Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

A Review of Technologies for Open Innovation: Characteristics and Future Trends

Stefan Hrastinski

Department of Informatics and Media Uppsala University and

KTH Royal Institute of Technology stefan.hrastinski@im.uu.se

Niklas Z Kviselius Swedish IT-User Centre Uppsala University and

Stockholm School of Economics niklas.kviselius@hhs.se

Håkan Ozan

CSC hozan@csc.com

Mats Edenius Swedish IT-User Centre and

Department of Informatics and Media Uppsala University

mats.edenius@nita.uu.se

Abstract

Fuelled by increased interest among

organizations to team up with their customers, technologies for supporting open innovation are emerging. In this paper, we explore how current technologies are designed to support open innovation, in order to envision future developments and research challenges. A sample of 51 systems has been classified. Surprisingly, we found that most technologies for open innovation are not very innovative because they provide similar features. Typically, users are asked to suggest ideas or solve predefined problems in collaboration with others. By drawing on our review, we identified four types of open innovation systems: idea management system, problem solving system, marketplace system and innovation analysis system. In conclusion, we noted that a key challenge for research and practice is to gain a deeper understanding of how collaboration technologies can be connected with more sophisticated opportunities for reflection and analysis.

1. Introduction

To sustain the pace of innovation, organizations need to improve their ability to produce, integrate and recombine knowledge. Most innovations happen at the boundaries between disciplines or specializations [32]. Hence, it is not a coincidence that knowledge processes are opening up far beyond the borders of organizations. Strategies are needed for finding new boundaries and potentially new innovations [34]. The

terms open innovation and networked innovation have both been proposed to illuminate this process. Open innovation is built on the assumption that organizations need to cast their nets far and wide to garner the knowledge they need to create new products and processes. Today we also see how ideas as well as knowledge come from both inside and outside organizations [19, 20]. Swan and Scarbrough [37] suggest a different focus and promote the term networked innovation and define it as “innovation that occurs through relationships”. By doing this they emphasize that innovation is not only about open processes, but also dependent on the integration of knowledge from diverse sources from networks of different kinds.

Information technology has greatly enhanced the ability for organizations to expand their repertoire of knowledge by engaging external actors and crossing borders in the innovation process. Hitherto, research has mainly focused on business strategy and organizational change in relation with open innovation and how open innovation practices can stimulate the development of novel technologies [24]. This research has undoubtedly advanced our understanding of innovation processes, not at least by showing its multifaceted nature. However, despite that it is widely acknowledged that technologies are often of importance in open innovation practices [19], little research has studied how various technologies can be strategically used to enable and support open innovation practices [23]. For example, Chesbrough [19] 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 [24]. Drawing on the above definitional work, the

Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

aim of this paper is to shed light on how information technology can support open innovation. In addressing the aim, we explore how current technologies are designed to support open innovation, in order to envision future developments and research challenges. The findings presented in this article should be of interest to both researchers and practitioners that need guidance in choosing among the abundance of available technologies.

The paper is structured as follows. In the next section, we define the concept open innovation system by reviewing the literature. This is followed by a discussion of the research procedure that guided a review and classification of systems for open innovation. The paper then proceeds by describing the characteristics of the reviewed systems. Next, we discuss our findings. The findings from the review of current open innovation systems are used to support the envisioning of future developments and research challenges. 2. Open Innovation Systems

In this section, we identify two fundamental characteristics of open innovation systems. First, we argue that open innovation occurs in knowledge networks. In the second section, we suggest that information technology can sustain knowledge networks and innovation by supporting generation and sharing of knowledge. 2.1. Innovating in knowledge networks

Going to its etymological roots almost every definition of innovation includes the concept of “newness”. The “newness” might, for example, be connected to new ideas, new technologies, new artifacts, new products or new ways of doing things. However, who decide what is to be regarded as an innovation? Innovations might be said to be things that are perceived and defined as new by the relevant unit of adoption [21]. Furthermore, innovation refers not just to an outcome, such as a new idea or product, from a relevant unit of adoption, but also to different processes, such as how a new idea emerges and how it is technologically supported [27]. This is the reason why our investigation starts from investigating different knowledge processes across boundaries linked to different available technologies.

The open innovation paradigm suggests that organizations increasingly need to team up with other organizations and customers in developing and refining ideas, services and products [15, 38]. Most scholars agree on the collaborative nature of open

innovation. For example, according to Chesbrough [19, p. 43], “open innovation means that valuable ideas can come from inside or outside the company and can go to market from inside or outside the company as well” and can be enhanced by purposive inflows and outflows of knowledge [20].

The term open innovation system has been used quite frequently in the literature, but is commonly used when arguing that organizations need to move from closed to open innovation systems, mainly referring to open systems of collaborating organizations and customers [19, 20, 22]. Unfortunately, these definitions give limited attention to the fact that technologies play a key role in enabling open innovation practices. Consequently, we suggest the following definition of open innovation systems: “open networks of innovating organizations, customers and users, commonly supported by information technologies”.

Open innovation depends on networks of different kinds, including formal organizational networks and informal interpersonal networks [28]. Hansen et al. [29] show, for example, that formal and informal networks complement each other. They argue that informal networks are useful for generating new ideas, while formal networks are needed for deciding which ideas to pursue further.

We are still in the early stages of understanding how IT can support open innovation. However, Gordon and colleagues [26] are pioneers. Their study identified activities that were used and believed to be useful in the front end of innovation by users in 80 companies. The front end of innovation refers to the earliest stages of innovation and includes activities such as collaboration, intelligence gathering and idea generation. Gordon et al. suggest that IT can beneficially support innovators in (1) helping one another, (2) accessing and sharing organizational knowledge, (3) analyzing and modeling data, (4) visualizing innovations, and (5) generating ideas. Our ambition is to extend the work of Gordon et al., because we include all phases of open innovation. 2.2. Innovating by generating and sharing knowledge

From the knowledge management literature two fundamentally different kinds of knowledge practices are to be found. The first knowledge practice draws on the assumption that knowledge can be stored, distributed and analyzed [e.g. 33]. The other knowledge practice focus on how knowledge can be shared and integrated in communities [e.g. 17, 39]. We argue that these streams of thoughts on knowledge managing theory can provide insight in

Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

how to design open innovation systems. On the one hand, such systems can be used for storing and analyzing ideas. On the other hand, such systems can be used for sharing, integrating and generating knowledge among participants. Deleuze [21] captures these streams in a nutshell: Learning (i.e. knowledge processes) can be repetition and difference simultaneously.

The perspectives introduced in the aforementioned paragraph are mirrored in research on how technologies can support open innovation. The first stream emphasizes storage, distribution and analysis of data. For example, Dodgson et al. [23, 24] 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” [24, 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 stream is focused on collaboration technologies for innovation, which support sharing and integration of 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” [40, p. 1]. Common ground helps individuals to understand each other, while too much common ground can constrain innovation [18]. An underlying assumption of using collaboration technologies to support open innovation is that each participant in a community has unique knowledge and experiences that others can benefit from taking into account [16].

Many researchers and practitioners subscribe to one of these two streams of research, despite that they are probably most useful when used in combination. An underlying assumption of using collaboration technologies to support open innovation is that each participant in a community has unique knowledge and experiences that other can benefit from taking into account [16]. However, a key challenge when using collaboration technologies as support for open innovation is to enable opportunities for reflection and analysis in order to synthesize shared knowledge and turn it into innovations [16, 40]. This can beneficially be supported by using tools promoted by the first stream of research.

3. Method

In this section, we describe how the technologies for open innovation were selected followed by the classification scheme that was developed to categorize the systems. Initially, Internet searches using Google were carried out to identify technologies for open innovation, by using keywords such as “innovation system”, “ideation”, “idea management” and “open innovation”. We have also used a Google alert service that sent us e-mail when new pages that use these keywords were published. Reasons for including aspects of idea management are that such terms are frequently included in definitions of open innovation [19] and because research has focused on how the process of developing ideas can be turned into innovation [25]. We also knew about a number of systems before conducting our search, because we continuously monitor web sites, blogs and RSS feeds, such as ideamanagementsystems.com, on “open innovation” and “idea management”.

In total, 58 systems were identified. As it is not possible to produce an exhaustive list of all available technologies for open innovation, this sample should give us an indicative, rather than definite view of such technologies. Out of these, 51 systems were chosen for further analysis. These systems explicitly stated that they support innovation and included at least one open feature, such as the possibility for users to suggest ideas or collaborate, typically supported by a web interface. Most of the excluded systems mainly focused on individual creation of models and visual representations. While it is possible for open systems of innovating organizations and customers to meet and work together in front of a computer, such systems are mainly intended for individual work. Other reasons for omitting systems were that they mainly focused on the commercialization of innovations, because there were limited information available on the website or because the system was no longer available for licensing or purchase.

A number of key characteristics were iteratively derived, by reviewing the websites of the selected systems. The overview of characteristics was intended to describe the state of the art of current technologies for open innovation. By taking the existing literature discussed in the previous section into account and the features of the reviewed systems, our review combines insights from both research and practice. Initially, we identified 43 features and then synthesized these into 7 categories. These categories were iteratively refined while reviewing the systems. In the end, the following key

Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

categories were used in the final classification: idea submission, problem submission, problem solving, evaluation, collaboration, expert directory and marketplace. The steps in the classification process are summarized in Table 1.

Table 1. Steps in the classification process [adapted from 35, p. 4] Steps Description 1 Search for

systems 58 systems were identified.

2 Selection of systems

51 systems were selected for further analysis.

3 Development of classification scheme

Iterative process of classification of systems and refinement of categories.

4 Final classification

Final naming of the categories and classification of the systems.

5 Description Description and analysis of the classified systems.

6 Inter-rater reliability

A second author also classified 20 % of the systems to ensure reliability.

To ensure a sufficient level of reliability, the

second author randomly selected and classified 20 % of the systems. A test of inter-rater reliability was undertaken, using Holsti’s [30] coefficient of reliability. The test resulted in a percent agreement figure of 0.97, i.e. nearly total agreement. The generally accepted level is 0.70. 4. Characteristics of Open Innovation Systems

In this section, the identified typical characteristics of technologies for open innovation are described. A detailed classification scheme is included in Table 2 (see Appendix). 4.1. Idea submission

Many of the systems (80 %) supported idea submission, i.e. functionality that makes it possible for users to suggest ideas. Typically, such functionality makes it possible to collect ideas from, for example, employees, customers and, in some cases, partners. In many cases the submission of ideas can be done within predefined categories, often based on current challenges, as in the case of MyStarbucksIdea, where Starbucks’ customers can submit ideas in areas such as “Coffee & Espresso Drinks” or “Ordering, Payment & Pick-Up” [9].

4.2. Problem submission

More than half of the systems (57 %) supported problem submission, i.e. functionality that makes it possible to submit a problem. Often, problems that need to be solved are defined by an organization. Then, users can be asked to suggest solutions to the problem (see section 4.3). An early example of this implementation is the Ubuntu Brainstorm project, where it was found that only submitting ideas without a description of the underlying problem or need, made most solutions standalone and difficult to coordinate [31]. For example, Atizo [1] and several other marketplaces, lets an organization suggest a challenge and a reward. Then, users generate solutions and further develop the solutions in collaboration with the organization. 4.3. Problem solving and analysis

A rather common characteristic of the reviewed systems (69 %) was problem solving and analysis, i.e. functionality that supports users in solving problems and further development of ideas. Some of the systems supported established techniques for inventive problem solving [e.g. 3], such as TRIZ [13] (Russian acronym for the “Theory of Inventive Problem Solving”), while others where focusing more on creativity and supported other forms of problem solving stimulus, such as mind mapping. Often, problem solving is directly related with a specific problem, as exemplified in the previous section. Most idea generation systems encourage users to further analyze and develop a submitted idea in collaboration, usually by using a discussion board. 4.4. Evaluation

Most systems (84 %) supported evaluation, i.e. functionality that makes it possible to analyze the quality of ideas and solutions. Basically, two types of evaluation could be discerned: user evaluation and business evaluation. Users can evaluate ideas and solutions, usually by letting them vote and comment or discuss others’ ideas and solutions. The other, less common, type of evaluation is conducted by the organization using the system, in which the quality and potential of ideas and solutions are evaluated. Most systems focus on one of the two types of evaluation, although an interesting exception is Idea Central that supports “an informal, unstructured voting process for all contributors” and “a formal, structured process for review team members” [4]. Innovator [8] provides sophisticated features for

Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

management evaluation, including review committee function, rules that support determining how and when ideas are moved between review committees and an executive dashboard with, for example, measures of return of investment and inventor statistics. The management evaluation phase is often proceeded by screening, which can be done automatically and/or by an administrator. 4.5. Collaboration

Most systems (82 %) supported collaboration, i.e. functionality that supports collaboration among users. Commonly, collaboration occurs through discussion and evaluation of ideas and solutions, and in some cases problem analysis. The common ground for most collaboration supportive systems was either one of the following two approaches; 1) sharing of material, such as links and white papers, to increase the accumulated knowledge on a certain topic, 2) communication, to share knowledge and opinions on topics. Collaborative features are often simple and typically supported by simple technologies, such as voting or a discussion board. For example, Innovation Exchange simply includes “a set of blogs, which allow people to share innovations they are working on or innovations they feel are really needed or would like to find” [7]. 4.6. Expert directory

A quarter of the systems (25 %) included an expert directory, i.e. functionality that makes it possible to describe and locate expertise. As a matter of fact, for open innovation marketplaces this is a prime feature, where having a strong expert directory is a competitive advantage. Having many and competent problem solvers could make the problem solving process more efficient. In most cases, such as Brightidea [2], experts are included in a directory. Moreover, rewards and recognition are used as incentives for participation. For example, WebForce [11] provides a “Fellow Ranking” based on frequency of posting and rewarded postings. 4.7. Marketplace

About a fifth of the systems (22 %) can be characterized as a marketplace, by connecting solutions seekers with innovators. There are basically two types of marketplaces that we label open marketplace and confidential marketplace. Open marketplaces provide functionality that makes it possible for anyone to submit problems, ideas and

solutions. For example, InnoCentive “connects companies, academic institutions, public sector and non-profit organizations” [6]. In other cases, marketplaces are confidential. For example, IdeaConnection provides a tool for problem solving that needs to be confidential [5]. Every potential problem solver signs a confidentiality agreement. The focus of marketplaces can be non-specified [e.g. 6] or focus on specific fields, for example, science and technology [e.g. 10]. Most of the marketplaces offer financial incentives in order to stimulate participation. For example, in InnoCentive “seekers” can describe problems and offer “solvers” a “challenge reward” for the best solutions [6]. 4.8. Analysis

In the matrix of Table 3 (see Appendix) a number of significant correlations are displayed. There is a strong correlation between supporting idea submission, and evaluation (r=0.47, p<0.01) and collaboration (r=0.42, p<0.01). Thus, technologies that let users submit ideas very commonly also provide functionality for evaluating ideas in collaboration with other users. Supporting this claim, there is a strong correlation between evaluation and collaboration (r=0.37, p<0.01). Not surprisingly, there is a strong correlation between problem submission, and problem solving and analysis (r=0.78, p<0.01), i.e. functionality is provided both for suggesting problems and for solving these problems. It is, however, interesting to note that technologies that let users submit problems rather often also include an expert directory (r=0.45, p<0.01), which can support the localization of experts that can solve these problems.

Positive correlations can be noted between marketplaces, and problem submission (r=0.30, p<0.01) and problem solving and analysis (r=0.38, p<0.01), while a negative correlation can be noted with idea submission (r=-0.43, p<0.01). In comparison with other technologies for open innovation, marketplaces commonly require management to define problems and then users are asked to propose solutions to these problems. However, there are few opportunities for users to suggest ideas in more open ways.

5. Conclusion

In this paper, we explored how current technologies are designed to support open innovation, by summarizing characteristics of current systems. Open innovation can roughly be divided into three

Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

stages: (1) the front end of innovation, where people are supported in generating ideas that are creative and potentially valuable [26], (2) innovation development and evaluation processes and (3) the back end of innovation, where innovations are implemented as, for example, products, services and processes. Our review has revealed that a vast majority of the technologies for open innovation focus on supporting collaborative idea generation and problem solving. Thus, a challenge for research and practice is to gain a deeper understanding of how collaboration technologies can be connected with more sophisticated opportunities for reflection and analysis. Currently, precious few systems provide support for the back end of innovation.

Surprisingly, many of the reviewed systems provide similar functionality, indicating that there is an implicit agreement on how to design technology for supporting open innovation. Typically, users are asked to suggest an idea or the management specifies a problem. Then, a phase including problem solving and evaluation in collaboration is followed. In successful cases, these processes eventually lead or contribute to an innovation. By drawing on our review, we can identify four basic kinds of open innovation systems (see Table 4). Table 4. Four types of open innovation systems Type Description Idea management system

Lets users suggest, evaluate and discuss ideas openly or within predefined categories.

Problem solving system

Provides opportunities for defining problems and then suggesting, evaluating and discussing solutions.

Innovation marketplace

Asks users to suggest solutions to problems defined by an organization, and use rewards and recognition as an incentive.

Innovation analysis system

Provides sophisticated tools for evaluating and analyzing the quality and potential of ideas and solutions.

6. Future Challenges

In this section, we first acknowledge limitations and suggest topics for further research. We also reflect on future challenges when developing and using the next generation of technologies for open innovation.

6.1. Limitations and future research

In this paper, we have provided an initial characterization of current technologies for open innovation. As such, the paper is subject to some limitations.

Research is needed on how technologies for open innovation are used in context. We have in this paper focused on the technical features of the systems. These features do not necessarily reveal how a particular system is being used. Also, we have used the web sites of system vendors, which is associated with at least two problems. First, we could not test each system, which gave us a somewhat limited understanding of their features. Second, the system vendors mainly put forward the benefits of their systems. Therefore, in our future research, we intend to conduct case studies in order to more deeply understand how technologies for open innovation are being used.

Research is needed on how opportunities for collaboration and sophisticated analysis can be combined. In the second section, we distinguished two streams of research on technologies for open innovation. The first stream draws on the assumption that knowledge can be stored, distributed and analyzed. The second stream is focused on sharing and integration of knowledge over time and across geographical boundaries. Most technologies for open innovation are related with the second stream and build on the assumption that collaboration can support open innovation. A deeper understanding of how collaboration supports open innovation can help practitioners understand when, how and why to use technologies for this purpose.

Many technologies for open innovation combine collaborative functionality with the possibility to analyze the quality of ideas and solutions. However, most of the reviewed systems seem to have less support for analysis and reflection. Thus, it is important that future research look into how knowledge sharing and collaboration can be combined with opportunities for sophisticated analysis [16, 40]. There seems to be great potential in combining the two complementing streams of research.

Research is needed on how different technical features can be beneficially used for different open innovation practices. Technical features ranging from simple to advanced are currently used to support open innovation. While some systems simply include blogs and discussion forums, others provide sophisticated features for analysis. In some cases, a simple system might be preferable, because users sometimes spend a lot of time on technology sense-

Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

making when using sophisticated systems [12]. On the other hand, simple tools provide fewer opportunities for sophisticated analysis. 6.2. Implications for design

What future trends lies ahead for technologies for open innovation? What problems might be faced when implementing such systems in organizational settings and how can they be addressed? Designers of open innovation systems do face some intriguing challenges. Intellectually, the open innovation paradigm is attractive. However, how can information technology support the opening of parts of such a sensitive organizational knowledge repository as the innovation process? This has been safeguarded heavily in the past because of fear of competitors striking the first-mover advantage. Below, we give examples of future challenges.

Increased customization. Most of the systems described in this paper provide stand-alone functionality targeting open innovation initiatives without much thought on how to relate to existing organizational structures or systems. One future trend could be more support and alternatives for tailoring these systems to the features of existing organizational formal and informal infrastructure. This could include various customization options to synchronize with activities of, for example, enterprise systems and intranets.

One example of a variable with a need for highly flexible customization is user segmentation. Today few existing systems are supporting different classes of users. While some system owners aim for creating one ideation melting pot involving as diverse users as possible, others could see a need to create several pots under the same umbrella with some control on information flows in between. In order to get the most out of user co-creation some systems may be modified to accommodate such diverse groups as a company’s customers, employees, as well as other stakeholders such as suppliers and consultants. The most interesting challenge comes with designing systems that successfully support these groups and the interaction among them.

Attracting innovators. Already we see examples on functionality for attracting more users via widgets that can be published on other web pages or in emails. These can, for example, include functionality for idea generation and voting. As organizations increasingly aim for the attention of innovative users, these users will become selective in their participation. There will be a competition for the smart and engaged users that truly can contribute to substantial innovation. One way of providing

increased use and marketing of a specific open innovation activity is with a system that provides a number of possible entrance points and interfaces to participate. For example, further support for mobile devices has not been exploited in the existing open innovation systems. This could support users in engaging and providing feedback in a specific context.

Handling information overflow. Some of these systems face issues of information overflow. If successful, most of the tools described and analyzed in this paper, would provide a plethora of un-structured or semi-structured information from a large number of users. It is interesting that most existing systems in the study have stretched the users’ engagement in the innovation process beyond ideation and into idea evaluation. However, simple features of voting and commenting will need to be extended. A future trend that we anticipate is stronger administrative tools behind the scenes to cut through the potentially voluminous material provided by users. This is necessary in order to capture ideas and move them towards commercialization.

Supporting the creative front end of innovation. Designing physical as well as virtual environments, rules of engagement, and organizational structures that encourage creativity is a complex theme that has been approached by various academic disciplines and is at the very heart of innovation. Creativity has been found a precursor of organizations’ effectiveness and very survival [14, 36].

Creative generation of ideas includes activities like summarizing and wrapping up a line of thought, freezing one assumption and moving the discussion along for a while, then thawing this assumption and starting all over. Such complex mental tricks taking place with more limited number of participants, often face-to-face, in brainstorming sessions all over the world, will be hard to simulate by technologies for open innovation. Still we anticipate more creative environments with the aim of creating ongoing brainstorming including richer communication channels than text, such as voice and visualization.

Based on the analysis of existing systems, there is much room for future development of features that could stimulate how an idea is conceived and formulated. While the point of departure today is the submission of an idea, future system designs may pay more attention to the mental and social process involving the generation of new ideas. While many of the reviewed systems support collaboration in structured ways, creativity is often argued to be driven by knowledge sharing in iterative and unstructured ways [26]. As noted by Gordon and colleagues [26], we still know little about supporting

Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

the creative front end of innovation. They suggest that organizations and researchers need to address the following challenge: “How can IT best support and encourage innovation in its earliest stage?” As noted earlier in the discussion of the back end of innovation, we also anticipate a need to ask: “How can IT support the implementation of innovations as, for example, products, services and processes, in practice?”

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Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

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Appendix Table 2. Classification of technologies for open innovation

Idea subm.

Problem subm.

Problem solving Evaluat. Collab. Expert

directory Market-

place Accept Ideas x x x Accolade x x x x Atizo x x x x x x x BeeQuu x x x x x x BrainReactions x x x x x Brainstorm on Demand x x x Brightidea x x x x x x Crowdsourcing Engine x x x x x De Bono Thinking x x x eSuggestions x x eThink x x x x x e-Tipi x x x x EmployeeSuggestionBox x x x FellowForce x x x x x x Goldfire x x HypeIMT x x x x x x Idea Central x x x x x x Idea Manager x x x x IdeaConnection x x x x x x x IdeaBox x x Idealink x x x x x x Idealyst x x x IdeaScale x x x Ideation 2.0 x x x x x x

Hrastinski, S., Kviselius, N. Z., Ozan, H., & Edenius, M. (2010). A review of technologies for open innovation: characteristics and future trends. In 43rd Hawaii International Conference System Sciences (HICSS) (pp. 1-10). IEEE. DOI: 10.1109/HICSS.2010.29.

IdeaTorrent x x x x x iDS Idea Development x x x x x Ingenuity Bank 4.0 x x InnoCentive x x x x x InnovateUs x x x Innovation Exchange x x x x x x Innovation Portal x x Innovation Portal 3.5 x x x x x x Innovation Suite 3.1 x InnovationSpigit 2.0 x x x x x Innovator x x x x x x i-Nova Suite x x x x x Jenni x x x x x Kindling x x x NineSigma x x x x OrchIdea x x x x x Philoptima x x x x Planet Eureka x x PrIsM x x Realyst x x SalesForce Ideas x x x SimNet8 x x x x x Solutions Genie x x x x TekScout x x x ThoughtOffice x x x x x x UserVoice x x x WebForce 2.1 x x x x x Total (n=51) 41 (80 %) 29 (57 %) 35 (69 %) 43 (84 %) 42 (82 %) 13 (25 %) 11 (22 %)

Table 3. Correlation matrix

Idea subm. Prob. subm. Prob. solv. Evaluation Collab. Expert dir. Marketpl. Idea subm. 1.00 Prob.subm. .07 1.00 Prob solv. -.23 .78** 1.00 Evaluation .47** .17 -.06 1.00 Collaboration .42** .12 .02 .37** 1.00 Expert dir. .19 .45** .32* .020 .23 1.00 Marketplace -.43** .30* .38** -.27 -.23 .07 1.00

* Correlation is significant at the 0.05 level (p < 0.05), ** Correlation is significant at the 0.01 level (p < 0.01)