Scholarly article on topic 'Exploring innovation through open networks: A review and initial research questions'

Exploring innovation through open networks: A review and initial research questions Academic research paper on "Economics and business"

Share paper
Academic journal
IIMB Management Review
OECD Field of science
{"Inbound open innovation" / "Knowledge brokering" / "Open networks" / "Innovation performance" / Seekers / Solvers}

Abstract of research paper on Economics and business, author of scientific article — Jonathan Ye, Atreyi Kankanhalli

Abstract The open innovation strategy as an emerging approach towards innovation is beginning to receive attention from organisations and researchers. Open innovation signifies opening up of internal R&D by leveraging inflow and outflow of knowledge. Open network is one mechanism of open innovation, which brings solvers from different domains to work on problems posted by seekers. Since solvers and seekers serve as the foundation for the realisation of the open innovation strategy, such understanding is imperative to encourage participation and realise benefits from open networks. This article investigates the potential factors that can promote solvers' and seekers' participation in open networks.

Academic research paper on topic "Exploring innovation through open networks: A review and initial research questions"

IIMB Management Review (2013) 25, 69-82

available at

SciVerse ScienceDirect

journal homepage:


Exploring innovation through open networks: A review and initial research questions

Jonathan Yea1, Atreyi Kankanhalli b *

a Department of Information Systems & Operations Management, The University of Auckland Business School, Owen G Glenn Building, Auckland, New Zealand

b Department of Information Systems, School of Computing, National University of Singapore (NUS), Singapore


Inbound open


Knowledge brokering;

Open networks;





Abstract The open innovation strategy as an emerging approach towards innovation is beginning to receive attention from organisations and researchers. Open innovation signifies opening up of internal R&D by leveraging inflow and outflow of knowledge. Open network is one mechanism of open innovation, which brings solvers from different domains to work on problems posted by seekers. Since solvers and seekers serve as the foundation for the realisation of the open innovation strategy, such understanding is imperative to encourage participation and realise benefits from open networks. This article investigates the potential factors that can promote solvers' and seekers' participation in open networks.

© 2013 Indian Institute of Management Bangalore. Production and hosting by Elsevier Ltd. All rights reserved.


Innovation has been perceived as playing a central role in the long-term survival of organisations. It serves as a tool for organisations to adapt to environmental dynamism and to obtain competitive advantage by providing new products or services to underserved or unserved customers (Huston & Sakkab, 2006). With such potential benefits, innovation has

* Corresponding author. Tel.: +65 6516 4865. E-mail addresses: (J. Ye), atreyi@ (A. Kankanhalli). 1 Tel.: +64 9923 7154.

Peer-review under responsibility of Indian Institute of Management Bangalore

aroused continuing interest among researchers and practitioners. Previous research suggests that innovations create value for companies by decreasing the costs of existing products or services, improving their quality, inventing new products or services for which there is sufficient demand, or delivering better business or delivery models (Hauser, Tellis, & Griffin, 2006). Moreover, radical innovations transform or even destroy existing markets by finding new solutions to problems. They can bring down giant incumbents and propel small start-ups into dominant positions creating new jobs for the market. Therefore, how to innovate is a key problem that management and researchers are interested in addressing (Lichtenthaler, 2009a).

In the past, many companies believed that as long as they invested more heavily in R&D than their competitors and protected their intellectual property from spilling over, they could innovate faster and more radically than competitors and hence sustain their competitive advantage. This paradigm of innovation is called closed

0970-3896 © 2013 Indian Institute of Management Bangalore. Production and hosting by Elsevier Ltd. All rights reserved.

innovation which requires the aggressive control of internal knowledge from leaking outside (Herzog & Leker, 2010).

However, this philosophy has been challenged due to the dramatic increase in the number and mobility of knowledge workers and the growing availability of private venture capital (Chesbrough, 2003a). Increasing mobility of knowledge workers makes it difficult for firms to appropriate and control their R&D investments. If innovative ideas fall outside of the current operations of firms, knowledge workers involved can commercialise their innovative ideas through a start-up firm. Private venture capital helps to finance new firms and efforts to commercialise ideas that have spilt outside of corporate R&D. Therefore, large companies can now commercialise external ideas within their market channel or licence out internal innovation by deploying outside pathways to the market (Chesbrough, 2006). The trend of opening up internal R&D is further propelled by the setting up of open innovation intermediaries such as InnoCentive,, Innovation eXchange, and NineSigma. Companies can obtain innovative solutions by posting problems for knowledge workers to solve and can also spin off innovative ideas or solutions to other companies through these intermediaries (Boudreau & Lakhani, 2009). This new philosophy of innovation is termed as open innovation, which is beginning to receive attention from both practitioners and researchers (Dodgson, Gann, & Salter, 2006; Enkel, Gassmann, & Chesbrough, 2009).

Importance of open innovation

The changing environment characterised by globalisation and technological advancements has been challenging many organisations. Under the pressure of radical environmental changes, organisations may need to adopt an open innovation strategy since they cannot rely only on internal R&D to innovate in a world of widely distributed knowledge (Rohrbeck, Holzle, & Gemunden, 2009). This is because the boundaries between a firm and its environment have become so porous that innovations can easily transfer inward and outward. Firms that are "too internally focused" are prone to miss a number of opportunities because many innovative ideas will fall outside the organisation's current business or will need to be combined with external technologies to unlock their potential (Chesbrough, 2003b).

Moreover, firms often lack adequate organisational processes or routines to handle the market and the technological uncertainty associated with innovation. The high costs of internal R&D and short product life cycles imply considerable financial risks of innovation that firms can scarcely solve by relying on internal measures (Keupp & Gassmann, 2009). Licencing-in or co-creation of innovation with outside partners can be an attractive option to diversify risk and share uncertainty.

Furthermore, many companies, especially large ones, suffer from impediments to innovation such as the performance trap (Valikangas & Gibbert, 2005). The inertia to change when organisational performance is adequate does not favour innovation (Blumentritt & Danis 2006). Organisations are unlikely to undergo radical changes instantly in order to cultivate an environment for innovation. The more

impediments to innovation companies face, the more likely they will tend to rely on and commercialise outside innovations (Keupp & Gassmann, 2009). Therefore, gradually, organisations may rely more and more on external sources of knowledge to foster and sustain innovation, enhance their performance and obtain competitive advantage (Laursen & Salter 2006; Lichtenthaler, 2009b).

For example, even with its significant resources, Procter & Gamble (P&G) still recognises that it cannot perform all its innovation in-house and that it needs to understand customers' needs much better than it did in the past to drive successful innovation in the future (Huston & Sakkab, 2006). Every year, P&G invests more than two billion dollars in innovation2 with a large proportion of its innovations coming from a diverse global network of external partners. In 2000, P&G launched the "Connect and Develop" programme and has since relied considerably on external innovators to build new brands and products or improve existing products. The percentage of all innovations that originated from outside P&G amounted to at least one external idea for each business unit in 2011.3

One mechanism of open innovation is through open networks. For example, P&G uses web technologies to seek out new ideas for future products through its own open innovation website (, and through open networks such as NineSigma, InnoCentive, YourEncore, and Also, P&G uses these open networks to licence out its innovations that fall outside its current business if an internal business does not use the idea within three years (Huston & Sakkab, 2006).

In these open networks, such as, "seekers" (usually companies) come to post problems that cannot be solved internally for "solvers" (external parties) to tackle (Lakhani & Jeppesen, 2007). InnoCentive helps the seeker to define the problem so that a diverse set of solvers can tackle it and so that a solution can be identified. When posting a problem, a seeker stipulates a time frame for solving it and a cash prize for the winning solution. Solvers who are interested in working on the problem then do so in isolation from both other solvers and from the seeker. By Sept 2012, more than 1450 problems had been posted on garnering more than 31,000 solutions with an average award rate of 57%.4

Benefits and challenges of open innovation

In a dynamic market, where consumer preference of a product or service is highly varied or not yet well-understood, and innovation approaches for particular products or service have yet to be established, opening up the innovation to the external world has considerable advantages (Boudreau & Lakhani, 2009). First, by licencing-in or co-creating technology or intellectual property with external collaborators, companies can quickly obtain advanced technology for their production and complement internal innovation activities (Lichtenthaler, 2008).

Second, by taking part in strategic joint ventures or alliances, companies can diversify the risk of innovation and share uncertainty with outside partners (Keupp & Gassmann, 2009). Strategic alliances allow companies to leverage innovation capabilities that are not available

internally and lower the risk of short product life cycles. By obtaining knowledge from partners in different domains and incorporating it into their internal innovations, companies can lower the cost and unlock the potential of internal innovation (Chesbrough, 2006).

Third, by including customers, suppliers and other sources of knowledge, companies can obtain continuous innovation, improve acceptance of customers (Von Hippel, 2001), and avoid being trapped by previous performance (Valikangas & Gibbert, 2005). For example, Google and Apple allow for a distinct part of their innovation process by users in order to take advantage of their diverse wealth of knowledge for developing new applications. The benefits of open innovation are summarised in Table 1.

Although some pioneering firms of open innovation such as P&G have achieved major benefits, others have experienced difficulties in profiting from external knowledge (Cassiman & Veugelers, 2006; Lichtenthaler, 2009a). There are several challenges to be met for open innovation to be successfully implemented (see Table 1). First, companies have struggled with precisely how to open up their product/service development to the external world and how to motivate and manage outside innovation (Boudreau & Lakhani, 2009). This includes concerns about how to find the right partner or valuable knowledge source and how to motivate outsiders to supply an ongoing stream of innovation ideas (West & Gallagher, 2006). Companies are also concerned with the risks connected to open innovation activities such as the loss of knowledge and control, higher coordination costs, as well as higher complexity which inhibit companies from adopting open innovation strategies and practices (Enkel et al., 2009).

Second, how to strike a balance between open innovation activities and internal innovation (Enkel et al., 2009) is challenging for companies. Since the resources of the firm are limited, how to allocate the resources for open innovation and internal R&D is unclear to many companies. On the one hand, the returns of exploiting existing knowledge are more certain and closer in time in the condition of stable environments. This attracts companies to rely on exploiting existing knowledge for innovation (Atuahene-Gima, 2005) which reduces firms' capability to adapt to future environmental changes and new opportunities (He & Wong, 2004). On the other hand, leveraging external heterogeneous knowledge brings radical innovation (March, 1991), which helps companies to adapt to turbulent environments in the long term.

Third, companies which are interested in open innovation are keen to know how external knowledge can be

incorporated into internal R&D and exploited (West & Gallagher, 2006). Opening and connecting to external knowledge sources does not always guarantee successful innovation and superior organisational performance (Laursen & Salter, 2006). To incorporate external knowledge into innovation activities, the relevant capabilities are required. These include the absorptive capacity and political willingness to incorporate external knowledge (West & Gallagher, 2006). The appropriate method of configuring internal absorptive capacity and policy to assimilate external knowledge has confounded many companies.

Fourth, even if external innovations are identified and incorporated, it does not mean they will be leveraged into the firm's product and service strategies. A firm that was once highly successful with the closed innovation model will tend to believe its innovations superior to competing ideas from outsiders, which results in the not-invented-here syndrome (West & Gallagher, 2006). Therefore, it is important to enhance the understanding of acquiring, learning, coordinating, and integrating external knowledge in order to benefit from the open innovation paradigm (Chesbrough, 2006).

In order to address the challenges of open innovation, researchers (e.g., Ebner, Leimeister, & Krcmar, 2009; Rohrbeck et al., 2009) have provided anecdotal evidence to explain how and why firms differ in the extent to which they conduct open innovation activities. Other researchers (e.g., Keupp & Gassmann, 2009; Laursen & Salter, 2006) have linked the open innovation strategy to firm performance by testing on large scale survey data. However, many gaps remain in our knowledge of open innovation mechanisms and their efficacy as indicated by the challenges in Table 1.

In order to achieve the intended benefits of the open innovation strategy, it is important to know what mechanisms are available for open innovation.

Mechanisms of open innovation

There are several mechanisms for organisations to adopt and implement an open innovation strategy. First, organisations can licence-in outside technology or intellectual property to complement their internal innovation activities (Lichtenthaler, 2008). The licencing-in process involves knowledge transfer from vendors to clients complementing absorptive capacity generated by internal R&D (Cohen & Levinthal, 1990). This helps client organisations in quickly adopting and leveraging licenced-in technology or

Table 1 Benefits and challenges of open innovation.

Benefits of open innovation Challenges of open innovation

Quickly obtain technology for Lichtenthaler (2008) How to manage and motivate open Boudreau and Lakhani

production innovation (2009); Enkel et al. (2009)

Diversify risk and share uncertainty Keupp and Gassmann How to balance internal R&D and Enkel et al. (2009)

(2009) external innovation

Lower the cost of innovation Chesbrough (2003b) How to incorporate it into internal R&D West and Gallagher (2006)

Improve customer acceptance of Von Hippel (2001) How to leverage external knowledge

products or services and combat not-invented-here (NIH)

Obtain continuous innovation syndrome

intellectual property for innovation and in obtaining competitive advantage in the market (Dodgson et al., 2006). A high level of internal absorptive capacity is important for recognising and evaluating the value of external knowledge, intellectual property and technologies (Huston & Sakkab, 2006; Zahra & George, 2002). This can be illustrated by licencing-in of the graphical user interface (GUI) technology by Apple Computer from Xerox (Chesbrough, 2003b).

Second, strategic alliances with suppliers and competitors render opportunities fororganisations to gain such knowledge or utilise complementary resources to exploit that knowledge (Chesbrough, 2006). This allows them to quickly respond to market and technological change by leveraging the core competencies of alliance partners (Xie & Johnston, 2004). Inter firm collaboration allows firms to access other organisations' capabilities. Thus organisations can seek competitive advantage through cooperation with other firms in order to achieve common and specific goals. This is illustrated by the example of strategic alliance between dedicated biotechnology companies and large, integrated pharmaceutical companies (Grant & Badeb-Fuller, 2004).

Third, user community and user generated innovations can be a preferred way for companies to tap into lead users to anticipate the emerging market. Lead users innovate to find tailored solutions for their needs (Von Hippel, 2005), which are usually months or years before the mass marketplace encounters them. For example, open source software products represent the leading edge of innovation development and diffusion conducted for and by users themselves (Von Krogh & Von Hippel, 2006). Another example is provided by Apple Inc's widely successful iPhone. Thousands of external software developers have written complementary applications for the iPhone that have greatly enhanced its value, transforming the product into a blockbuster that has become the centre of a thriving business ecosystem (Boudreau & Lakhani, 2009). Moreover, Microsoft makes extensive use of user communities, for the development of new products, and for further development of existing products (Von Stamm, 2008).

Fourth, open networks can provide an alternative way for companies (seekers) to find solutions for their problems (Boudreau & Lakhani, 2009) by providing incentives to "solvers". Companies post their problems for a solution within a certain deadline within the open networks. In these networks, "solvers" from a range of disciplines and countries with different professional knowledge come forward to tackle the problems for incentives. Taking Innovation as an example, it functions to match organisations seeking innovative products, services, processes or business models with individuals and organisations offering such innovations.5

Among these mechanisms, the open network mechanism is one of the least understood since strategic alliances (Grant & Baden-Fuller, 2004; Xie & Johnston, 2004), joint ventures (Enkel et al., 2009), and licencing-in (Chesbrough, 2006; Chesbrough & Crowther, 2006) have been studied. It is unclear how companies can obtain and apply external knowledge from open networks for beneficial outcomes. Thus, this study attempts to investigate the motivations of individual knowledge workers and organisations to participate in open networks and the influence of open network usage on seekers' innovation performance.

Open networks

Open networks are web-based talent markets serving as a platform for companies to find complementary knowledge assets (Boudreau & Lakhani, 2009). Open networks bring people with rich experience and new patterns of thinking from other organisations or industries for organisational utilisation. They disseminate the "briefs of challenges" to a broad range of audience including customers, competitors, suppliers, and scientists or researchers. For example, recently, collaborated with the Nature Publishing Group (NPG), a scientific and medical publisher (, to launch the Open Innovation Pavilion ( aiming to promote scientific collaboration and open innovation. Such a large pool of expertise should increase the chances of solving issues of worldwide concern.6 A summary of a sample of innovation intermediaries from around the world can be seen in Table 2.

In open networks, those companies that post problems and set incentives and deadlines for problem solving are called "seekers" while those individual knowledge workers or companies that write proposals for the problems are called "solvers" (Allio, 2004). These open networks serve as innovation intermediaries for seekers to find solution providers or solvers. They help their clients (seekers) by scrutinising the solutions submitted and eliminating those not meeting the criteria laid down on the website. In order to regulate the transaction between seekers and solvers, solvers are required to sign a contract relating to confidentiality and intellectual property rights, while seekers are required to fulfil their obligations to pay the agreed amount when an obvious solution to the problem is submitted. Solvers that do not win retain the rights to their solution after the evaluation period is complete. The seeker retains no rights to any IP not awarded.3 This helps protect the IP of both seekers and solvers (Huston & Sakkab, 2006).

Despite the potential of open networks, the motivations of seekers and solvers in participating in open networks and the benefits realised from open networks remain unclear.

Research questions

In order to open up their innovation process, companies will have to scan the environment for relevant knowledge. However, without a clear purpose of scanning, organisations may engage in extensive search for external knowledge that may not be leveraged by internal R&D (Huston & Sakkab, 2006). Even if organisations identify the relevant and useful knowledge sources, it entails costs of transferring knowledge from the "solvers" to "seekers" to allow the latter to interpret, acquire, and assimilate the knowledge. It needs seekers' specific knowledge to realise and assimilate specific knowledge (Choudhury & Sampler, 1997). Those activities may incur a large cost for companies contrary to the open innovation strategy's purpose of lowering cost of innovation (Chesbrough, 2006).

However, there is a lack of research to understand the underlying process of how seekers leverage external knowledge, specifically, how companies integrate external

Table 2 Open innovation intermediaries/open networks.

Open network name Scope Domains Size Location of


InnoCentive (http://www. • Connect companies with • Business and Entrepreneurship 260,000 solvers Massachusetts, USA contract partners • Chemistry

• Help companies find solutions • Computer/Info. Technology

to technology problems • Engineering/Design

• Food/Agriculture

• Life sciences

• Mathematics/Statistics

• Physical sciences

NineSigma (http://www. • Connect companies with contract • Adhesives, Sealant, and Surface Solvers from more Ohio, USA partners • Aerospace and Defense than 135 countries

• Help companies find solutions to • Automotive

technology problems • Chemicals and Materials

• Broker solutions to more narrowly • Consumer Products

defined scientific problems • Energy, Oil and Gas

• Food and Beverage

• High tech

• Materials technology

• Pharma, Healthcare, Medical

Yet2 • An online marketplace for More than 28 domains ranging from Aerospace, Technologies worth $10 Boston, MA, USA;

( intellectual property exchange Communications, Consumer, Education, billion and 500 major Tokyo, Japan;

• Broker technology transfer both into Environment, Food, Health and Wellness, clients Liverpool, UK

and out of companies, universities, Manufacturing, Optics, to Public Administration

and government labs and Transportation

IdeaConnection • An online marketplace for 56 different domains ranging from Acoustic, Over 40,000 solvers Victoria, Canada

(http://www. intellectual property exchange Biomedical, Chemistry, Engineering, Material Science, Nanotech, to Telecom

-vl UJ

knowledge with internal knowledge to innovate. It is not clear whether any process or structural changes are necessary for implementing open innovation successfully (West & Gallagher, 2006).

Also, despite the gradually increasing popularity of open networks in practice, there is little research to investigate how companies make best use of open networks for innovation and the role of open networks in facilitating companies' searching and finding appropriate solutions for their problems. In order to better leverage the open innovation strategy through open networks, a deeper exploration of the nature of the phenomenon is required. Also an empirical linkage between how open networks are used and what outcomes are obtained should be established.

Therefore, the research questions this paper proposes are: (1) What motivates solvers to use open networks? (2) What motivates seekers to use open networks? (3) Does use of open networks improve the seekers' innovation performance? Previous work suggests that it is important for a firm to be connected with external sources through open networks (e.g., Huston & Sakkab, 2006). To investigate this, we examine the influence of the brokering capabilities of open networks and the role of absorptive capacity in the open innovation's outcome, e.g., product or service innovations and firm performance.

Conceptual background

The outcomes we are interested in investigating are the innovation performance and the open networks usage by seekers and solvers. In this section, we review a selection of relevant literature from knowledge exchange in online settings, innovation management, and information systems fields. The review of these literatures serves to fulfil the following objectives: First, it provides a detailed account of theories that have been used to study knowledge exchange and open innovation. Second, it introduces brokering capabilities of open networks that might affect open innovation. Third, based upon these theories, it explicates the important variables which are critical to the understanding of open innovation through open networks. Last but not least, it helps position the current research with respect to the prior and ongoing work in related fields, thus paving the way for advancement of existing work.

Previous literature on open networks

It is important to understand why solvers participate in open networks to provide problem solutions. Since solvers usually have not interacted with the seekers before, they may not be motivated to help. In order to investigate the motivation of solvers' participation in open networks, Lakhani & Jeppesen (2007) surveyed solvers in InnoCentive. com and found that beyond the extrinsic reward obtained by providing solutions, solvers tend to be motivated by the enjoyment of taking on a novel problem and degree of challenge or novelty of the problem. Another online survey conducted in found that prize money, enjoyment of solving puzzles, and enhancing one's skills are the main motivations for solvers' participation (Travis, 2008). The summary of solvers' motivations from previous

literature can be seen in Table 3. These factors are proposed among the antecedents of solvers' open network usage in our article.

As for seekers, supported by open networks, they can fluently define problems and find the relevant solvers with heterogeneous but effective solutions (Huston & Sakkab, 2006). Open networks help solvers access a broad range of expertise by broadcasting the problems to professionals in varied fields and identifying possible solutions by filtering the proposals. These factors can be subsumed under the brokering capabilities of open networks which will be introduced later and adapted among the antecedents of seekers' open network usage in our article.

However, the above studies are not based on theory and in the case of seekers have not been empirically tested. Thus, there is a lack of theoretically-based empirical studies in understanding solvers and seekers' motivation of using open networks. In order to understand the drivers of solvers' and seekers' use of open networks, the following sections will introduce the theories that may be useful to explain the behaviours of solvers and seekers, and the influence of open network usage on organisational innovation performance.

Theories for knowledge exchange

Since open networks are a new and under-researched phenomenon, there is a lack of theories that have been used to study them. In order to examine open networks, we borrow from related theories that have been used to explain knowledge exchange in online settings. This section will introduce social exchange theory and knowledge brokering concepts for this purpose.

Social exchange theory

Social exchange theory explains human behaviour in social exchanges (Blau, 1964) from a cost-benefit perspective (Kankanhalli, Tan, & Wei, 2005a). It posits that individuals behave in ways that maximise their benefits and minimise their costs (Molm, 1997) and that they take part in an exchange only when they expect the rewards from it to justify the costs of taking part in it (Gefen & Ridings, 2002). Social exchange differs from economic exchange in that the exchange is not governed by explicit rules or agreements. In such exchanges, people do others a favour with a general expectation of some future return but no clear expectation of exact future return. This belief of future returns is central to a social exchange because the lack of explicit rules and regulations means that people have to rely on this

Table 3 Motivation for solvers.

Motivations Authors

Enjoyment of taking on a novel problem/degree of challenges/problem novelty Prize money Enhancing skills Lakhani and Jeppesen (2007) Travis (2008) Travis (2008)

belief to justify their expected benefits from the exchange. Therefore, social exchange assumes the existence of relatively long-term relationships of interest as opposed to one-off exchanges (Molm, 1997).

These principles of social exchange, i.e., a cost/benefit analysis of exchange, have been used to understand the knowledge exchange phenomenon (Hsu, Ju, Yen, & Chang, 2007; Wasko & Faraj, 2000) in online communities and organisations. They suggest that a member will contribute to the organisation or community as long as they obtain benefits from their contributions such as reputation, recognition, and enjoyment from helping (Kankanhalli et al., 2005a), or expect others to return their favours in the future due to reciprocity (Kankanhalli et al., 2005a; Wasko & Faraj, 2005).

The social exchange theory has been applied to study knowledge sharing in different contexts (e.g., Bock, Kankanhalli, & Sharma, 2006; Bock, Zmud, Kim, & Lee, 2005; Hargadon, 1998; Kankanhalli et al., 2005a; Powell 1998; Wasko & Faraj, 2000). For example, Kankanhalli et al. (2005a) applied the social exchange theory to explain the usage of the electronic knowledge repositories (EKRs) by employees to contribute knowledge. In Bock et al. (2006), the social exchange theory was used to explain knowledge seeking behaviours in EKRs. The antecedents of knowledge sharing that have been derived from this theory and social capital theory can be seen in Table 4 and Table 5.

While social exchange theory has been proposed for social exchanges, it may be possible that some of these costs and benefits apply to knowledge sharing in economic exchanges such as open networks as well since there could be overlapping motivations. Therefore, we suggest the relevant motivations from Tables 3 and 4 to explain solvers' knowledge provision behaviours in open networks.


Since the interaction between solvers and seekers is mediated, there is no common organisational context between them in our study. Therefore, organisational factors, i.e., identification, norm, centrality, organisational climate, and organisational support, are not adopted in this study. However, trust could be an important contextual factor for solvers and seekers to use open networks. Trust refers to the belief in others' good intent and concern, competence and capability, and reliability (Gefen, Benbasat, & Pavlou, 2008). It is developed based on past experience, reputation, and trust propensity. In our context, it is important for solvers to believe that they can sell their ideas securely via open networks and for seekers to believe that they can obtain the desired knowledge or technology. Therefore, trust should be relevant to solvers' and seekers' participation in open networks.

Table 4 Antecedents for knowledge contribution.

Dimension Constructs Authors Context

Benefit Organisational reward/extrinsic Kankanhalli et al. (2005a) Knowledge contribution in electronic

reward/career advancement and security knowledge repositories (EKRs)

Bock et al. (2005) Knowledge contribution

in organisations

Hargadon (1998) Knowledge sharing in

McKinsey and Andersen


Reciprocity Kankanhalli et al. (2005a) Knowledge contribution in EKRs

Knowledge self-efficacy

Enjoyment in helping others Chiu, Hsu, and Wang (2006); Knowledge contribution in

Hsu et al. (2007) online communities

Kankanhalli et al. (2005a) Knowledge contribution in EKRs

Enhanced reputation Wasko and Faraj (2005) Knowledge sharing in


Cost Codification effort Kankanhalli et al. (2005a) Knowledge contribution in EKRs

Loss of knowledge power

Cognitive effort Cillo (2005) Market knowledge sharing

within firms

Contextual . Trust Chiu et al. (2006); Knowledge contribution in

factors Hsu et al. (2007) online communities

Identification Chiu et al. (2006)

Norm Pro-sharing norm Kankanhalli et al. (2005a) Knowledge contribution in EKRs

Subjective norms Bock et al. (2005) Knowledge contribution in

Organisational climate Bock et al. (2005); Wasko organisations

Centrality and Faraj (2005)

Organisation support King and Marks (2008) Organisational contribution

via KMS

Table 5 Antecedents for knowledge seeking.

Dimensions Constructs




Contextual factors

Perceived ease of use/ease of knowledge access


Seeker knowledge growth

Perceived usefulness/perceived output quality/value of knowledge

Resource availability (time) Incentive availability Future obligation Perceived risk of knowledge

consumption Collaborative norms

Resource facilitating conditions Trust

Bock et al. (2006)

Watson and Hewett (2006) Bock et al. (2006) Bock et al. (2006); Desouza, Awazu, and Wan (2006) Bock et al. (2006); Kankanhalli, Tan, and Wei(2005b) Watson and Hewett (2006) Kankanhalli et al. (2005b)

Bock et al. (2006) Desouza et al. (2006)

Quigley, Tesluk, Locke, and Bartol (2007) Bock et al. (2006) Quigley et al. (2007); Watson and Hewett (2006)

Using electronic knowledge repositories (EKRs) for knowledge seeking Knowledge seeking in organisations Using EKRs for knowledge seeking

Using EKRs for knowledge seeking

Knowledge seeking in organisations Using EKRs for knowledge seeking

Using EKRs for knowledge seeking

Knowledge seeking in organisations

Using EKRs for knowledge seeking Knowledge seeking in organisations

Among the benefits and costs outlined in Table 4, factors that require a long term relationship to develop such as reciprocity and enhanced reputation are not adopted in the context of our study. Combining variables from Table 3 and Table 4, we propose extrinsic reward, enjoyment of solving a novel problem, skill enhancement, and knowledge self-efficacy as the benefit factors and loss of knowledge power, cognitive effort, and codification effort as the cost factors for open network usage for solvers.


The antecedents of knowledge seeking identified in Table 5 are mainly meant for individual seekers while our context involves organisational seekers. Therefore, factors such as self-efficacy, seeker knowledge growth, resource availability, incentive availability, future obligation, collaborative norms, and resource facilitating conditions are less relevant to our context. Since problems proposed by seekers are those which cannot be solved internally, the knowledge required for these problems is not well-known beforehand. As mentioned above, seekers' belief in the capabilities of open networks is an important factor for seekers' open network usage. The risk of knowledge consumption can be subsumed under the concept of seekers' trust in open network capabilities. The perceived ease of use and usefulness are two key features of IT systems (Davis, 1989), which determine their usage. However, these two potential antecedents of seekers' open network usage may depend on the knowledge brokering capabilities of open networks (Ye et al. 2012) which are introduced next.

Knowledge brokering

Opening up to the external world for knowledge sources involves many challenges including lack of common

knowledge or cognitive distance, shared vision and trust between knowledge source and knowledge seekers (Cillo, 2005). Other challenges include the complexity of knowledge to be transferred, the seekers' inability to value, assimilate, and apply external knowledge, and causal ambiguity of knowledge (Szulanski, 1996). In order to overcome these challenges, knowledge brokers have emerged in the market to serve as bridging ties for knowledge transfer from knowledge sources to knowledge seekers, and to help integrate external knowledge into knowledge seekers' innovation activities (Tiwana, 2008). These bridging ties leverage the heterogeneous knowledge from weak ties and complement strong ties for better innovations by brokering knowledge from where it is known to where it is not (Hargadon, 1998).

Knowledge brokers (KBs) are the third parties who connect, recombine, and transfer knowledge to companies to facilitate innovation (Hargadon & Sutton, 2000). They work close to their business clients to provide specific innovation solutions by helping transfer complex knowledge between different parties that are not directly related and rarely interact. For better knowledge absorption and integration for innovation, they translate and repackage the knowledge obtained from knowledge source for knowledge seekers (Cillo, 2005).

Moreover, KBs serve to overcome the frequent tradeoffs between the quality of ties, i.e., the ease and speed of knowledge transfer, and the innovativeness, i.e., the heterogeneity of knowledge source (Hargadon, 2003). Knowledge brokers help companies extend their knowledge seeking scope to these distant knowledge sources in different domains for more novel information and knowledge for innovation (Granovetter, 1973). By bridging otherwise disconnected domains, brokers overcome the structural isolation between different domains, and cognitive constraints that exist in the domains from which knowledge comes and to which it is applied. The vantage

network position enables them to overcome the local beliefs and actions of any one domain and to recognise the value of resources across domains. Besides, KBs serve as the bridging ties for organisations to access their cognitively distant knowledge sources without having to interact with them frequently (Hargadon, 2002). Mediated by knowledge brokers, knowledge seeking companies are spared from maintaining a strong tie with these cognitively distant knowledge sources since tie maintaining could be costly (Burt, 1992), while enjoying the benefits of the strong ties.

Knowledge brokers and innovation

By exploiting strategic positions spanning multiple domains or industries, knowledge brokers consistently create new products or services by recognising and transferring ideas from where they are known to where they are unknown. They facilitate clients' innovation by gaining access to a wide range of domains, bridging the disconnected domains, learning the diverse knowledge that resides within these different domains, linking this past knowledge residing in one domain to solutions for current problems in another domain, and, finally, implementing these new solutions in the form of new products or processes (Hargadon, 1998, 2002).

Gaining access to the ideas, artefacts, and people that reside within one domain and yet may be valuable in others (Burt, 1992) provides new patterns to recombine the existing knowledge. These patterns derive from the fragmented and isolated domains that are constructed with different habitualised actions, interactions, and beliefs of the inhabitants. These different actions and beliefs provide insights for those not in this particular domain and hence new patterns to recombine the existing resources.

Bridging otherwise disconnected domains, brokers form a strong bridge tie among these domains and benefit from moving resources from one group to another. They utilise their vantage network position to overcome the local beliefs and actions of any one domain and to recognise the value of resources across domains. This overcomes the structural isolation between domains, and cognitive constraints that exist between domains of the knowledge sources and knowledge seekers. This strong bridge tie is conducive to the knowledge transfer between domains.

Learning about problems and solutions from a particular domain increases brokers' range of responses to the demands of current and future projects (Hargadon, 2002). By working in many different domains, knowledge brokers are able to learn many different ways to see situations that inhabitants of a single domain take as given. These learning activities require knowledge brokers not only to acquire knowledge of existing resources within a particular domain but also to learn under what conditions their members experience the problems that reside in those domains, and what others in the organisation know. Moreover, learning about new resources and problems often gives new meanings to their past knowledge, particularly when the learning experiences are shared across the organisation. Conversely, past knowledge shapes the way individuals, project teams, and organisations learn about new resources and problems. This loop of learning generates new meaning of their

experience in previous domains. Thus, this learning process accumulates knowledge of the extant resources and problems of different domains in ways that enable it to become the raw material for innovation.

Linking is the process through which organisations recognise how old resources can address new and problematic situations by sharing their knowledge within the organisation. Knowledge brokers link their inventory of existing problem definitions and solutions to current situations through a process of analogical reasoning. Analogical reasoning involves recognising links between a current, problematic situation and recalled past problems and their solutions. It allows problem solvers to consider new definitions of the problems they are facing by linking their current situation to ones they have seen before. Using analogical reasoning, knowledge brokers frame the current situation in terms of a past problem in order to identify a set of past solutions that can be adapted to fit the new situation. They embed observed events in a context that gives them meaning. By framing problematic situations in the context of past problems, knowledge brokers identify a set of solutions normally associated through analogies to previously known problems.

Implementing is the process where organisations move from innovative ideas to accepted innovations by building new network ties and embedding the emerging recombinations within a new domain. Recognising and creating novel recombinations of existing resources is rarely enough; innovations are successful only to the extent that they are adopted by and alter the behaviours of their intended audience. To successfully implement innovations, knowledge brokers use their knowledge and networks to introduce these innovations into new domains and build supporting ties around them. The supporting ties that crystallise around a new combination of resources create the necessary conditions for turning initial innovations into enduring institutions, through diffusion and learning-in-use.

Virtual knowledge brokers

To extend the concept of knowledge brokers to online contexts, Verona, Prandelli, & Sawhney (2006) proposed the term virtual knowledge brokers (VKBs) to describe those who leverage the Internet to support third parties' innovation activities. They enable firms to extend their reach in engaging with customers and help them to complement their knowledge base. By utilising their special position in knowledge networks, VKBs bridge the differences between different worlds and enhance creativity by connecting previously separated nodes. They provide specific solutions to firms for their inter-industrial and inter-organisational exposure. Virtual knowledge brokers use the external knowledge for innovation. There are other related terms like crowd-sourcing (Von Hippel, 2001), open innovation (Chesbrough, 2003a) or user generated innovation (Von Hippel, 2005) that have been used to describe the process of utilising external knowledge sources for organisational task solving or innovation. In a sense, VKBs are one of the mechanisms for utilising external knowledge for innovation, which fall under the model of open innovation.

Open networks such as and NineSigma. com exemplify one type of open innovation mechanism.

They serve as virtual knowledge brokers by connecting companies with problems to a broad range of solvers from different domains and industries. They work closely with their client companies (seekers) to define the problems, find the possible solvers, filter the proposals and help intellectual property transfer from solvers to seekers. Therefore, open networks help seekers to connect to distant knowledge sources and facilitate their product and service innovations. The open networks' function of defining problems of knowledge seekers and matching problems with potential solvers facilitates the relevant knowledge flow to seekers. For example, in collaboration with InnoCentive, client companies have learnt to break up their problems in sophisticated ways to avoid revealing strategy and other proprietary information (Lakhani & Jeppesen, 2007). The function of filtering solvers' proposals alleviates the information load of seekers by weeding out irrelevant or low quality proposals. Serving as translators and interpreters requires knowledge of the perspectives of each user group, the ability to situate the meaning of the knowledge in that context, and the ability to communicate those meanings and their significance to other groups (Pawlowski & Robey, 2004).

Since open networks serve as virtual knowledge brokers, we consider their knowledge brokering capabilities as determinants of seekers' usage of open networks for innovation. Apart from social exchange and knowledge brokering theories, a third theoretical perspective i.e., exploration-exploitation dichotomy, can be used to explain seekers' organisational impediments to innovate, which may motivate them to use open networks.

Theories for open innovation

Apart from the above theories that can be used to explain knowledge transfer through open networks, there are several theories that have been used to explain open innovation. These include the exploration-exploitation dichotomy, open innovation model, and absorptive capacity theory. We will discuss these theories and the studies employing them in turn.

Exploration-exploitation dichotomy

The exploration-exploitation dichotomy was first proposed by March (1991) to explain organisational learning. It suggests that while firms are good at exploiting current capabilities, they struggle with exploratory tasks. Exploitation hones and extends current knowledge, seeking greater efficiency and improvements to enable incremental innovation. Exploration, on the other hand, entails the search and development of new knowledge, experimenting to foster the knowledge recombination needed for more radical innovation (Atuahene-Gima, 2005). Typically, an exploratory task renders a distant and uncertain return (Levinthal & March, 1993).

Exploratory technological innovations imply changes in the firm's existing processes, management composition, and resource allocation (Andriopoulos & Lewis, 2009). However, all of these are complex and inert systems that are unlikely to undergo radical changes in the short run. In

every innovating firm, rigidities to innovation are likely to exist. These rigidities can be explained by the firm's current organisational structure and strategy, which may impede innovation (Blumentritt & Danis, 2006), by its culture, which may favour exploitative activities while discouraging exploratory innovation (Jassawalla, Hemant, Sashittal, 2002), by innovation-specific individual-level problems such as the not-invented-here syndrome (Katz & Allen, 1982), or by resistance to changing environments (Gilbert, 2005). In such a situation, Chesbrough (2003b) argues, firms are prone to miss a number of opportunities because many will fall outside the organisation's current business or will need to be combined with external technologies to unlock their potential. Therefore, the literature converges in recommending organisational separation of exploration and exploitation by which the exploration of new opportunities is conducted in collaboration with external knowledge sources (Lavie & Rosenkopf, 2006; Rothaermel & Deeds, 2004; Wadhwa & Kotha, 2006). Thus, to integrate such external partners into the innovation process, open innovation may provide a way for the firm to overcome internal problems arising from a purely "closed" innovation approach.

Based on the exploration-exploitation dichotomy, Keupp and Gassmann (2009) suggested an alternative reason as to how and why firms differ regarding the extent to which they conduct open innovation activities. Specifically, they hypothesise that internal impediments to innovation will stimulate companies to search for external sources of knowledge or innovation. A firm that experiences impediments to innovation will be more likely to open up its innovatory activities than a firm that does not experience such impediments, and thus may pursue innovation alone. Opening up their innovation is an active response by firms to overcome internal rigidities caused by impediments to innovation. After analysing the data from a Swiss innovation survey, their study found that information-and-capabilities-related impediments and risk-related impediments stimulate companies to adopt inbound open innovation (openness of firms' external search processes). Inbound open innovation was measured by the width and depth of external knowledge searching. External search width refers to the number of external sources or search channels that firms rely upon in their innovative activities. External search depth refers to the extent to which firms draw deeply from the different external sources or search channels (Laursen & Salter, 2006).

However, conflicts between exploration and exploitation process during innovation need to be considered as well (Andriopoulos & Lewis, 2009). There are tensions between exploration and exploitation innovation for which companies must strike a balance as innovation tensions may trigger traps. Firms tend towards homogeneity, finding comfort as they develop mindsets and routines supporting one form of innovation, escalating their efforts in their preferred mode to the neglect of the other (Smith & Tushman, 2005). In this case, firms can innovate but cannot make any radical innovation.

Due to the possible influence of internal impediments on open innovation strategy adoption, we suggest that internal impediments can explain seekers' motivation of using open networks.

Open innovation model

In the past, many firms adopted the philosophy of self reliance in their internal R&D operations i.e., a closed innovation system (Chesbrough, 2003a). In otherwords, companies must generate their own ideas that they would then develop, manufacture, market, distribute, and serve themselves. Thus, companies invested more heavily in internal R&D than their competitors and they hired the best and the brightest to discover the best and greatest number of ideas, and get them to the market first. The benefits of innovation stemmed from aggressively controlling theirintellectual property to prevent competitors from exploiting it.

By contrast, models of open innovation offer the promise that firms can achieve a greater return on their innovative activities and their intellectual properties (IPs) by loosening their control over both. Compared to the self-reliant closed innovation model, the open innovation model stresses the importance of using a broad range of knowledge sources for a firm's innovation and invention activities, including customers, rivals, academics, and firms in unrelated industries while simultaneously using creative methods to exploit a firm's IP. It also underscores the importance of commercialising a company's in-house ideas to market by deploying pathways outside its current business e.g., through licencing, spin-offs or joint ventures (Chesbrough, 2006).

Open innovation requires systematically encouraging and exploring a wide range of internal and external sources for innovation opportunities, consciously integrating that exploration with firm capabilities and resources, and broadly exploiting those opportunities through multiple channels (West & Gallagher, 2006). Therefore, the open innovation paradigm goes beyond just utilising external sources of innovation such as customers, rivals, and universities (e.g., Von Hippel, 1998) and is as much a change in the use, management, and employment of IPs as it is in the technical and research driven generation of IPs.

In the open innovation model, there are two components: inbound open innovation and outbound open innovation (Chesbrough, 2006). Inbound open innovation is the practice of leveraging the discoveries of others. Through searching, acquiring, and integrating external knowledge or technology into internal R&D operation or licencing-in external technology, companies can unlock the potential of internal innovation. They can commercialise or learn new ways to reconfigure the existing knowledge allocation and exploitation for innovation. In our study, seekers' open network usage could be considered as inbound open innovation.

Outbound open innovation refers to externally commercialising a company's innovation through licencing-out, spin-offs, joint ventures, or alliances. It suggests that firms can look for external organisations with business models that are suited to commercialise its technology exclusively or in addition to its internal application (Chesbrough & Crowther, 2006). Firms can benefit from this process by licencing fees, shared risks, and extended capacity. These different streams of income create more overall revenue from the innovation (Lichtenthaler & Ernst, 2007).

Using the open innovation model, Laursen and Salter (2006) explored the relationship between openness of firms' external search strategies and their innovation performance. They proposed two dimensions of search for external knowledge i.e., breadth and depth of search strategies. Based on a large scale survey from 2707 manufacturing firms in the U.K., the study found that searching widely and deeply is curvilinearly related to innovation performance. This study suggests that moderate effort of searching widely and deeply across a variety of search channels can obtain ideas and resources that help firms explore and exploit innovative opportunities.

However, with the further increase in effort of search depth and width, innovation performance decreases. Laursen and Salter (2006) explain that innovation search is time consuming, expensive, and laborious, needing many resources. If companies can effectively and efficiently screen, assimilate, and exploit external knowledge for innovation, the resources and cost for searching will be covered by the benefits obtained from the innovation of products or services. If they lack the capability of exploiting external knowledge, opening-up to outside environment for knowledge turns out to be very costly.

Therefore, the relationship between open innovation strategy and innovation performance is dependent on firms' internal capabilities and resources, i.e., absorptive capacity and technology base. Since open networks serve as a mechanism of inbound open innovation, using open networks for innovation can be considered as a type of inbound open innovation strategy. Thus, we consider this open innovation model relevant to explain the influence of open network usage on innovation performance. The concept of absorptive capacity will be introduced in the following section.

Absorptive capacity

Absorptive capacity was first introduced by Cohen and Levinthal (1990) to denote the capabilities of the firm to innovate and, thus, to be dynamic. It consists of the capabilities to recognise the value of new knowledge, to assimilate it, and to apply it to commercial ends. As a byproduct of organisational R&D, absorptive capacity influences the innovation performance of the firm.

The basic assumption is that prior related knowledge determines a firm's level of absorptive capacity (Lane, Koka, & Pathak, 2006). Firms need some knowledge overlap with an external knowledge source to successfully absorb new knowledge, but a very strong overlap limits the possibilities of gaining new insights (Lord & Ranft, 2000). This path-dependent understanding is underlined in capability-based reconceptualisation of absorptive capacity (Zahra & George, 2002). Due to the underlying learning process, recent work has developed a process-based view of absorptive capacity. In the process based view, a firm's stock of prior knowledge constitutes the basis for knowledge flows within the three learning processes (Lane et al., 2006), i.e., explorative, transformative, and exploitative learning of absorptive capacity. Explorative learning refers to the capability of recognising and assimilating external knowledge. Transformative learning

denotes the capability of maintaining and reactivating assimilated knowledge, while exploitative learning reflects the capability of transmuting and applying internal knowledge for production (Lichtenthaler, 2009b).

Absorptive capacity has been applied and extended in different contexts. Forexample, Zahra and George (2002) first reconceptualised it as a dynamic capability that includes knowledge acquisition, assimilation, transformation, and exploitation capabilities. Based on this previous work, Todorova and Durisin (2007) reintroduced the capability of recognising the value of knowledge as a dimension of absorptive capacity and explicated the relationship between assimilation and transformation capabilities relying on the cognitive learning literature. Todorova and Durisin (2007) suggested that the relationship of different dimensions is not linear and the link between transformation and assimilation should be interactive, which reinforce each other.

The positive influence of absorptive capacity on innovation performance has been empirically tested by several studies (e.g., Jansen, van den Bosch, & Volberda, 2005; Lichtenthaler, 2009b). For example, Lichtenthaler (2009b) found that different components of absorptive capacity are complementary to each other to enhance innovation and organisational performance. Further, a high level of absorptive capacity guarantees the success of leveraging external and internal knowledge for innovation.

In other words, opening up internal R&D and connecting to outside knowledge sources does not guarantee successful knowledge transfer from knowledge sources to seekers and hence the innovation performance of seekers (Laursen & Salter, 2006). What matters is the absorptive capacity of seekers, which determines their capabilities to recognise potentially valuable knowledge (Cohen & Levinthal, 1990; Todorova & Durisin, 2007), establish a strong connection with the knowledge source through knowledge brokers to acquire the knowledge (Tiwana, 2008), assimilate and transform acquired knowledge for later use, and apply the knowledge forinnovation (Lichtenthaler, 2009b; Zahra & George, 2002).

As emphasised in the open innovation paradigm, in order to better leverage the inward knowledge flow for innovation, companies should develop their absorptive capacity to recognise, assimilate, and exploit the external knowledge for innovation. This supports the arguments proposed by Huston and Sakkab (2006) that the open innovation strategy does not rob the jobs of R&D researchers but contrarily emphasises their importance in leveraging external knowledge for innovation. Scientists and researchers inside the company are critically important in determining which problems should be broadcast and are needed to help implement the solutions into products (Lakhani & Jeppesen, 2007).

Therefore, absorptive capacity is considered as one of the factors that moderate the influence of open network usage strategy on innovation performance. The Todorova and Durisin (2007) model is useful to explain the influence of open network usage on innovation performance.

Study contributions

This article is expected to contribute in the following ways. First, building on the past literature, this study has

proposed various seekers and solvers' motivations of using open networks. Second, this article suggests the influence of different open network capabilities on the effectiveness of open innovation. Third, this study has attempted to explain the relationship between open innovation through open networks and firm's innovation performance. Fourth, this article explores the role of absorptive capacity in leveraging open innovation for organisational innovation and commercialisation. This attempts to address in part the knowledge gap of how companies leverage external innovative ideas, intellectual property, or technology for innovation and commercialisation. Fifth, it may serve to enrich the currently limited literature on open innovation in the information systems (IS) field, thereby paving the way for future research in this area.

Apart from the theoretical contributions, this article also attempts to contribute practically by providing practitioners with insights on how open networks facilitate open innovation in order to bring more value to companies, thereby better fulfilling the objectives of their implementation. It aims to provide suggestions to managers on paying attention to the unique factors for open-network innovation success and what aspects should be improved upon in order to produce more value from an open innovation strategy.


Allio, R. J. (2004). CEO interview: the innocentive model of open

innovation. Strategy and Leadership, 32(4), 4-9. Andriopoulos, C., & Lewis, M. W. (2009). Exploitation-exploration tensions and organizational ambidexterity: managing paradoxes of innovation. Organization Science, 20(4), 696-717. Atuahene-Gima, K. (2005). Resolving the capability-rigidity paradox

in new product innovation. Journal of Marketing, 69, 61-83. Blau, P. M. (1964). Exchange and power in social life. New York: John Wiley.

Blumentritt, T., & Danis, W. M. (2006). Business strategy types and innovative practices. Journal of Managerial Issues, 18(2), 274-291.

Bock, G. W., Kankanhalli, A., & Sharma, S. (2006). Are norms enough? The role of collaborative norms in promoting organizational knowledge seeking. European Journal of Information Systems, 15(4), 357-367. Bock, G. W., Zmud, R. W., Kim, Y. G., & Lee, J. N. (2005). Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87-111. Boudreau, K. J., & Lakhani, K. R. (2009). How to manage outside

innovation. MIT Sloan Management Review, 50(4), 69-76. Burt, R. S. (1992). Structural holes: the social structure of

competition. Cambridge, MA: Harvard University Press. Cassiman, B., &Veugelers, R. (2006). In search of complementarity in innovation strategy: internal R&D and external knowledge acquisition. Management Science, 52, 68-82. Chesbrough, H. (2003a). Open innovation. Cambridge, MA: Harvard University Press.

Chesbrough, H. (2003b). The era of open innovation. MIT Sloan

Management Review, 44(3), 35-41. Chesbrough, H. (2006). Open business models: How to thrive in the new innovation landscape. Boston, MA: Harvard Business School Press.

Chesbrough, H., & Crowther, A. K. (2006). Beyond high tech: early adopters of open innovation in other industries. R&D Management, 36, 229-236.

Chiu, C. M., Hsu, M. H., а Wang, E. T. G. (2006). Understanding knowledge sharing n virtual communities: an integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872-1888.

Choudhury, V., а Sampler, J. L. (1997). Information Specificity and environmental scanning: an economic perspective. MIS Quarterly, 21(1), 25-53.

Cillo, P. (2005). Foster market knowledge use in innovation: the role of internal brokers. European Management Journal, 23(4), 404-412.

Cohen, W. M., а Levinthal, D. A. (1990). Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35, 128-152.

Davis, F. D. (1989). User acceptance of Computer technology: a Comparison of two theoretical models. Management Science, 35, 982-1003.

Desouza, K. C., Awazu, Y., а Wan, Y. (2006). Factors governing the consumption of explicit knowledge. Journal of the American Society for Information Science and Technology, 57(1), 36-43.

Dodgson, M., Gann, D., а Salter, A. (2GG6). The role of technology in the shift towards open innovation: the case of Procter and Gamble. R&D Management, 36(3), 333-346.

Ebner, W., Leimeister, J. M., а Krcmar, H. (2009). Community engineering for innovations: the ideas competition as a method to nurture a virtual community for innovations. R&D Management, 39(4), 342-356.

Enkel, E., Gassmann, O., а Chesbrough, H. (2009). Open R&D and open innovation: exploring this phenomenon. R&D Management, 39(4), 311-316.

Gefen, D., Benbasat, I., & Pavlou, P. (2008). A research Agenda for trust in online environments. Journal of Management Information Systems, 24(4), 275-286.

Gefen, D., & Ridings, C. M. (2002). Implementation team responsiveness and user evaluation of customer relationship management: a quasi-experimental design study of social exchange theory. Journal of Management Information Systems, 19(1), 47-69.

Gilbert, C. (2GG5). Unbundling the structure of inertia: resource versus routine rigidity. Academy of Management Journal, 48(5), 741 -763.

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 6, 1360-1380.

Grant, R. M., а Baden-Fuller, C. (2GG4). A knowledge accessing theory of strategic alliances. Journal of Management Studies, 41(1), 61-84.

Hargadon, A. B. (1998). Firms as knowledge brokers: lessons in pursuing continuous innovation. California Management Review, 40(3), 2G9-227.

Hargadon, A. B. (2GG2). Brokering knowledge: linking learning and innovation. Research in Organizational Behavior, 24, 41-85.

Hargadon, A. B. (2GG3). How breakthroughs happen: The surprising truth about how companies innovate. Cambridge, MA: Harvard Business School Press.

Hargadon, A. B., а Sutton, R. (2GGG). Building an innovation Factory. Harvard Business Review, (May-June), 157-167.

Hauser, J., Tellis, G., & Griffin, A. (2006). Research on innovation: a review and agenda for marketing science. Marketing Science, 25(6), 687-717.

Herzog, P., & Leker, J. (2010). Open and closed innovation; different innovation cultures for different strategies. International Journal of Technology Management, 52(3), 322-343.

He, Z. L., а Wong, P. K. (2GG4). Exploration vs. exploitation: an empirical test of the ambidexterity hypothesis. Organization Science, 15, 481-494.

Hsu, M. H., Ju, T. L., Yen, C. H., & Chang, C. M. (2007). Knowledge sharing Behavior in virtual communities: the relationship between trust, self-efficacy, and outcome expectations. International Journal of Human-computer Studies, 65(2), 153-169.

Huston, L., & Sakkab, N. (2006). Connect and develop: inside Procter & Gamble's new model for innovation. Harvard Business Review, 84(3), 58-66.

Jansen, J. J. P., van den Bosch, F. A. J., & Volberda, H. W. (2005). Managing potential and realized absorptive capacity: how do organizational antecedents matter? Academy of Management Journal, 48, 999-1015.

Jassawalla, A., Hemant, R., & Sashittal, C. (2002). Cultures that support product-innovation processes. Academy of Management Executive, 16(3), 42-54.

Kankanhalli, A., Tan, B. C.-Y., & Wei, K.-K. (2005a). Contributing knowledge to electronic knowledge repositories: an empirical investigation. MIS Quarterly, 29(1), 113-143.

Kankanhalli, A., Tan, B. C. Y., & Wei, K. K. (2005b). Understanding seeking from electronic knowledge repositories: an empirical study. Journal of the American Society for Information Science and Technology, 56(11), 1156-1166.

Katz, R., & Allen, T. J. (1982). Investigating the not invented here (NIH) syndrome: a look at the performance, tenure, and communication patterns of 50 R&D project groups. R&D Management, 12, 7-12.

Keupp, M. M., & Gassmann, O. (2009). Determinants and archetype users of open innovation. R&D Management, 39(4), 331-341.

King, W. R., & Marks, P. V. (2008). Motivating knowledge sharing through a knowledge management system. Omega, 36, 131-146.

Lakhani, K. R., & Jeppesen, L. B. (2007). Getting unusual suspects to solve R&D puzzles. Harvard Business Review, 85(5), 30-32.

Lane, P. J., Koka, B., & Pathak, S. (2006). The reiflcation of absorptive capacity: a critical review and rejuvenation of the construct. Academy of Management Review, 31, 833-863.

Laursen, K., & Salter, A. J. (2006). Open for innovation: the role of openness in explaining innovation performance among UK manufacturing flrms. Strategic Management Journal, 27,131-150.

Lavie, D., & Rosenkopf, L. (2006). Balancing exploration and exploitation in alliance formation. Academy of Management Journal, 49, 797-818.

Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14, 95-112.

Lichtenthaler, U. (2008). Open innovation in practice: an analysis of strategic approaches to technology transactions. IEEE Transactions on Engineering Management, 55(1), 148-157.

Lichtenthaler, U. (2009a). Outbound open innovation and its effect on flrm performance: examining environmental influence. R&D Management, 39(4), 317-330.

Lichtenthaler, U. (2009b). Absorptive capacity, environmental turbulence and the complementarity of organizational learning processes. Academy of Management Journal, 52(4), 822-846.

Lichtenthaler, U., & Ernst, H. (2007). External technology commercialization in large flrms: results of a quantitative benchmarking study. R&D Management, 37(5), 383-397.

Lord, M. D., & Ranft, A. L. (2000). Entering emerging international markets: sharing local knowledge within the flrm. Journal of International Business Studies, 31, 573-589.

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71-87.

Molm, L. D. (1997). Coercive power in social exchange. New York: Cambridge University Press.

Pawlowski, S. D., & Robey, D. (2004). Bridging users Organizations: knowledge brokering and the work of information technology professionals. MIS Quarterly, 28(4), 645-672.

Quigley, N. R., Tesluk, P. E., Locke, E. A., & Bartol, K. M. (2007). A multilevel investigation of the motivational mechanisms underlying knowledge sharing and performance. Organization Science, 18(1), 71-88.

Rohrbeck, R., Hölzle, K., & Gemünden, H. G. (2009). Opening up for competitive advantage - how Deutsche Telekom creates an open innovation ecosystem. R&D Management, 39(4), 420-430.

Rothaermel, F. T., & Deeds, D. (2004). Exploration and exploitation alliances in biotechnology: a system of new product development. Strategic Management Journal, 25, 201-221.

Smith, W. K., & Tushman, M. L. (2005). Managing strategic contradictions: a top management model for managing innovation streams. Organization Science, 16(5), 522-536.

Szulanski, G. (1996). Exploring internal stickiness: impediments to the transfer of best practice within the Arm. Strategic Management Journal, 17, 27-43.

Tiwana, A. (2008). Do bridging ties complement strong ties? An empirical examination of alliance ambidexterity. Strategic Management Journal, 29, 251-272.

Todorova, G., & Durisin, B. (2007). Absorptive capacity: valuing a reconceptualization. Academy of Management Review, 32, 774-786.

Travis, J. (2008). Science by the mass. Science, 319(March), 1750-1752.

Valikangas, L., & Gibbert, M. (2005). Boundary setting strategies for escaping innovation traps. Sloan Management Review, 46(3), 58-65.

Verona, G., Prandelli, E., & Sawhney, M. (2006). Innovation and virtual environments: toward virtual knowledge brokers. Organization Studies, 27(6), 765-788.

Von Hippel, E. (1998). Economics of product development by users: the impact of 'sticky' local information. Management Science, 44(5), 629-644.

Von Hippel, E. (2001). Innovation by user communities: learning from open-source software. MIT Sloan Management Review, 42(4), 82-86.

Von Hippel, E. (2005). Democratizing innovation: Users take center stage. Boston, MA: MIT Press.

Von Krogh, G., & Von Hippel, E. (2006). The promise of research on open source software. Management Science, 52(7), 975-983.

Von Stamm, B. (2008). Collaborating for innovation, managing innovation, design and creativity (2nd ed.). New Jersey: John Wiley and Sons.

Wadhwa, A., & Kotha, S. (2006). Knowledge creation through external venturing: evidence from the telecommunications equipment manufacturing industry. Academy of Management Journal, 49, 819-835.

Wasko, M. M., & Faraj, S. (2000). 'It is what one does': why people participate and help others in electronic communities of practice. Journal of Strategic Information Systems, 9(2/3), 155-173.

Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35-57.

Watson, S., & Hewett, K. (2006). A Multi-Theoretical Model of Knowledge Transfer in Organizations: Determinants of Knowledge Contribution and Knowledge Reuse. Journal of Management Studies, 43(2), 141-173.

West, J., & Gallagher, S. (2006). Challenges of open innovation: the paradox of firm investment in open source software. R&D Management, 36(3), 319-331.

Xie, F. T., & Johnston, W. J. (2004). Strategic alliances: incorporating the impact of e-business technological innovations. The Journal of Business Industrial Marketing, 19(3), 208.

Ye, H., Kankanhalli, A., & Yang, Z. (2012). Knowledge brokering for open innovation: a case study of innovation intermediaries. In: International conference on information systems, Orlando.

Zahra, S. A., & George, G. (2002). Absorptive capacity: a review, reconceptualization, and extension. Academy of Management Review, 27, 185-203.