective participants based on criteria with respect to their interest in, opinions on, and expertise in the problem, and by evaluating the idea according to its novelty and potential to confer competitive advantage (Majchrzak & Malhotra, 2016).
Transactional innovation engagement models are arm’s length in nature. Such arrangements emphasize the generation and transfer of innovative output from participants. This mode of innovation engagement does not incentivize collaboration or sharing of ideas and feedback among participants generating innovative outputs. The natural tendency is toward competitiveness among participants contributing innovative solutions. Governance of such arrangements can vary depending on the digital platform model and innovation ownership model enacted. Given their inward orientation, firm-owned digital platform models tend to favor platform-owned innovation ownership. Consequently, their approach to governance of transactional innovation engagement is authoritative and centralized to ensure that the firm gets the innovative outputs it desires. For instance, Swarovski initiated a jewelry design contest and rigidly articulated the rules of exchange, while inviting participants to contribute their evaluation, feedback, or suggestions on submitted ideas for further revision and improvement (Füller, Hutter, Hautz, & Matzler, 2014). In another example, a public transportation organization initiated an innovation contest to generate new ideas on interior designs for trains. The organization adopted an authoritative form of governance for rules by setting up the criteria for winning ideas, while also adopting a decentralized form of governance in selection by allowing all participants to vote on submitted ideas (Hutter et al., 2015). In contrast, firms adopting a market digital platform model have an outward orientation and tend to favor a pass-through stance on innovation ownership. Consequently, governance of transactional innovation engagement is relatively more decentralized and less authoritative in nature. For example, in their innovation contests, Kaggle.com adopts horizontal governance in determining rules for participants, criteria for winning solutions, and selection of finalists. Solution seekers play a primary role in making these determinations in addition to providing feedback on submitted solutions (Koh, 2019).
Table 2 shows that innovation engagement models differ in the degree of control exerted over the innovation process, the nature of participant engagement, the explicitness of the outcome, and how bounded the innovation is expected to be. Digital platform owners who clearly know what they expect from participants’ contributions can adopt authoritative and centralized forms of governance so that they can collect innovative outputs from a specific group of participants more efficiently. Digital platform owners who want to see multiple innovative solutions proceeding at the same time can adopt forms of governance that are less constraining. They can retain decision power while facilitating a relatively wide range of innovation contributions. Another critical factor that helps organizations make the decision is the type of innovation outputs expected of participants, which is discussed in the next section.
Table 2. Governance Approaches Across Different Innovation Engagement Models
Innovation engagement model
Control over innovation process
Nature of participant engagement
Explicitness of expected outcome
Centralized or decentralized
The digital nature of digital open innovation platforms necessitates that the output of innovation engagements on the platform also be digital. Even for solutions that culminate in a physical product, the process by which the solution is articulated is digital. For example, although the solutions generated in 3D printing innovation communities pertain to physical products, the actual design and specifications are digital (Kyriakou, Nickerson, & Sabnis, 2017; Stanko, 2016). Digital artifacts are transmissible across a large-scale network, enabling numerous participants to engage in their construction.
Broadly, the types of outcomes generated can range from simple text to artifact design to a fully functioning artifact. Varying levels of expertise and commitment are required of participants to generate innovation outputs. It is important to note that although innovation outputs embody a variety of modalities, the modalities themselves do not imply greater expertise or commitment from participants. Consider the examples of Dell IdeaStorm and InnoCentive, both of which use text as a modality for generating outputs on their digital platforms. On IdeaStorm, articulating new product ideas—in the form of text—typically requires little specialized expertise and time by participants. In contrast, on InnoCentive, articulating a solution to an innovation challenge—also accomplished in the form of text—requires greater specialized expertise from participants as they must draw upon their knowledge, clearly specify multiple aspects of their solution, and fully state their assumptions. Consequently, the modality of the innovation output is an important consideration, but it must be coupled with the level of expertise and commitment required by participants. Governance approaches are likely shaped by consideration of both. We now discuss the governance approaches likely to be associated with each broadly identified modality and the implied level of expertise for participants.
Decentralized governance approaches are more likely to be used in engagements where text is the main modality for innovation outputs and the amount of expertise required of participants is lower. The contributions of participants are primarily driven by social currency—whereby participants gain some social value (e.g., reputation, good will, influence) in exchange for making a contribution to the innovation effort (Parker et al., 2016). Enactment of such governance is decentralized in the sense that participants are responsible for recognizing the currency and giving it meaning. Digital open innovation platforms implement such decentralized governance through visible features designed into the platform. Cumulative counts of the number of ideas submitted and the amount of feedback given to others appear next to participants’ names on the digital platform. In contrast, for innovation engagements requiring greater levels of expertise and a greater investment of time, social currency tends to be a less salient governance mechanism. Digital open innovation platforms that require participants to devise fully fleshed solutions utilize monetary rewards to incentivize desired participation (King & Lakhani, 2013). This form of governance is moderately centralized in the sense that, while digital platform owners require that a monetary reward be offered, solution seekers have the latitude to determine the size of the reward.
Open innovation engagements in which participants are expected to contribute the design of a solution (e.g., conceptual design) inherently require a high degree of expertise and time commitment (Füller et al., 2014). For example, Power Trading Agent Competition (a competitive gaming platform) requires research groups to submit new designs to solve sustainable electricity challenges, although the solutions are not expected to be fully functioning (Ketter, Peters, Collins, & Gupta, 2016). Digital open innovation platforms like TopCoder, Thingyverse, and Threadless enable participants to contribute such digital designs. Although this form of contribution is time consuming and requires specialized expertise, different governance approaches are evident. Collaborative and embedded innovation engagement models employ decentralized governance, providing participants with the latitude to enforce rules through community norms (Kyriakou et al., 2017). Social currency mechanisms, such as “likes” and “collect” of 3D designs, enable community members to socially reward participants who conform to norms. In contrast, other digital open innovation platforms employ moderately centralized governance in the form of a combination of social currency and monetary rewards. Such digital platforms leverage social currency to decentralize governance through badges and voting but also demonstrate authoritative governance by stipulating the necessity for monetary rewards. For instance, Threadless allows community members to vote on designs submitted by participants while the platform also controls the contest rewards. Similarly, Topcoder offers social currency, such as badges and leader boards, as a form of decentralized governance to incentivize participant contributions while also mandating that solution seekers on the digital platform offer rewards (Zhang et al., 2019).
Finally, digital open innovation platforms that require the development of fully functioning solutions require significant expertise and time commitment by participants. Kaggle requires participants to develop the actual predictive models in their innovation contests (Lee et al., 2018), GitHub and SourceForge expect participants to take part in the software development process by developing new source code, testing code, and tracking and fixing errors (Singh & Phelps, 2012; Zhang et al., 2019) and participants produce audio tracks in music remixing communities (Stanko, 2016). Interestingly, as demanding as these innovative outputs are in terms of expertise and time commitment, there appears to be evidence of both decentralized and moderately centralized forms of governance associated with them. Decentralized governance is employed in the development of open-source software through an embedded innovation engagement model (Dabbish et al., 2012). Participant behavior is shaped by strong norms that are informed by a community ideology (Daniel et al., 2018; Maruping et al., 2019; Stewart & Gosain, 2006). In contrast, digital open innovation platforms that employ a transactional innovation engagement model tend to use authoritativeness by stipulating that monetary rewards are required but providing solution seekers with the latitude to determine the size of the reward (Koh, 2019; Lee et al., 2018).
In summary, the innovation outcomes generated on digital open innovation platforms demand varying degrees of expertise and time commitment. Innovation outputs that require low levels of expertise and time commitment tend to employ decentralized forms of governance. It is interesting to observe that demanding (in terms of expertise and time commitment) innovation outputs employ both decentralized and moderately centralized forms of governance depending on the collaborative versus transactional model of innovation engagement.
Conclusion and Future Research
This article is motivated by the recognition that, despite their promise of generating innovative outputs, digital open innovation platform owners face several challenges in orchestrating the open innovation process at scale. The role of governance approaches is highlighted as a useful lens to understand how digital platform owners can ensure adherence to rules of engagement while providing the degree of autonomy necessary for successful innovation efforts. The framework developed in this article suggests that the degree of centralization versus decentralization of governance on digital open innovation platforms is informed by the digital platform ownership model, innovation output ownership type, innovation engagement model, and innovation output.
The framework developed herein highlights that firm-owned digital open innovation platforms tend to favor centralized and authoritative forms of governance as this affords them the greatest amount of control over innovation outputs, the process by which they are selected, and the value that is derived from them. In contrast, market-based and community-based digital open innovation platforms tend to favor decentralized, less authoritative forms of governance that open up the benefits of the innovation outputs to a broader audience. Authoritative and centralized forms of governance manifest in digital open innovation platforms that seek to assume ownership of generated innovations, and decentralized forms of governance manifest when ownership belongs with participants. Finally, decentralized forms of governance emerge for innovation outputs requiring minimal expertise and time commitment and those requiring deep expertise under an embedded innovation engagement model. Moderately centralized governance approaches emerge where digital design innovation outputs requiring deep expertise are concerned.
With this conceptual foundation, two promising directions for future research are briefly highlighted. First, one of the principal challenges that digital platform owners face when it comes to governance of open innovation is maintaining a healthy balance between granting participants autonomy to engage in innovative activity via decentralized, less authoritative governance and the need to ensure their own outputs are achieved. The costs of achieving such balance increase substantially as innovation activity scales up. As observed in the synthesis of the governance literature and development of the framework, social currency appears to be the most prominent mechanism for enacting decentralized governance. Unfortunately, research suggests that such mechanisms can have unintended consequences that are counterproductive to the innovation objective. For instance, Yuan and Zhou (2008) found that the presence of individuals who have accumulated plenty of social currency can lead participants with less social currency to self-censor, reducing the number of contributions they make to the innovation process. Reitzig and Sorenson (2013) and Keum and See (2017) observed similar findings regarding the limited contributions of participants with lower social currency when in the presence of those with higher social currency. Additionally, participants in open innovation have been found to favor and stick to their own ideas, giving little regard to others’ ideas, including those of higher quality (See, Morrison, Rothman, & Soll, 2011; Soll & Larrick, 2009). Algorithms have potential to be an effective mechanism to facilitate open innovation at scale in a way that alleviates such biases. An important research question to motivate future research in this domain is: What role do algorithms play in facilitating open innovation in decentralized governance regimes? This as a promising avenue for future research because algorithms have the capacity to act with precision and speed at large scale and can be designed to circumvent the psychological biases that afflict participants in the open innovation process.
Finally, a robust set of algorithms is emerging to coordinate open innovation processes. Although still in its nascent stages preliminary research has begun to identify algorithms involved in the orchestration of open innovation in the context of open-source software development (Hukal, Berente, Germonprez, & Schecter, 2019). This leads to two research questions. First, what are the different purposes that algorithms can serve in supporting open innovation? This question recognizes that algorithms can be designed to possess different underlying logics that give them their purpose. Open innovation involves multiple activities for which algorithms may be well suited in supporting participant activities. Illuminating these activities and the role that algorithms can play in executing or supporting them is an avenue ripe for exploration. Second, what do those purposes look like across different platform ownership types and objectives? The second research question recognizes that, across different digital open innovation platforms—each with their own objectives, underlying rules, and constraints—the activities that are necessary to achieve desirable innovation outputs are likely to differ. There is an opportunity to enrich understanding of open innovation at a broader level by uncovering the nuances of algorithmically supported innovation across different open innovation contexts.
As suggested by the research questions posed, the future of digital open innovation platforms is algorithmic, involving human participants working with algorithms and with each other to achieve innovation outputs.
Ågerfalk, P. J., & Fitzgerald, B. (2008). Outsourcing to an unknown workforce: Exploring opensourcing as a global sourcing strategy. MIS Quarterly, 32(2), 385–409.Find this resource:
August, T., Shin, H., & Tunca, T. I. (2017). Generating value through open source: Software service market regulation and licensing policy. Information Systems Research, 29(1), 186–205.Find this resource:
Bauer, J., Franke, N., & Tuertscher, P. (2016). Intellectual property norms in online communities: How user-organized intellectual property regulation supports innovation. Information Systems Research, 27(4), 724–750.Find this resource:
Bayus, B. L. (2013). Crowdsourcing new product ideas over time: An analysis of the Dell Ideastorm community. Management Science, 59(1), 226–244.Find this resource:
Boudreau, K. J. (2012). Let a thousand flowers bloom? An early look at large numbers of software app developers and patterns of innovation. Organization Science, 23(5), 1409–1427.Find this resource:
Brabham, D. C. (2010). Moving the crowd at Threadless: Motivations for participation in a crowdsourcing application. Information, Communication & Society, 13(8), 1122–1145.Find this resource:
Brown, A. E., & Grant, G. G. (2005). Framing the frameworks: A review of IT governance research. Communications of the Association for Information Systems, 15(1), 696–712.Find this resource:
Butler, B. S. (2001). Membership size, communication activity, and sustainability: A resource-based model of online social structures. Information Systems Research, 12(4), 346–362.Find this resource:
Butler, B. S., & Wang, X. (2012). The cross-purposes of cross-posting: Boundary reshaping behavior in online discussion communities. Information Systems Research, 23(3, Part 2), 993–1010.Find this resource:
Chesbrough, H., Vanhaverbeke, W., & West, J. (2006). Open innovation: Researching a new paradigm. New York, NY: Oxford University Press.Find this resource:
Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—Platforms and infrastructures in the digital age. Information Systems Research, 29(2), 381–400.Find this resource:
Dabbish, L., Stuart, C., Tsay, J., & Herbsleb, J. (2012). Social coding in Github: Transparency and collaboration in an open software repository. Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work: Toolkits and Software Development (pp. 1277–1286), Seattle, WA: Association for Computing Machinery.Find this resource:
Dahl, D. W., Fuchs, C., & Schreier, M. (2015). Why and when consumers prefer products of user-driven firms: A social identification account. Management Science, 61(8), 1978–1988.Find this resource:
Dahlander, L., & Frederiksen, L. (2012). The core and cosmopolitans: A relational view of innovation in user communities. Organization Science, 23(4), 988–1007.Find this resource:
Daniel, S. L., Maruping, L. M., Cataldo, M., & Herbsleb, J. (2018). The impact of ideology misfit on open source software communities and companies. MIS Quarterly, 42(4), 1069–1096.Find this resource:
Di Gangi, P. M., Wasko, M. M., & Hooker, R. E. (2010). Getting customers’ ideas to work for you: Learning from Dell how to succeed with online user innovation communities. MIS Quarterly Executive, 9(4), 213–228.Find this resource:
Dong, J. Q., & Wu, W. (2015). Business value of social media technologies: Evidence from online user innovation communities. The Journal of Strategic Information Systems, 24(2), 113–127.Find this resource:
Faraj, S., Jarvenpaa, S. L., & Majchrzak, A. (2011). Knowledge collaboration in online communities. Organization Science, 22(5), 1224–1239.Find this resource:
Faraj, S., & Shimizu, T. (2018). Online communities and knowledge collaborations. In R. Aldag (Ed.), Oxford research encyclopedia of business and management (pp. 1–18). Oxford, UK: Oxford University Press.Find this resource:
Faraj, S., von Krogh, G., Monteiro, E., & Lakhani, K. R. (2016). Special section introduction—Online community as space for knowledge flows. Information Systems Research, 27(4), 668–684.Find this resource:
Felin, T., & Zenger, T. R. (2014). Closed or open innovation? Problem solving and the governance choice. Research Policy, 43(5), 914–925.Find this resource:
Franke, N., Keinz, P., & Klausberger, K. (2013). “Does this sound like a fair deal?”: Antecedents and consequences of fairness expectations in the individual’s decision to participate in firm innovation. Organization Science, 24(5), 1495–1516.Find this resource:
Franke, N., & Piller, F. (2004). Value creation by toolkits for user innovation and design: The case of the watch market. Journal of Product Innovation Management, 21(6), 401–415.Find this resource:
Franke, N., & Von Hippel, E. (2003). Satisfying heterogeneous user needs via innovation toolkits: The case of Apache Security Software. Research Policy, 32(7), 1199–1215.Find this resource:
Füller, J., Hutter, K., Hautz, J., & Matzler, K. (2014). User roles and contributions in innovation-contest communities. Journal of Management Information Systems, 31(1), 273–308.Find this resource:
Gallaugher, J., & Ransbotham, S. (2010). Social media and customer dialog management at Starbucks. MIS Quarterly Executive, 9(4), 197–212.Find this resource:
Gizaw, A. A., Bygstad, B., & Nielsen, P. (2017). Open generification. Information Systems Journal, 27(5), 619–642.Find this resource:
Gu, B., Konana, P., Rajagopalan, B., & Chen, H.-W. M. (2007). Competition among virtual communities and user valuation: The case of investing-related communities. Information Systems Research, 18(1), 68–85.Find this resource:
Haefliger, S., Von Krogh, G., & Spaeth, S. (2008). Code reuse in open source software. Management Science, 54(1), 180–193.Find this resource:
Hann, I.-H., Roberts, J. A., & Slaughter, S. A. (2013). All are not equal: An examination of the economic returns to different forms of participation in open source software communities. Information Systems Research, 24(3), 520–538.Find this resource:
Howison, J., & Crowston, K. (2014). Collaboration through open superposition: A theory of the open source way. MIS Quarterly, 38(1), 29–50.Find this resource:
Huber, T. L., Kude, T., & Dibbern, J. (2017). Governance practices in platform ecosystems: Navigating tensions between cocreated value and governance costs. Information Systems Research, 28(3), 563–584.Find this resource:
Hukal, P., Berente, N., Germonprez, M., & Schecter, A. (2019). Bots coordinating work in open source software projects. IEEE Computer, 52(9), 52–60.Find this resource:
Hutter, K., Füller, J., Hautz, J., Bilgram, V., & Matzler, K. (2015). Machiavellianism or morality: Which behavior pays off in online innovation contests? Journal of Management Information Systems, 32(3), 197–228.Find this resource:
Hwang, E. H., Singh, P. V., & Argote, L. (2019). Jack of all, master of some: Information network and innovation in crowdsourcing communities. Information Systems Research, 30(2), 389–410.Find this resource:
Jeppesen, L. B. (2005). User toolkits for innovation: Consumers support each other. Journal of Product Innovation Management, 22(4), 347–362.Find this resource:
Jeppesen, L. B., & Frederiksen, L. (2006). Why do users contribute to firm-hosted user communities? The case of computer-controlled music instruments. Organization Science, 17(1), 45–63.Find this resource:
Jeppesen, L. B., & Lakhani, K. R. (2010). Marginality and problem-solving effectiveness in broadcast search. Organization Science, 21(5), 1016–1033.Find this resource:
Johnson, S. L., Faraj, S., & Kudaravalli, S. (2014). Emergence of power laws in online communities: The role of social mechanisms and preferential attachment. MIS Quarterly, 38(3), 795–808.Find this resource:
Kallinikos, J., & Tempini, N. (2014). Patient data as medical facts: Social media practices as a foundation for medical knowledge creation. Information Systems Research, 25(4), 817–833.Find this resource:
Kane, G. C., & Ransbotham, S. (2016). Research note—Content and collaboration: An affiliation network approach to information quality in online peer production communities. Information Systems Research, 27(2), 424–439.Find this resource:
Kankanhalli, A., Ye, H. J., & Teo, H. H. (2015). Comparing potential and actual innovators: An empirical study of mobile data services innovation. MIS Quarterly, 39(3), 667–682.Find this resource:
Karhu, K., Gustafsson, R., & Lyytinen, K. (2018). Exploiting and defending open digital platforms with boundary resources: Android’s five platform forks. Information Systems Research, 29(2), 479–497.Find this resource:
Ketter, W., Peters, M., Collins, J., & Gupta, A. (2016). A multiagent competitive gaming platform to address societal challenges. MIS Quarterly: Management Information Systems, 40(2), 447–460.Find this resource:
Keum, D. D., & See, K. E. (2017). The influence of hierarchy on idea generation and selection in the innovation process. Organization Science, 28(4), 653–669.Find this resource:
King, A., & Lakhani, K. R. (2013). Using open innovation to identify the best ideas. MIT Sloan Management Review (Fall), 55(1), 41–48.Find this resource:
King, K. (2019). Why IBM embraces is embracing, expanding open hardware ecosystem in wake of Red Hat deal. WRAL TechWire.Find this resource:
Koh, T. K. (2019). Adopting seekers’ solution exemplars in crowdsourcing ideation contests: Antecedents and consequences. Information Systems Research, 30(2), 486–506.Find this resource:
Kyriakou, H., Nickerson, J. V., & Sabnis, G. (2017). Knowledge reuse for customization: Metamodels in an open design community for 3D printing. MIS Quarterly, 41(1), 315–332.Find this resource:
Lakhani, K. R. (2009). InnoCentive A: Harvard Business School Case 608–170. Boston, MA: Harvard Business School.Find this resource:
Lakhani, K. R., Lifshitz-Assaf, H., & Tushman, M. (2013). Open innovation and organizational boundaries: Task decomposition, knowledge distribution and the locus of innovation. In A. Grandori (Ed.), Handbook of economic organization: Integrating economic and organizational theory (pp. 355–382). Northampton, MA: Edward Elgar Publishing.Find this resource:
Lee, G. K., & Cole, R. E. (2003). From a firm-based to a community-based model of knowledge creation: The case of the Linux kernel development. Organization Science, 14(6), 633–649.Find this resource:
Lee, H. C. B., Ba, S., Li, X., & Stallaert, J. (2018). Salience bias in crowdsourcing contests. Information Systems Research, 29(2), 401–418.Find this resource:
Lerner, J., & Tirole, J. (2005). The scope of open source licensing. Journal of Law, Economics, and Organization, 21(1), 20–56.Find this resource:
Majchrzak, A., & Malhotra, A. (2016). Effect of knowledge-sharing trajectories on innovative outcomes in temporary online crowds. Information Systems Research, 27(4), 685–703.Find this resource:
Malhotra, A., & Majchrzak, A. (2014). Managing crowds in innovation challenges. California Management Review, 56(4), 103–123.Find this resource:
Maruping, L. M., Daniel, S. L., & Cataldo, M. (2019). Developer centrality and the impact of value congruence and incongruence on commitment and code contribution activity in open source software communities. MIS Quarterly, 43(3), 951–976.Find this resource:
Medappa, P. K., & Srivastava, S. C. (2019). Does superposition influence the success of floss projects? An examination of open-source software development by organizations and individuals. Information Systems Research, 30(3), 764–786.Find this resource:
Nambisan, S., & Baron, R. A. (2010). Different roles, different strokes: Organizing virtual customer environments to promote two types of customer contributions. Organization Science, 21(2), 554–572.Find this resource:
Oh, W., Moon, J. Y., Hahn, J., & Kim, T. (2016). Research note—Leader influence on sustained participation in online collaborative work communities: A simulation-based approach. Information Systems Research, 27(2), 383–402.Find this resource:
Parker, G., & Van Alstyne, M. (2018). Innovation, openness, and platform control. Management Science, 64(7), 3015–3032.Find this resource:
Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform revolution: How networked markets are transforming the economy–And how to make them work for you. New York, NY: W.W. Norton.Find this resource:
Reitzig, M., & Sorenson, O. (2013). Biases in the selection stage of bottom‐up strategy formulation. Strategic Management Journal, 34(7), 782–799.Find this resource:
Roberts, J. A., Hann, I.-H., & Slaughter, S. A. (2006). Understanding the motivations, participation, and performance of open source software developers: A longitudinal study of the Apache projects. Management Science, 52(7), 984–999.Find this resource:
Schlagwein, D., & Bjørn-Andersen, N. (2014). Organizational learning with crowdsourcing: The revelatory case of Lego. Journal of the Association for Information Systems, 15(11), 754–778.Find this resource:
See, K. E., Morrison, E. W., Rothman, N. B., & Soll, J. B. (2011). The detrimental effects of power on confidence, advice taking, and accuracy. Organizational Behavior and Human Decision Processes, 116(2), 272–285.Find this resource:
Shaikh, M., & Henfridsson, O. (2017). Governing open source software through coordination processes. Information and Organization, 27(2), 116–135.Find this resource:
Shneiderman, B. (2007). Creativity support tools: Accelerating discovery and innovation. Communications of the ACM, 50(12), 20–32.Find this resource:
Singh, P. V., & Phelps, C. (2012). Networks, social influence, and the choice among competing innovations: Insights from open source software licenses. Information Systems Research, 24(3), 539–560.Find this resource:
Singh, P. V., Tan, Y., & Mookerjee, V. (2011). Network effects: The influence of structural capital on open source project success. MIS Quarterly, 35(4), 813–829.Find this resource:
Soll, J. B., & Larrick, R. P. (2009). Strategies for revising judgment: How (and how well) people use others’ opinions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(3), 780.Find this resource:
Song, P., Xue, L., Rai, A., & Zhang, C. (2018). The ecosystem of software platform: A study of asymmetric cross-side network effects and platform governance. MIS Quarterly, 42(1), 121–142.Find this resource:
Stanko, M. A. (2016). Toward a theory of remixing in online innovation communities. Information Systems Research, 27(4), 773–791.Find this resource:
Stewart, K. J., Ammeter, A. P., & Maruping, L. M. (2006). Impacts of license choice and organizational sponsorship on user interest and development activity in open source software projects. Information Systems Research, 17(2), 126–144.Find this resource:
Stewart, K. J., & Gosain, S. (2006). The impact of ideology on effectiveness in open source software development teams. MIS Quarterly, 30(2), 291–314.Find this resource:
Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. New York, NY: Doubleday.Find this resource:
Tiwana, A., Konsynski, B., & Bush, A. A. (2010). Platform evolution: Coevolution of platform architecture, governance, and environmental dynamics. Information Systems Research, 21(4), 675–687.Find this resource:
West, J., & Bogers, M. (2014). Leveraging external sources of innovation: A review of research on open innovation. Journal of Product Innovation Management, 31(4), 814–831.Find this resource:
Weinstein, P. V. (2018). Why Microsoft is willing to pay so much for GitHub. Harvard Business Review.Find this resource:
Yuan, F., & Zhou, J. (2008). Differential effects of expected external evaluation on different parts of the creative idea production process and on final product creativity. Creativity Research Journal, 20(4), 391–403.Find this resource:
Zhang, C., Hahn, J., & De, P. (2013). Research note—Continued participation in online innovation communities: Does community response matter equally for everyone? Information Systems Research, 24(4), 1112–1130.Find this resource:
Zhang, S., Singh, P. V., & Ghose, A. (2019). A structural analysis of the role of superstars in crowdsourcing contests. Information Systems Research, 30(1), 15–33.Find this resource:
Zheng, H., Xu, B., Zhang, M., & Wang, T. (2018). Sponsor’s cocreation and psychological ownership in reward‐based crowdfunding. Information Systems Journal, 28(6), 1213–1238.Find this resource: