Keywords

1 Introduction

Nowadays, Innovation is crucial for the enterprises survival and current economic environment demands the best ways of achieving it. Open innovation is a useful strategy enterprises adopt to seek knowledge and value from outside of their boundaries. In such way, enterprises share technology and knowledge with their suppliers and customers to develop innovative and improved products, and leveraging value creation. Assuming this strategy, enterprises may even disclose secrete technology or free intellectual property, for the sake of obtaining such benefits. But this is not done without risks, as open innovation projects might fail for diverse reasons. For instance, the appropriation of others’ know-how or trying to take unmerited benefits from a technology are considered disruptive behaviors in open innovation projects.

To effectively benefit from open innovation, it is recommended the utilization of adequate risk models. Open innovation is essentially a collaborative strategy. There is a significant number of risk-like assessment approaches for collaborative networks already, but few specifically addressing risk in open innovation.

Our aim is to develop research work towards developing a risk assessment approach for open innovation. As explained in Sect. 3, this problem has had little attention by the research community. Given this scarcity, we considered a research question, which can be stated as: How to model open innovation risk assessment? Our effort is, therefore, devoted to contribute to provide an answer to this question.

Our research method starts by problem formulation. It is followed by the conceptualization of open innovation and risk and by the related research analysis. The next phase is devoted to work on risk modeling in open innovation. In this regard, our risk assessment approach is based on a widely used risk model, namely, FMEA. The obtained approach is illustrated through an application example, followed by result analysis.

In the next section, we provide the relevant concepts regarding open innovation and risk. We briefly describe a few existing risk assessment models. A research on the state of the art related to risk in open innovation is presented in Sect. 3, aiming at identify and characterize risk assessment models in open innovation. In Sect. 4, we illustrate how we could model risk assessment using the FMEA model. We also provide an illustrative application example. We finish the results analysis, conclusions and future work.

2 Base Concepts

2.1 Open Innovation

The concept of open innovation, coined by Chesbrough in 2003 [1], has become relevant in practice and in academia. Open innovation is now a mainstream research focus in innovation literature [2]. One of the reasons for the advance of the open innovation paradigm is based on the principle that today´s problems are often complex and require a wide range of expertise. To create and implement solutions it is required that collaboration occurs among different areas and people with a variety of experience and knowledge. It is very difficult for organizations to build solutions and create knowledge by themselves. The basic assumption of open innovation is opening up the innovation process. This means to treat research and development as an open system. Open innovation is usually put in contrast to closed innovation, supposedly its precursor, where companies generate their own innovation ideas, and then develop, build, market, distribute, service, finance, and support them on their own [1].

Open innovation suggests that valuable ideas can come from inside or outside the company as well. The mobility of competencies, the increasing presence of venture-capital, the emergence start-ups and the role of university research and its linkages give rise to a more open approach towards innovation. Collaboration is argued to facilitate the production of new knowledge more than just transferring it. Chesbrough [3] has referred to open innovation not only as being a business model, but also as a way of promoting and sharing knowledge. This means that open innovation can go beyond the idea of knowledge transaction and of in/outsourcing ideas, but promote the creation of new knowledge.

Companies can develop and bring ideas to the market using channels outside of their current businesses, in order to generate value for the organization. A path for accomplishing this can involve new businesses and licensing agreements. These might be financed and staffed with the existing company’s personnel. In addition, ideas can also be originated outside the company and be brought inside it, for commercialization [4]. In other words, the connection between a company and its environment is more permeable, enabling innovative ideas to transfer easily where boundaries once existed.

As argued by Felin and Zenger [5], the mechanisms for accessing external knowledge and promoting open innovation encompass a range of alternatives including alliances and joint ventures, corporate venture capital, licensing, open source platforms, and participation in various development communities. Each of the modalities brings distinct benefits, implies distinct levels of openness, and various risks. For instance, they vary in terms of intellectual property ownership, from Joint-ventures, created by formal-agreements, and “open-source models”, in which IP is given away to a (large and open) software development community.

An open innovation strategy can generate a positive result, but, it also involves some risks. Therefore, companies should have a policy to secure open innovation within their organization, to create interfaces and make achievements measureable. The way employees manage their external partners is also very important and plays a central role.

2.2 Risk Models in Engineering

Proceeding as specified in our research method, we now address the concept of risk in an engineering context. To overcome global competition and rapid technological advances, in order to predict and positively respond to changes, the development of organizational capability to innovate has become one of the prime strategy in SMEs. This has been done despite the lack on practical models, metrics and tools to assist their risk management efforts [6].

For many years, organizations risk has been seen mainly as a combination of the probability of occurrence of harms and the severity of these harms. Nowadays, such in project management, risk is viewed as related to uncertain events or conditions that, when they occur, pose positive or negative effects on projects objectives. Similarly, as the uncertainty associated with innovative processes is bonded not only to inherent risk of failure, but also to inherent chance of success, these subsequently bring on the necessity of adequate risk management in innovative processes [7]. Thereby, and since the purpose of an integrated risk management is to facilitate innovation rather than stifle it, innovating firms require a strategy not of risk avoidance, but of early risk diagnosis and management [8, 9], which should be spread through organizations and collaborative networks.

Based on general project management practices, several frequently used risk assessment models can be identified, such as the Balanced Scorecard (BSC), Failure Mode Effects Analysis (FMEA), Fault Tree Analysis (FTA), Analytic Hierarchy Process (AHP) and Risk Diagnosing Methodology (RDM).

The BSC model is typically used to facilitate the monitoring of the firm’s success factors, which can be viewed as opportunities as well as risks, meaning that the BSC is by nature an instrument close to the risk’s grounds function [10].

Another commonly used risk assessment approach is the Failure Mode Effects Analysis (FMEA), which is a systemic approach suited to help identify and reduce critical aspects in early stages of products and processes conception. Due to its role in the context of our research work, this risk assessment technic is explained in more depth in the next section.

The Fault Tree Analysis (FTA) can be both used as a risk identification method or as a risk analysis instrument. In this approach, the probability of negative events can be estimated and their causes deducted from a named Fault Tree, in which the probabilities of alternative situations are assessed. These situations are organized using Boolean logic, in which lower-level events are fed into upper-level ones [10].

Another used risk assessment approach is the Analytic Hierarchy Process (AHP). AHP is considered a multivariate analysis technique that aims to decrease the randomness of subjective assessments, by having in consideration different objectives grounded on distinct criteria [11]. It is predominantly used in scenario selection and evaluation [12].

Another risk assessment approach is Risk Diagnosing Methodology (RDM) which main purpose is to help provide strategies that will support and improve the chance of projects success, by identifying and managing their potential risks [8, 13]. It is used to support the systematic diagnosis of companies, considering issues such as consumer and trade acceptance, commercial viability, competitive responses, external influential responses, product technology and manufacturing technology [8], in which the assessment of project risk is determined not only by risk likelihood and its effects, but also by the companies’ ability to influence the course of the risk actions.

2.3 The FMEA Risk Assessment

FMEA is widely used risk assessment approach. It was original developed inside Aeronautic Industry in the 50’s to guide the design process. But it has been widely used in a broader sense, not only to assess physical systems but also organizational ones, such as those in the areas of Knowledge Management [14] and supply-chain management [14]. The FMEA can be used as a design tool to systematically analyze postulated component failures and identify the resultant effects on system operations. A failure at a lower level may very well cause a larger failure on the higher level. Therefore, it is essential to find them as fast as possible [16].

With FMEA, we can quantify the risk level of each identified failure, whether known or potential. Then, an estimate of its likelihood of occurrence, severity, and detectability is made for each one. At this point, an evaluation of the necessary actions is performed, namely, they can be taken, planned or ignored. The emphasis is to minimize the probability of failures or to minimize their effects. The main constituents of the FMEA model are described as:

Failure - Event/process in which any part of the organizational system does not perform according to the prescribed behavior. Example: absence of knowledge sharing when it was expected.

Failure mode - The specific manner by which a failure occurs in terms of the failure of some part of the organization. It shall at least clearly describe the end state of the item under consideration. It is the result of the failure mechanism (see next). Example: An enterprise owing key knowledge left the project.

Failure cause and/or mechanism - Defects/problems detected in the elements of the organizational system which are the underlying cause or sequence of causes that initiate behaviors leading to a failure mode over a certain time. A failure mode may have several causes. Example: Inadequate organizational rules adoption may cause network weaknesses.

Failure effect - Immediate consequence of a failure. Example: Lack of knowledge sharing decreases the quality of a project and increases its duration.

Likelihood of occurrence (L) - The Likelihood of occurrence is the probability that a specific failure mode, which is the result of a specific cause under current open-innovation network, will happen. Failure Likelihood is a relative ranking within the scope of an individual FMEA. A suggested likelihood scale is given in Table 1.

Table 1. Risk guidelines rank.
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Severity (S) - Severity is an assessment of the most serious effect for a given failure mode. Example: The severity of problematic partners can be reduced through their exchange. If such an exchange is attainable, the failure can be minimized eliminated. Severity is also a relative ranking within the scope of an individual FMEA.

Detectability (D) - Detectability is an assessment of the ability to identify any potential failure modes, case they occur. Detectability is a relative ranking within the scope of an individual FMEA.

The suggested risk guidelines for severity, occurrence and detection are given in Table 1.

Risk Priority Number (RPN) - The Risk Priority Number defines the priority of each failure. RPNs have no value or meaning by themselves. They are used only to rank (define) the potential open- innovation network deficiencies. The RPN is calculated by multiplying these three ratings:

$$ {\text{RPN}} = {\text{Failure}}\;{\text{Likelihood}} \times {\text{Severity}} \times {\text{Detectability}}. $$
(1)

In this equation, failure Likelihood, Severity and detectability must have a value greater than zero. A suggested RPN ranking is provided in Table 2.

Table 2. Risk category guidelines.
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Recommended Action - The recommended action may be specific action(s) or it may correspond to major changes in the operating process of an open innovation project or network. The idea of the recommended actions in FMEA is to reduce the severity, occurrence, detection or them all.

3 Depicting Existing Approaches of Risk Assessment in Open Innovation

During our research work, it was relatively difficult to find related research concerning risk assessment in open innovation. This difficulty led us to formulate the hypothesis that such models might be currently scarce. But this is an assumption that is difficult to demonstrate. Frequently in science, we can more easily prove that something exists than proving that it does not [17, 18].

In order to overcome this difficulty, we devised an approach to estimate the amount of research work concerning risk in open innovation. For such, a small content analysis based on the utilization of “Publish or Perish” search engine was performed.

“Publish or Perish” allows flexible searches of published research works. In our approach, we looked for papers with certain words in their titles. For instance, when a research intends to publish a paper related to risk modeling or assessment, the word “risk” or “uncertainty” is likely to appear in its title. Similarly, this is also true for researchers aiming to write papers on innovation. In this case, the word “innovation” or its synonymous would appear in the title.

The search was restricted to publications written since 2004, as the term “open innovation” was coined in 2003. After initial trials, we perceived that many results were unrelated to open innovation, so we tuned the search engine to filter out papers containing the words: “financial”, “bank”, “credit”, “price”, “climate” and “drug”. We performed similar searches for words in areas that are considered consolidated, to serve as a comparison basis. The obtained results are summarized in Table 3.

Table 3. Publications regarding risk in open innovation, with comparison examples.
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The mentioned table is split in three groups of results. The first group indicates there is a significant number of papers addressing risk in either closed and open innovation, and that some of them (could be up to 48) are more specific to risk models. The second group indicates there are few papers which consider risk in open innovation, about 7, and that only one has got the word “model” in title. Concerning risk, such areas of collaborative networks also present similar numbers. The last group of results serves as comparison basis, as it provides figures for areas that are more stabilized. The approach can be repeated and extended with more words. For instance, the number of papers with “uncertainty” and “innovation” in their titles was 308. But if we include the word “open”, we only obtain 6 publications. A reader is right stating that the approach may not provide very rigorous figures. But the divergence of values between open innovation and other consolidated areas provides support to our claim, that such models for risk in open innovation might be scarce.

As to establish a theoretical bases for our research in open innovation risk assessment models, we performed an analysis of the more promising publications among the few ones which were obtained during our context analysis:

Research works addressing risks, whiteout risk assessment models: In [19], a risk-based technology management approach was proposed. In this approach, risk is considered as an inherent aspect of the product development. It simultaneously considers both quantitative innovation objectives and quantitative product delivery objectives, such as cost, schedule and performance, enabling to establish formal quantitative technology innovation objectives and to track and monitoring them during a product development cycle. In [20], the external technology dependence, complex process management, difficulty in intellectual property protection, market information leakage, and mismatched resources capacity are identified as situations that pose risks in open innovation projects. In [21], several factors related to intellectual property competition in open innovation are highlighted. The study reveals the likelihood that open and proprietary competitors will clash, according to the industry type, e.g. radio and television, Medical, Electric motors, food and beverages, etc.

Identified risk models in open innovation: The research work in [22] is focused on the risk evaluation of customer integration in new product development. A number of risk factors of customer integration in new product development were identified, namely, organization risk, capacity risk, knowledge risk, and market risk. Their goal was to develop a risk evaluation method. The approach is based on rough set theory to handle vagueness. In [23], a research was conducted towards developing methodologies for managing risks of open-source software adoption in a context of an ecosystem of developers. The approach combined risk monitoring methods in order to provide early warnings of risks and their mitigation. The developed tools also included Bayesian networks and social network analysis. In [24], a risk management approach for crowdsourcing innovation is proposed. It provided overall guidelines to managing risks associated with crowdsourcing strategy, and a risk model suited for small and medium enterprises.

4 Application of FMEA to Open Innovation

4.1 Factors of Risk in Open Innovation

As mentioned in previous section, there are many definitions of risk according to the respective contexts in consideration. For all that matters in this research work, we can assume one of these general descriptions, in which risk can be seen as a probability or threat of damage, injury, liability, loss, or any other negative occurrence that is caused by external or internal vulnerabilities, and that may be avoided through preemptive action [25].

There are several innovation modalities, which as described before, are characterized by several aspects, like degree of openness to external partners. Each of these modalities pose distinct risk types. Envisaging a risk assessment instrument requires the identification of these risk types. Table 4 presents an initial characterization of these risks, which affect enterprises participating in open innovation.

Table 4. Situations posing risks in innovation, based on [20].
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For each risk of a failure, we can identify its causes, its effects and corresponding degree of impact, and the likelihood of its occurrence, as illustrated in Table 5.

Table 5. Characterization of open innovation risks
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4.2 Assessing Risk Example

The information regarding the risk factors described in previous section can be used to illustrate how we could assess risks in open innovation using.

Let us suppose the existence of an open innovation project aiming at developing an Internet of Things application for health care. In an initial stage of the project, the set of potential failures where identified and analyzed. The corresponding values for the likelihood of occurrences, severity and detectability of each failure were estimated. The obtained risk assessment results are presented in Table 6. The values in the table were merely chosen for illustrative purposes.

Table 6. Open innovation risk assessment illustration with FMEA method
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The obtained risk priority numbers provide indication of which failures must receive more attention. For instance, the RPN for “uncooperative behavior” is low, while the risk of abuse of IP by peers is extreme (see Table 2). Based on this assessment, a project manager can take preventive actions in order to minimize the likelihood of these failures and minimize the effects of their eventual occurrence. For instance, reducing the risk that some peers may abuse of intellectual property during the project implies establishing agreements with effective rules to protect IP, as well as the monitoring processes to ensure the compliance with these rules.

5 Conclusions

We described an approach for risk assessment towards providing an answer to the formulated research question, which is concerned on how to access risk in open innovation. Although existing research works address risk in open Innovation, specific works considering risk assessment models seem scarce.

After an initial conceptualization and related research analysis, we identified illustrative examples of failures in open innovation projects from literature. As a way to illustrate how to assess open innovation risks, we characterized these failures according to one widely used risk model, namely the FMEA. To our knowledge, the FMEA model had never been used in the concrete “open innovation” realm. An application example of FMEA illustrated the approach.

The study and development of our approach led us to conclude that it is necessary further research for an adequate open innovation risk assessment, including a more comprehensive characterization of open innovation risks. In this regards, we should assume a more holistic perspective. Furthermore, open innovation is inherently collaborative. Therefore, one line of research for future work may include the adaptation of existing collaborative risk models, so they could suit in open innovation. In the area of collaborative networks, there is a variety of collaboration-related key performance indicators already, as well as assessment models of more soft nature, such as value systems, benefits sharing methods, and collaboration preparedness. Encompassing these elements into a tailored and holistic approach to risk assessment in open innovation is planned for the next phase of this research work.