The emerging technology of virtual worlds enabling avatar-based innovation
Virtual Worlds like Second Life are computer-generated physical spaces, represented graphically in 3D that can be experienced by many users, or so-called avatars, at once. They may herald the next leap of evolution for open innovation because of two main rationales: First, incorporating the latest technological advances of virtual worlds into open innovation practice enriches existing Web-based customer integration methods by allowing real-time, media-rich and highly interactive collaboration between organizations and consumers. Second, virtual worlds build on a new mode of production where the host firm facilitates unrestrained consumer freedom and empowerment. The playful user-generated worlds resemble engines of creation that provide the freedom to experiment and lead to unprecedented rates of innovation.

Several companies have already tried to leverage avatar-based innovation especially within Second Life and asked residents to engage in different innovation activities along various stages of the innovation process. For example, Osram, a light manufacturer, started an idea contest and invited Second Life residents to contribute ideas on the topic of lightning. Toyota Scion launched a virtual car model and encouraged participants to modify and customize their cars. Another example is the case of Aloft, a new hotel concept from Starwood Hotels. Before the real hotel was built, a virtual mock-up was discussed, evaluated, modified, and further developed in Second Life. Based on the feedback, several changes to the overall design of Aloft’s virtual and physical hotel resulted (Kohler et al. 2009).

Lack of participation and the need to create compelling experiences
Regardless of the promising opportunities provided by avatar-based innovation, one major challenge impeding its development is the lack of interest in corporate projects among avatars and the failure of these platforms to provide a compelling experience for the visiting avatars. The overwhelming majority of avatar-based innovation pioneers is challenged by too few interested participants and, therefore, too few activities that make the place a vibrant source of great connections and innovations. To find out how companies can effectively use virtual worlds for open innovation to collaborate with creative consumers, we launched the Ideation Quest initiative.

The ideation quest in Second Life

Together with KTM and Philips Design we created an interactive idea competition that took place within an immersive virtual world experience within Second Life. The third and most recent project called the Green Ideation Quest attracted 266 participants who spend an average of 85 minutes to discuss a greener future and generate ideas, which were then evaluated by the community of participants. The trailer at http://tiny.cc/eSooS gives an overview on the process users went through.

The studies generated a number of lessons. The first set of implications refers to embracing avatar-based innovation. Using the latest technological advances can help leverage a firm’s innovation process, both by harvesting the medium-related benefits, and by tapping into avatars’ creativity. The second implication relates to the need for open innovation practitioners to fully acknowledge the importance of the co-creation experience. If companies decide to use an avatar-based innovation strategy, they must recognize that an invitation for avatars to actively participate in co-creation is not enough. Present corporate activities in virtual worlds imply that the critical challenge to use the emerging technology is not so much in devising the technological infrastructure but in creating and maintaining a rewarding experience for visitors. Companies need to go beyond implementing the visual presence and instead seek to actively engage customers by recognizing their motivations and delivering benefit to them. The third set of lessons refers to the strategies that promise success in facilitating compelling experiences. By focusing on hedonic aspects, leveraging the social nature of virtual worlds and co-creating the open innovation project itself, companies can enhance their open innovation efforts by engaging and co-creating with avatars – the latest visual representation of their potential or actual customers.

Recommended reading
Kohler, T., Matzler, K. and Füller J. (2009). Avatar-based innovation: using virtual worlds for real-world innovation, Technovation, 29, 395-407.

Kohler, T./Füller J./Matzler K./Stieger D. (2010). Avatar-based Innovation: Consequences of virtual co-creation experiences: HICSS-37, Hawaii, January 5-8.

Kohler, T./Füller J./Matzler K./Stieger D. (Forthcoming), Co-creation in virtual worlds: the design of the user experience, MISQ.

Exemplifying displacement in progress
Let me, for a moment, shed light on Louis Pasteur and his work with microorganisms. In the end of the nineteenth century Pasteur´s laboratory turned its attention to finding a preventive to the disease anthrax. As Latour (1983) and Cooper (2002) tell us, Pasteur takes his laboratory to a farm in the French countryside. He and his colleagues are out in the field, learning from the farmers´ knowledge of the anthrax bacillus and how it affects the animals. After a period, he brings his new gained knowledge back in the laboratory. Back in the laboratory he is able to grow the bacillus in isolation and he is now able to simulate anthrax outbreaks in his laboratory. But, as Copper (2003) stresses, it was still necessary to make a further displacement (now in form of a vaccine) to the farm to demonstrate and test it effectiveness.
What this short example illustrates is that open innovation is neither a new way for making top results in a business, nor making possible of thinking in terms of discrete variables like internal and external R&D or indoor knowledge and “smart” people outside the company.

Innovation will always be innovation..
To take advantage in the new age is to set up a laboratory, to displace information, to be humble about the conflation of notions of internal knowledge and “smart” people outside, as internal and external R&D. The winners in the future might not be the companies with the smartest brains or even the organizations that locate the smartest brains in the cyberspace or in the market. The winners are people and organizations that successfully can set up laboratories and practice displacement in large scale. Displacement, however, is not a new idea. One may even argue that Chesbrough´s thoughts and arguments are just old wine in new bottles. Nevertheless – I would say - new bottles are not to be despised. As the market changes, bottles need to change with it.

Recommended reading
Chesbrough, H. (2003). The Open Innovation Paradigm, in Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press: Boston. p. 43-62.

Cooper, R. (1992). Formal Organization as Representation: Remote Control, Displacement and Abbreviation, in M. Reed and M. Hughet (eds.) Rethinking Organization. London: Sage.

Latour, B. (1983). Give me a laboratory and I will raise the world, in K. Knorr-Cetina and M. Mulkay (eds.) . Science observed: Perspectives on the Social Study of Science. London. Sage Publication.

Fundamentally, the social world is created and recreated through human actions. Most actions are social: “That action will be called social which in its meaning as intended by the actor or actors, takes account of the behavior of others and is thereby oriented in its course” (Weber, 1978, p. 4). This suggests that a social action has both social grounds and social purposes. As claimed by Mead (1934, p 6) “the behavior of an individual can be understood only in terms of the whole social group of which he is member, since his individual acts are involved in larger social acts, which go beyond himself and which implicate the other members of the group.” It is thus essential to look both for social grounds and social purposes when studying actions. Social grounds form the history of an action. Social actions are thus interactive where several inter-related actions constitute patterns of actions related to each other by initiatives and responses (Linell, 1998). One single action can be both an initiative and a response. A human being intervenes in the world, by performing actions, in order to create some differences in their environment—to achieve ends. An important distinction can be made between the result and the effects of an action (von Wright, 1971): while the actor is in control of the immediate results of their actions, the long-term effects may be hard to predict.

The philosophical orientation of pragmatics puts attention to peoples’ use of language and the knowledge about interaction in solving basic problems of communication. This means a focus upon meaning, action, and coherence. In ordinary interaction these are managed through constraints participants impose on their interaction through linguistic and non-linguistic means, especially as these frame allowable contributions to ongoing activity (Levinson, 1979). Constraints arise as participants shape their actions to contribute to a purpose and interpret others’ actions relative to that joint purpose (Levinson, 1979). Constraints include who can participate and what identify they can project, the allocation of turns and types of turns, topics, and so on (Drew and Heritage, 1993; Levinson, 1979). Purpose can be joint goals, or shared identities, that vary in explicitness and formality as in the way chat, business meeting, negotiation, and a judicial hearing differ from each other (Drew and Heritage, 1993; Levinson, 1979).

Constraints can also be an object of intentional design as people create and re-configure the ways they meet and gather and as technologies and procedures are developed to support various kinds of communication. In the history of work and management, for instance, the nature, role, and relationship of types of meetings workers have with each other and with management have been an important part of management (Yates, 1989). Meetings, encounters, and the activities these are to produce are the object of design in many theories of organizational design. These micro-matters are the space where disruptive technology creates shifts and thus struggles over the role and nature of encounters and meetings in the workplace (who should be involved, what should be discussed, topic, relevant contributions, how to solve differences, and the like).[quote]

The uses of social media, and other IT, to engage various stakeholders provide accordances and constraints for interaction between an organization and its stakeholders. These technologies are actable—that is, they are tools for stakeholders to act relative to the organization and each other. Of course, the technology only supports certain actions and fosters certain forms of interactivity. It is in the design of these technologies that organizations signal their relations to stakeholders. The design of the technology and what action it makes possible (or impossible) reveals practical pragmatic theories for organizational communication. The design of the technology, and especially what it presupposes about organizational communication, can be a source of innovation and struggle. Pragmatics provides grounding for understanding this aspect of organizational communication as it relates to open innovation through social media.

We put forward the following concepts, drawn from Aakhus (2002) and de Moor and Aakhus (2006), to guide the empirical task of reconstructing the practical pragmatic theories evident in managerial responses to social media:
• Exigence: Activities exist relative to some exigency which is the framing of the “something needed to be done,” the audience, and the constraints on the situation.
• Purpose: Activities have some overriding purpose (or hierarchy of purposes) that address (or have the potential to address) the exigence as conceived.
• Orchestration: Activities have aspects that can be characterized in terms of features of interactivity. This includes features, such as:
o Types of contributions
o Sequencing of contributions
o Roles and participation status
o Networks and the input‐output relationships among ‘conversations’.
• Rationale: Activities have a rationale that links the ostensible means‐ends relationship between the process and product of interaction. Whether that link is actual or symbolic matters but for a variety of instrumental and non‐instrumental reasons.

Researching the role of social media in open innovation calls for diversity in the application of research methods (Langley, 1999; Romano, 2003). A research programme based on the concepts laid out above is seen as a pragmatic means for empirical and design oriented research into open innovation through social media. Such research would be highly influenced by studying different instances of interactions related to the disruptive nature of social media by considering the elements that make up a social media infrastructure (e.g., in addition to the technology, the policy statements, interviews, news accounts, agreements of different kinds, such as non‐disclosure agreements, explicit and informal work agreements and arrangements between groups and organizations) as well as blog commentaries.

We are currently in the process of systematically collecting actual cases of managerial responses to social media and analyzing these by use of the pragmatic approach discussed here. We aim to develop further our explanatory mechanism that distinguishes how managerial responses vary to include why these responses vary. We assume that has to do with practical theories about how interaction processes lead to outcomes, such as new innovations. The ambition is to develop practical theories for informing organizations in their task of developing an attitude and strategies for using the potential of social media in their interaction with various stakeholders. Our hope is that such an orientation will generate relevant empirical data and help develop a reflective attitude towards open innovation through social media by people taking part in such interaction. The results of this endeavor will hopefully appear in the near future as Part III of this article series.

Recommended reading
Aakhus, M. (2002). Modeling reconstruction in groupware technology. In F. H. van Eemeren (Ed.), Advances in pragma-dialectics (pp. 121¬–126). Newport News, VA: Vale Press.

de Moor, A. & Aakhus, M. (2006). Argument Support: From Technologies to Tools. Communications of the ACM, 49(3), 93–98.

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Open innovation initiatives and platforms are closely interlinked with the social media phenomenon. It is by leveraging Web 2.0 technologies that, for instance, Dell and Starbucks have managed to open up their innovation processes and provided structures for involving customers more directly in their product development through what has become known as crowdsourcing (Howe, 2006). These platforms go beyond mere call for feature requests as they provide for users (i.e. customers) to actively propose, discuss, evaluate and rank ideas and solutions. Other companies, such as Moog Music and IKEA, utilise user forums to achieve some of the same benefits. In these cases customers are allowed freely (albeit moderated by the company) to discuss issues related to the company’s products. This sometimes results in feature requests being proposed although the main purpose of such forums seems to be customers’ taking care of customer support by helping each other to solve product related problems. Yet some companies, such as Lego, has taken a step further and released (parts of) their product portfolio as open source, thus letting their customers into the core of product innovation and design (Ågerfalk & Fitzgerald, 2008). Certainly, conversations similar to those in the company-controlled forums also happen in other corners of the social media space. Examples include third-party consumer information and product ranking sites, such as Pricerunner, non-company specific forums, such as KVR Audio, and independent image and video sharing services, such as Flickr and YouTube, not to mention Twitter, Facebook and the blogosphere were just about everything gets scrutinized. Thus, we can distinguish at least four modes of open innovation through social media with varying degrees of stakeholder interaction: opensourcing, crowdsourcing, discussion forums, and public discourse. These modes may be controlled by the company itself, by a third-party or be user-led.[quote]
Given the many alternatives for how to tap into and harness the Web 2.0 “wisdom of the crowd”, it is not surprising that many organizations are struggling with how to respond to the social media phenomenon in order to reap its benefits (according to Gartner a top ten strategic priority of organizations currently). In fact, several organizations have experienced serious inconvenience because of social media exposure brought about by creative, and sometimes displeased customers. In some of these cases, the company used the very same media to respond successfully to diverse incidents (e.g. The Domino’s Pizza case). In other cases, companies responded more or less with ignorance (e.g. the attempt to ban mobile phones at US college football games and the United Breaks Guitars case). Social media create new possibilities for interaction. In so doing, these media invite innovations and struggles over how stakeholders ought to interact with each other. Yet, the models for how all these stakeholder should stand in relation to each other and interact (or not) primarily arose with the large hierarchical organizations of the 20th century. These models are now being challenged. What then, can we as researchers do to help remedy this situation? By understanding better managerial response to social media, we would argue.

The emergence of open innovation through social media is similar to earlier disruptive innovations (Bower and Christensen, 1995), yet different in some key respects. First, to most organizations social media as such do not form part of the core business (if they are not software manufacturers). Rather, these technologies have evolved along with the development of service-based Internet infrastructures to become a threat or an indispensable asset for companies to communicate and interact, internally and externally. Second, the impact of social media on an organization is not based on market demand. Rather, as pointed out by Lyytinen and Rose (2003), disruptive information systems innovations tend to be based on pull strategies rather than push. That is, they emerge from within the organization. Third, it seems that social media become disruptive as they are adopted through a boot-strapping approach which builds momentum primarily due to an increasing number of users of the specific media rather than inherent features in the technology itself. Thus, they are “infrastructural innovations” (Lyytinen and Rose, 2003) but with an emphasis on social momentum as a driver for disruption rather than a giant leap in underlying technology.
In order to understand how companies respond (and ought to respond) to social media and open innovation, we must first understand what it means to be social and how such an understanding can help to recognize how social media shape companies’ on-line presence and ongoing conversations with various stakeholders. We assume that managerial response to social media can be grounded in practical theories of pragmatics, emphasizing action and communication that make assumptions about how communication works and how it ought to work (Aakhus, 2007). We will explore further these assumptions and their implications in Part II of this article series—Stay tuned!

Recommended reading
Aakhus, M. (2007). Communication as design. Communication Monographs, 74(1), 112–117.

Ågerfalk P. J. & Fitzgerald B. (2008). Outsourcing to an Unknown Workforce: Exploring Opensourcing as an Offshore Sourcing Strategy, MIS Quarterly, 32(2), pp. 385–409.

Bower J. L. & Christensen C. M. (1995). Disruptive Technologies: Catching the Wave, Harvard Business Review, 73(1), 43–53.

Howe, J. (2006). The Rise of Crowdsourcing, Wired Magazine, 14(6).

Lyytinen, K. & Rose, M. G. (2003). The Disruptive Nature of Information Technology Innovations: The case of Internet Computing in Systems Development Organizations. MIS Quarterly, 27(4), 557–595.

[quote]Despite that it is widely acknowledged that technologies are of key importance in open innovation practices (Chesbrough, 2003), little research has studied how various technologies can be strategically used to enable and support open innovation practices (Dodgson, Gann, & Salter, 2005). For example, Chesbrough (2003) argues that new technologies can support open innovation, but do not explore how, for what reason, and to what extent different information technologies can be used when innovation is desired (Dodgson, et al., 2005).

Drawing on the above, in Hrastinski et al. (2010), we explored how current open innovation software (OIS) are designed, by whom they are used and reflect on their potential to support open innovation processes.

The current focus is on idea collection

We classified a sample of 51 systems and found that most OIS are not very innovative in themselves because they provide similar features. Typically, users are asked to describe an idea or the management specifies a problem to be solved. Then, a phase that includes problem solving and evaluation in collaboration follows. In successful cases, these processes lead or contribute to an innovation.

Our review reveals that a vast majority of OIS focuses on the front end of open innovation through an emphasis on the collection of ideas or problem solutions requested by the management of technology industries.

Four types of open innovation software

When scrutinizing the reviewed OIS, few attempted to take a step towards OIS that support more than the collection of ideas. Drawing on the reviewed software, we suggest a classification, which includes four types of OIS. The four types of OIS can be used by organizations to reflect on how they are using IT to support open innovation, and most importantly, how they could use IT to support open innovation in new and innovative ways in the future.

Most innovation happens at the boundaries between disciplines or specializations. It is when people meet across the boundaries that new knowledge is generated or integrated and new innovations comes up. We know this, but we also know the relative complexity to manage innovative processes at a given boundary. The researcher Carlile has spent a lot of time in many papers to explore and investigate this complexity. We argue that by understanding this complexity we might be able to better manage these processes.

Carlile proposes that we should shed light on three different properties of knowledge at boundaries; difference, dependence and novelty (Carlile and Rebentisch 2003, Carlile 2004). Differences in knowledge refer to a difference in the amount of accumulated knowledge. And this is a dilemma. Creating a complex service (i.e. innovation), for example, often requires differences in the amount and type of knowledge. At the same time, practically, it means that different actors have different experiences, different terminologies, different incentives, etc. Furthermore, every actor has to re-learn. This might have a negative impact of the willingness of an actor to participate in an innovative process. Nevertheless, these processes need to be overcome.

The second relational property of knowledge at a boundary is dependence. To be able to manage innovative processes we need to take into account how different actors and their activities are dependent on each other. As Carlile (2004: 556) points out - “Without dependence, difference is of no consequence”. Dependence can, for example, be described in political terms, i.e. are actors willing to participate in innovative processes because of situated dependence? Furthermore, how will innovative processes change dependence between actors or processes?

The third relational property of knowledge at a boundary is how novel the circumstances are. As Carlile writes; “… the most challenging aspect of the relational nature of knowledge at a boundary is that for each actor there is novelty to share with others and novelty to assess from others.” To be able to manage innovative processes we must be aware of that when novelty arises there is often a lack of common knowledge to adequately share an assess knowledge at a boundary. An innovative thought might, for example, be regarded with suspicion and insecurity, not because the idea is not of great value, but that there is a lack of language to catch the innovation with.

Our approach and analysis may indeed be regarded as complex and abstract. However, by illuminating what happens at the boundaries between disciplines and specializations may help us to understand and develop an innovative climate in our organizations. By focusing on the three properties of boundaries we may open a window to further understanding about how the flux and flow of organizational processes can be arrested in concepts and translated into pragmatic use. What could be more instrumentally usable?

Recommended reading
Carlile, P., and E. Rebentisch (2003) Into the black box: The Knowledge transformation cycle, Management Science. Vol. 49, pp 1180-1195.

Carlile, P. (2004), Transferring, translating, and transforming: An integrative framework for managing knowledge across boundaries, Organization Science Vol. 15 No. 5, pp. 555-568.

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The research group behind Open Innovation Forum has organized a track on “Collaboration Systems for Open Innovation” at one of the most prestigious conferences on applied IT: The Hawaii International Conference on Systems Sciences. In this paper, Prof. Frank Piller, a well-known researcher in the open innovation field, summarizes a research paper that will be presented in our track. It is nominated for the best paper of the conference.

Product definition has been shown to be critical to new product success
User satisfaction (and, thus, adoption) with a new product regularly increases with the degree of fit between a user’s needs and the characteristics of a product. Conventionally, the manufacturer tries to understand this causal network with detailed information either acquired via market research or assumed via professional knowledge. Previous research has shown, however, that many companies fail to gather this required input in an efficient and effective way: Despite ever increasing methodological knowledge in market research, new product development (NPD) flop rates continue to rise.[quote]

MIT researcher Eric Von Hippel has explained the problem of firms to understand what customers really want by the stickiness of need information. Sticky information is context specific and difficult to formalize and transfer. Stickiness of customer (need) information is a function of multiple factors, including characteristics inherent in the information itself, such as the way information is encoded. Customers often use a different language to describe their demands and think in different design parameters than the manufacturer. As a result, firms regularly have to change design parameters as the requirements (representing information about the customers’ needs) are not stable during NPD. Even in highly specialized industrial markets, customers face an inherent difficulty in accurately specifying their needs at the outset of a NPD project, resulting in a co-evolution of the technological solution with the revision of customers’ articulated needs. Research further has shown that familiarity with existing product attributes can interfere with an individual’s ability to express needs for novel products. Needs become more refined as users come in direct contact with (a prototype of) a new product.

Embedded toolkits as enablers for open innovation
In our paper, my co-authors Christoph Ihl and Frank Steiner and I propose a new approach to accomplish this objective by applying a typical process of user innovation as a form of open innovation: embedded toolkits for user co-design.

Our idea is to shift some specifications of the product into the domain of the user. This strategy isolates the source of uncertainty, i.e. sticky information about user needs. A product with a respective toolkit should hence contain (1) a flexible architecture where the values for each design parameter are not fixed, but adaptable, (2) a set of rules about possible combinations of selection of values for each design parameter, and (3) an interface for individual users could differentiate the product according to their preferences by manipulating the values. Note that our idea is NOT to configure a product with an internet-based toolkit BEFORE it is being manufactured, as typical for a mass customization or build-to-order strategy. These configuration toolkits have already been subject of a rather intensive discussion ; and there are many examples of this strategy in practice (e.g., NikeID, Dell, BMW). In our concept, users shall be enabled to modify the product AFTER it has been manufactured and has reached the user domain. The key requirement of this concept is a user interface that allows users themselves to adapt a product according to their own requirements, hence addressing a core characteristic of open innovation with users. The embedded toolkit shall equip users with the possible solution capabilities to substitute the lack of professional training and experience.

In our concept, the toolkit is being embedded in the product architecture, allowing its real-time modification during the usage stage. Consider the example of a dashboard of an automobile. (To illustrate this example, watch this video with a mock-up prototype.)

A conventional interface allows the user just to interact with the car and to control specific predefined functionality (as demonstrated in the first segments of the video). In our approach, the user interaction could go much further. She could design the layout of the dashboard, the functions accessible by it, and also parts of the performance controlled by the customized dashboard (as demonstrated in Min. 3:05 and later in the video). Still, all functionality would stay within the safety requirements of the system and would allow full interface functionality with the rest of the car. Obviously, such a solution has much larger opportunities for users to find an exact product fitting to their needs, but demands much larger requirements in designing the solution space of such a flexible system. As demonstrated further in the video (last segment), the modifications of a user could be transferred to the manufacturer during a service check, providing input for the development of future product generations.

To test the idea of an embedded toolkit, we conducted a technology acceptance study that analyzed the feedback of 163 consumers on different scenarios of embedded toolkits in the automotive industry. Our results demonstrate the feasibility and the acceptance of the basic concept.

Recommended reading
Ihl, C., Piller F. & Steiner F. (2010). Embedded Toolkits for User Co-Design: A Technology Acceptance Study of Product Adaptability in the Usage Stage. Proceedings of the 43rd HICSS Conference, January 2010.

von Hippel, E. (1994). “Sticky Information” and the Locus of Problem Solving: Implications for Innovation. Management Science, 40(4), 429-439

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Main incentives are peer recognition and the value of [product] improvements
People initially join innovation networks because they are fascinated by the challenge and care deeply about the goals of their community. Their primary currency of reward is peer recognition. (Gloor, 2005) Other primary values for participants are to 1) build wider networks and connect to people with complementary business-critical competence, 2) build personal relationships with leaders in the field, which will improve their own productivity, 3) learn new skills and often find themselves promoted.

Open source software communities for example are composed of individuals who collaborate toward a common goal but do not share a common employer and are not governed by an employment hierarchy. According to the inventor of the open source software Apache Cocoon, the strongest driver for open source developers is to boost their ego by gaining a reputation as a supreme programmer. Even though the voluntary work in open source projects does not pay off immediately, it is considered a long-term career investment. In a large web-based study of Linux developers’ motivation, three main categories were found; 1) collective motives to help the community reach its goals, 2) peer recognition motives, to work with good people and to have them recognize your capabilities, 3) direct reward motives, such as learning, having fun or making money.

In a recent study performed by a research team at the Airport Living Lab the participating user innovators where questioned for the motivational drivers for participation. In this specific study all participants were employees at the airport but with different work roles. The single most popular incentive was “improving my own work situation”. Hence, when voluntary activity was an option to make their own situation better, they were well willing to work (to a certain limit) extra without personal financial gain to achieve a better situation for themselves, since this would improve their overall work experience. Also highly regarded, but still way behind the leading alternative, was “intellectually stimulating” and “collaborating collectively” while the incentive “financial compensation” was rated surprisingly low.

Innovation network participation may lead to long-term commitments due to lock-in effects
People who have joined a network and started collaborating with peers will be less likely to leave the community. There are primarily four reasons for this:

- Recognition. When interacting with peers there are increased likeliness of receiving positive feedback for distinguished traits of the participant. For many, it is important to interact in a community where your special knowledge is appreciated. Leaving the community would mean ending the recognition stimulus.
- Belonging. When relationships have been built within the network, both emotionally and professionally, they may all be lost if membership is discontinued. For very deep engagements, participants may even give up other external relationships that compete for participants’ available time.
- Investment. Once certain investments have been made to the community - may they be man-hours, monetary or skill - the investment made will not turn into profit if the network is abandoned.
- Decisiveness. When members have started working towards a mutual goal, they may get very absorbed by reaching targets so that backing out is not an option. One underlying reason may be simple stubbornness; another may be the engagement of actually fulfilling the goals, or solving the targeted problems, of the group - not for the sake of securing the profits but for the intrinsic purpose of finishing what has been started.

By joining innovation networks, participants find that they are able to get better faster by working with others in the networks rather than working on their own. Successful innovation networks must therefore focus on building long-term relationships with participants, creating opportunities for repeated interactions that demonstrate the value of cooperation.

Recommended reading
Ghazarian, N. (2009). User studies Within a Living Lab Context - Case Studies From Airport Living Lab. Master thesis in Computer Science, Uppsala University.

Gloor, P. A. (2005). Swarm Creativity - Competitive Advantage through Collaborative Innovation Networks. Oxford University Press.

Hagel III, J. & Seely Brown, J. (2006). Creation Nets: Harnessing the Potential of Open Innovation.

West J. & O’Mahoney, S. (2008) The Role of Participation Architecture in Growing Sponsored Open Source Communities.

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Theory can however be far away from practice. When studying rollouts of Open Innovation practices it is also necessary to seriously take a look at the perceived and/or real risks managers bring up. As in any business endeavor there are in fact several potential risks to employing Open Innovation.

Two perceived risks
At least two well articulated risks (or fears among managers) are brought up when studying business cases of Open Innovation activities. These risks represent two extremes of a continuum. The first risk becomes relevant if Open Innovation, as part of an organizations innovation strategy, is poorly executed. The second, and this may seem as a paradox at first sight, if Open Innovation is too well executed.

Risk 1: Creating one more suggestion box.
Here the Open Innovation project often gets limited attention and funding after the first energetic marketing phase is over – or the initiating manager leaves. The investment in Open Innovation practices, including educating and energizing the staff to really start co-creating, fades out. Initially active and interested staff looking forward to some involvement and attention from managers, file this effort as yet another management vogue. What is left behind is perhaps a little more than another suggestion box, with disillusioned staff and customers as the unintended result. Innovation becomes incremental at best.[quote]

One remedy would be to really treat Open Innovation as a new way of thinking about innovation that will involve some long-lasting effort, attention and resources dedicated to making a shift in innovation processes. This is naturally easier when current innovation processes are formally or informally agreed upon in the organization – something that in our experience not should be taken for granted.

This includes paying attention to creating a lasting infrastructure of innovation tools that really activates the right brain of the staff and triggers collaboration. In the Open Innovation Framework project we have recently evaluated 51 software systems designed to accomplish this (Hrastinski et al, 2009). Taking a look on how such low-cost and readily available support can help providing more than the typical “suggestion box” is recommended. We find them a positive step towards building communities of knowledge inside organizations and connecting them to outside stakeholders.

Risk 2: Competitors capitalizing on our Open Innovation efforts.
Here Open Innovation has successfully become the essential core of the organizations innovation efforts. All discussions on potential new products and services take place with the help of “the crowd”. This crowd successfully includes both internal experts and stakeholders and interested parties from outside. Despite this, there is still a looming feeling that research findings that are crucial to competitiveness and/or that brings in money through licensing are “given away”.

It is natural to worry about opening up windows into critical R&D processes resulting in potentially leaking knowledge that could be capitalized on by competitors. One solution would be to not treat the Open Innovation paradigm as binary, but add a crucial time axis and learn when innovation processes should be open, semi-open, or even closed. Management of how to balance openness vs. closeness becomes necessary.[quote2]

Take IBM as an example – one of the most prominent corporate evangelists for Open Innovation (see link to full interview below). IBM receives about US$1 billion in revenue annually from patents. However they spend about US$6 billion a year on R&D and constantly must take a number of decisions regarding the balance of openness vs. closeness. When should IBM leverage their 40, 000 patent portfolio to hinder other companies or maximize licensing revenue, and when should they not?

Making openness a well-informed choice
Facing these decisions, in most organizations in a much smaller scale and scope, perhaps leads to a somewhat less dramatic view on Open Innovation as phenomena. Organizations may feel less at home with Open Innovation as a dramatic paradigm shift, where an old (always closed and undesirable) innovation process must be replaced by a new (always open and desirable). And more as a view where openness is a well-informed choice based upon a number of variables.

The world of business is seldom as black or white, however these risks are real and can serve as a point for learning about succeeding in Open Innovation. But remember - to have these options, an organization must have invested in Open Innovation practices in the first place.

Recommended reading
Walsh, J.P. & Nagaoka, S. (2009). How “Open” is Innovation in the US and Japan?: Evidence from the RIETI-Georgia Tech inventor survey. 2009 RIETI Discussion Paper Series 09-E-022

Hrastinski, S., Kviselius, N.Z., Ozan, H. & Edenius, M. (2009). A Review of Technologies for Open Innovation: Characteristics and Future Trends. To be presented at Hawaii International Conference on System Sciences 43, January 5-8, 2010.

Tanokura, Y. & Oishi, M. (2009). ‘Only Open, Evolving Companies will Survive’—John E Kelly III, IBM. Last accessed September 17, 2009.

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Knowledge management has experienced at least three generations. The first generation was built on the assumption that knowledge can be stored and distributed by using information technologies (e.g. Nonaka & Takeuchi, 1995). This kind of knowledge was supposed to be possible to distribute without any distortion. Knowledge was regarded as “existing” more or less regardless of the container it happens to occupy and independent of particular context.

The second generation turned attention to how knowledge could be generated (e.g. Wenger, 1998) by letting people from different disciplines or specializations come together. In the late 90s, the merits and limitations of these two perspectives were commonly debated. During the third generation, which we are still experiencing, many have realized the complementarities between the two generations. Information technologies can be used for both storing and analyzing ideas, and to support sharing as well as generating knowledge. Thanks to new information and communication technologies, this relation has become of more immediate importance. For example, a system successful in supporting users in sharing knowledge would be even more useful if it could also provide sophisticated tools for storing and analyzing the shared knowledge.

In the open innovation field, we distinguish similar developments. Currently, there are two parallel perspectives of practice and research, which are similar to the first two knowledge management generations mentioned above. The first perspective of open innovation emphasizes storage and distribution of knowledge. For example, Dodgson et al. studied how new technologies for data mining, simulation, prototyping and visual presentation can support open innovation. They define innovation technology as “information and communication technologies [that] enable the exchange of distributed sources of information in the open innovation process” (p. 333). Data mining and searching support the sharing of data from internal and external sources, while simulation and prototyping support experimentation with ideas that eventually can become innovations.

The second perspective of open innovation is focused on collaboration technologies for innovation, which support sharing and applying knowledge over time and across geographical boundaries. Virtual collaboration for innovation can be defined as “the interaction of individuals from distinct knowledge domains, separated by time and space, and in which they share and combine their knowledge to develop and implement creative ideas” (Yates 2007, p. 1). And as we know, new and creative ideas often lead to innovations.

What is new about open innovation, as compared with knowledge management? We believe that a key difference is that we have moved from small scale collaboration towards mass collaboration in communities with thousands or even millions of users. Another difference is that the focus has been broadened and we are nowadays seeing many people as potential contributors to new knowledge. For example, organizations inspired by open innovation are commonly asking their customers for support while the knowledge management field has traditionally been more focused on inter-organizational knowledge sharing. A third difference is that the tempo has increased significantly.[quote2]

Drawing on the two current open innovation perspectives, we believe that the field is ready to move towards a third perspective. People commonly use information technologies to share ideas, experiences and generate knowledge in online communities, but they do not necessarily turn this knowledge into innovations. Interestingly, technologies from the first open innovation stream, such as data mining, simulation, prototyping and visual presentation, can be very useful in enabling opportunities for reflection and analysis in order to synthesize shared knowledge and turn it into great innovations. Prepare for the next generation of open innovation in which opportunities for collaboration and analysis will be combined in exciting ways!

Recommended reading:

Dodgson, M., Gann, D., & Salter, A. (2006). The role of technology in the shift towards open innovation: the case of Procter & Gamble. R&D Management, 36(3), 333-346.

Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford: Oxford University Press.

Vanhaverbeke, W. (2006). The Interorganizational Context of Open Innovation, in Open Innovation: Researching a New Paradigm, H. Chesbrough, W. Vanhaverbeke, and J. West, Oxford University Press, p. 205-219.

Yates, D. (2007). Technology support for virtual collaboration for innovation in synchronous and asynchronous interaction modes. Doctoral dissertation, University of Southern California.

Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge: Cambridge University Press.

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