Innovation Policy – From monetary incentives to behavioural and experimentation

When we think of policies, we inevitably think of taxes or fiscal stimulus. Innovation is certainly no stranger. The promotion innovation is often implemented as a tax relief for investments categorised as innovation.   

I guess we are all immediately able to recognise the limitations and shortcomings of this practice. Just to follow with the line of reasoning above, once the tax relief is effective, suddenly many investments are re-labelled as innovation investments or if the implementation is costly and the elasticity of the sector is low … they fail to attract companies that want to implement it.

We all are aware that monetary incentives go as far as they go and very often backfire. This is particularly true when we deal with a complex system where solutions vary a lot in their effectiveness as a result of small changes in either their design or implementation.

Complex systems are the result of multiple factors interacting together and many agents that try to adapt to them. Markets and particularly innovation, are good examples. As a result of this interaction, excellent solutions are close to the ones that don’t work, there is not a smooth path, an incremental way to approach best solutions.

In addition to that, the new all digital world makes more necessary to put in place specific policies. We need to promote apps that use Open Data or innovation in citizenship by developing new participatory systems or the change in business models in the electrical grid allowing co-generation. All this is certainly very specific, very precise to be able to be addressed by generic monetary stimulus.

Therefore leading agencies and leading cities are promoting new types of policies where the monetary incentive takes the backseat.

A good example of this is the use of challenges – competitions – in order to promote innovation in a particular field. This is not new, the XIX century is full of them, but actual IT platforms make it straightforward and global. DARPA competitions or any Open Data or apps4x challenge such as Bigapps, apps4Amsterdam, apps4Barcelona, apps4Finland, etc…

This type of policies draw more on the behavioural aspect than on monetary incentives, and they backfire less. A good example of these policies is the use of the concept of free versus a small payment. Humans don’t react linearly between free and small amounts, free always draws more than a proportional level of attention.

Therefore the use of small payments is useful in situations such as traffic congestion. Because traffic congestion is also not linear, a proportionally small reduction of it produces a more than significant impact, the use of small payments can lead to this small reduction and therefore to solving a problem that looked insolvable. A good example of this is Stockholm, where this small tax was introduced, taken away and reintroduced, producing a kind of natural experiment. And by the way, surveys show that the population is happy about the tax (it is important that the tax doesn’t limit the behaviour of a sector of the population because of affordability … this is certainly not the case of Stockholm).

Therefore it looks like this kind of problems are better solved by using behavioural policies. However, if this is a complex system how can we be sure that they are appropriate?

The easy and complex answer is that you cannot. In complex systems is very difficult to predict the REAL outcome without trying, therefore experimentation is a must. Experimentation can we implemented in multiple forms but it is the only way to really learn about collateral effects and overall effectiveness and experimentation has to lead to getting rid of the policies that don’t work, modify and perfect the ones that are promising and strength the ones that work.

Policies have to be easily understood and known by the actors in order to be effective. Nothing more useless that an unknown policy or one that only tax-experts are aware of. This is the reason why they must be simple, limited in number and easy to understand. Attention, particularly now, is scarce.

 Summarizing, the future of innovation policy is headed towards simple, limited in number, behavioural and experimentation enabled by IT and IT platforms that aim to address specific sectors and needs. This is certainly a dramatic change and we look at the reality of our legislation system and the organizations that implement policy … a long way to go. However, I suspect that it is the only effective one …

Esteve Almirall

Uncategorized | Leave a comment Permalink

How Important is Open Innovation for Latin America?

(The following article is reproduced courtesy of Exnovate www.exnovate.org and was originally published in their Newsletter on 1st March 2013).

 

Latin America is struggling to find ways to accelerate the development of modern innovation management strategies and practices by private firms. Available indicators of innovation activity show very low levels. For example, in a large sample innovation survey in Colombia, only 0,6% of manufacturing firms reported that developed a new product for the international markets in 2009 or 2010. Several governments in the region have earmarked substantial resources for the purpose of increasing innovation in business organizations, but the results are still far from the levels that would be necessary in order to accelerate economic growth in a sustainable way. This brief text proposes that re-setting the terms of the discussion       by introducing an Open Innovation framework should reduce the distances that today separate private and public actors, lowering transaction costs and accelerating learning and knowledge absorption about innovation in private firms.

This is an urgent issue for the region. For the first time in decades, Latin America has managed to navigate successfully the turbulence associated to a major international economic crisis, so the global deceleration process that started in 2008 did not produce a downturn in the region. In general terms, this happened because of sound macroeconomic policies and because of the strength of exports of natural resources. This has allowed the region to enjoy a relative level of prosperity while surrounded by a sea of turbulence. However, these fortunate circumstances will not last forever. If Latin America does not take advantage of favourable present conditions to build the foundations of solid sustainable growth, dire consequences will be here after a few years. Sooner or later, commodity price cycles will turn against the region and this episode of prosperity will prove to be short lived. If we do not figure out how to accelerate the shift of our economies from commodities towards innovative, high value products, history will repeat itself.

This is a multi–faceted problem. Low productivity and scarce innovation have been historically endemic to the region. Here I wish to highlight one aspect of the situation, which is the low impact of innovation in the region in terms of development of innovation capabilities by private firms, despite growing resources assigned to this effect.

A brief consideration of the metrics that each side uses to address the issue illustrates the difficulties in this dialogue.   While policy makers in Latin America recognize that innovation is a systemic problem, the metrics they use to gauge the advancement of innovation are still locked in a linear paradigm, where R&D is at the root of the generation of new technologies that are later incorporated into new products.  A recent review of innovation activity in Latin.

America by the Inter American Development Bank focused on the gaps between the innovation performance of Latin American countries in comparison to other regions of the world. The authors explicitly argue that this linear model is not a good expression of true innovation dynamics. However, the fact remains that the available metrics were developed out of the linear perspective. Therefore, the state of the art is an analysis where R&D is of paramount importance for examining innovation in a region where, given the key sectors in the economic structure, R&D is quite limited. The focus on this kind of metrics is also typical of the indicators on innovation used by the OECD. Although that organization has done substantial efforts recently to introduce new indicators that reflect other views on innovation, such indicators are far from mainstream use.

 Business leaders who engage in innovation set their priorities using very different metrics. Managers want to talk about the composition of their innovation portfolios, the contribution to the bottom line of products launched over the previous 18 to 36 months, the number of ideas in the pipeline, and so on. Very few business leaders in Latin America, in very few sectors, have high priorities for R&D, although they may have ambitious innovation goals. In a region where services and natural resources sectors may contribute more than 70% of GDP in several countries, business people find it hard to digest the idea that in order to achieve high aspirations in innovation they need to focus on R&D.

Therefore, efforts to enhance the spread of innovation in Latin America quickly reach a situation where dialogue among key actors becomes extremely difficult. From the stand point of policy makers, whatever happens inside the innovation systems of business enterprises is a black box. From the standpoint of the majority of businesses, the policy frameworks seem foreign and lacking in relevance.

In this setting, moving the discussion towards the concepts and tools of an Open Innovation model could bring high rewards for the Latin American push towards innovation.  There are several advantages to this approach.

•          As Henry Chesbrough defined it, Open Innovation is “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively”. The focus is on the flows of knowledge to and from the organization. This concept can be applied to innovation as defined in different ways, so it is relevant for efforts focused on R&D and also for initiatives that deal with service innovation (and anything in between).  There is no need to stall on the endless question of what is an innovation. The essence of the definition refers to the flow knowledge and the value that knowledge can create. This is an assertion that all relevant actors could easily agree with.

•          In the prevalent state of the policy discussion about innovation in Latin America, phenomena occurring at the firm or team levels have been dealt with as exogenous, or beyond the scope of the analysis. However, the Open Innovation model lends itself to different levels of analysis, as Wim Vanhaverbeke has pointed out. From an Open Innovation perspective, issues such as the barriers to change stemming from group inflexibility and inertia can be examined carefully and dealt with as an integral part of the task. Inertia in teams or individuals can be analyzed with the same degree of detail as the use of policy instruments by firms. This is an extraordinary advantage. Such a framework allows all the actors to understand and follow the advancement of proposed initiatives and not just assume that important blockages will be overcome in some unspecified way.

•          The Open Innovation model facilitates the dialogue among firms (and among firms and other institutions) who may be located in very different positions in a continuum of innovation capabilities. Open Innovation allows collaboration of firms with no regard to the frequency of new product announcements or the degree of R&D intensity that they manage.  The framework facilitates absorption of knowledge by small firms, or by firms who are only initiating their development as innovators, without imposing great requirements on the capabilities that they should have at the outset.

•          Research on Open Innovation is generating a growing inventory of  tools  and instruments that can be used by firms when they search for, use and share knowledge, on all aspects of operations that may be related to innovation, from user-led design to guidelines for leveraging on shared IP resources. This makes faster organizational learning possible, where newly identified problems can quickly lead to new interpretations and conceptualizations, with experimentation and testing of novel solutions following in quick sequence.

In short, Open Innovation provides a wide ranging framework that is already in use around the world for developing knowledge about innovation. Multiple perspectives are being explored using this framework. Citing the typology proposed in a recent academic article on the topic, these perspectives include the relationships between innovation management and the impact of geography; the role of industry structure; the role of users of products in the innovation process; the development of innovative processes; the possibilities that appear for a firm along the value chain when suppliers or distributors are involved in the innovation strategy; the development of tools and instruments; and the importance of culture and mindset. The adoption of an Open Innovation point of view to advance the discussion, having all key actors go through a process of engagement with its frameworks, language and tools, could be an important step in achieving effective progress in an innovation strategy for a country or a region, allowing knowledge to move rapidly from one player to the next, and applying and testing solutions with speed. This certainly would help Latin American countries to unleash their innovation potential.

 

Prof. Dr. Rafael Vesga

Professor, Entrepreneurship & Innovation

Universidad de los Andes

 

www.exnovate.org

Uncategorized | Leave a comment Permalink

New ESADE Working Paper published on Technology ecosystem governance

IIK director Jonathan Wareham, along with colleagues Paul Fox (La Salle Business Engineering School, Ramon Llull University) and Josep Lluís Cano ESADE Business School) have recently published this new working paper.

Technology platform strategies offer a novel way to orchestrate a rich portfolio of contributions made by the many independent actors who form an ecosystem of heterogeneous complementors around a stable platform core. This form of organising has been successfully used in the smartphone, gaming, commercial software, and other industrial sectors. While technology ecosystems require stability and homogeneity to leverage common investments in standard components, they also need variability and heterogeneity to meet evolving market demand. Although the required balance between stability and evolvability in the ecosystem has been addressed conceptually in the literature, we have less understanding of its underlying mechanics or appropriate governance. Through an extensive case study of a business software ecosystem consisting of a major multinational manufacturer of enterprise resource planning (ERP) software at the core, and a heterogeneous system of independent implementation partners and solution developers on the periphery, our research identifies three salient tensions that characterize the ecosystem: standard-variety; control-autonomy; and collective-individual. We then highlight the specific ecosystem governance mechanisms designed to simultaneously manage desirable and undesirable variance across each tension. Paradoxical tensions may manifest as dualisms, where actors are faced with contradictory and disabling „either/or‟ decisions. Alternatively, they may manifest as dualities, where tensions are framed as complementary and mutually-enabling. We identify conditions where latent, mutually enabling tensions become manifest as salient, disabling tensions. By identifying conditions in which complementary logics are overshadowed by contradictory logics, our study further contributes to the understanding of the dynamics of technology ecosystems, as well as the effective design of technology ecosystem governance that can explicitly embrace paradoxical tensions towards generative outcomes.

http://www.esadeknowledge.com/view/technology-ecosystem-governance-64280

Uncategorized | Leave a comment Permalink

How Open Innovation is improving the performance of research projects in large companies?

(The following article is reproduced courtesy of Exnovate www.exnovate.org)

Innovation is increasingly the result of collaborative efforts between different organizations. We examined all research projects of a European based multinational: the company has been increasingly collaborating with a wide range of innovation partners to successfully conduct its research projects. Tapping into external sources of knowledge is not new, but this large company started to record the collaboration activities for its research projects since 2003. This resulted in a large database with more than thousand research projects.

We studied the impact of collaboration with external partners on the performance of these projects. Research project success was measured in three different ways: transfer volume, innovation speed, and financial impact of R&D projects. We were interested in the effect of collaboration with external partners on projects’ success. We distinguished between so-called science-based  (e.g. universities and research institutions) and market-based (e.g. customers and suppliers) partners.

Effect on transfer volume

Open innovation research projects create more technology transfers than closed innovation research projects. The difference is also huge: Controlling for other influences, open innovation projects will generate almost twice as many transfers compared to closed innovation projects. There is also a difference between collaboration with technology-based and market-based partners: collaboration with market-based partners is boosting the number of transfers significantly, while technology partners have a much smaller effect.

Effect on Innovation Speed

Second, we zeroed in on transfer speed and the time to reach business success. We do not find convincing evidence showing that collaboration with external partners is accelerating the transfer speed. In contrast, collaboration with market-based partners (eventually in combination with science-based partners) speeds up the transfer process, while collaboration exclusively with science-based partners does not influence projects’ innovation speed.

Effect on Financial Impact

There is also strong evidence that open innovation projects are creating more business value than closed innovation projects. This is the case for collaborations with technology-based partners as well as with market-based partners.

Differences Between Open and Closed Projects

Open and closed innovation research projects are different in many ways. Open innovation projects are larger, more costly and have different objectives than closed innovation projects. Open innovation projects are “open” because of technological complexities and market related knowledge that is not available in the company. Closed innovation projects are on average more incremental in nature. Likewise, collaborations with science-based and market-based partners are quite different from each other. Market-based partners contribute more to larger transfer volume and the acceleration of the innovation speed than science-based partners, while the latter are more valuable in the creation of large business successes. We also find that a more formalized project management and spending more internal research time lead to greater business success when the company collaborates with market-based partners. The opposite is true for collaboration with science-based partners: universities and research labs can be loosely managed. This reflects the routinized cooperation and standardized interface between large companies and their research partners.

Choosing the right open innovation mode

Open innovation projects affect research project performance differently than closed innovation projects. Likewise, collaboration with technology-based partners leads to different benefits than collaboration with market-based partnerships. Therefore, managers should carefully choose between open and closed innovation and between different open innovation modes when they start a research project. The company usually chooses for instance to collaborate both with technology and market based partners for larger or more complex projects while closed innovation is usually used for smaller projects. The analysis of the effects of openness on innovation performance indicates that it is imperative for managers to optimize the choice between open and closed innovation projects and the type of collaboration which is required. A wrong choice between these innovation modes will lead to suboptimal-levels of innovation performance.

Choosing the right timing

In a follow-on study we examined the timing of collaboration in open innovation projects. Timing of R&D collaboration is crucial for the performance of research projects. We find that managers should pay attention to when and how long they collaborate with partners: a firm should not collaborate with all partners all the time. Optimal research project results are obtained when the company collaborates two-thirds of the project lifetime with market-based partners, while the duration of collaboration with science-based partners is not affecting the outcome. Research projects also benefit from continuous collaboration (without interruptions) with market-based partners, but the opposite holds for collaborating with science-based partners. In other words, collaborating with market-based partners should be done in a continuous way without interruptions, while it may be more beneficial to collaborate with science-based partners over different periods during the research project.

There is also evidence that a firm should not start to work with different types of partners at the same time; sequencing collaboration has its advantages. Research projects are performing better when collaboration with market-based partners takes place at the end of the project, while R&D collaboration with science-based partners is beneficial when it is organized at the beginning of a project. Initiating collaboration with science-based partners in a late stage can harm the innovation performance of research projects.

How open innovation data help you in making better managerial decisions?

Open innovation has been around for a decade. Yet there is almost no hard evidence based on large-scale databases about research projects or other innovation activities where open innovation may play a crucial role. The database we examined shows that longitudinal data about research projects can be very useful in measuring the performance impact of open innovation activities during these projects. The more detailed the data the deeper we can dig into specific mechanisms why open innovation is productive in particular circumstances and not in other ones. Collecting data over extended periods of time results in a wealth of data, which provides evidence about the transfer speed, and time to generate a business success. The data also provide ammunition to make decisions about the timing when to establish or end partnerships. Companies should not only know with whom they have to partner in different types of research projects, but also when, how long and in which sequence.

The company we examined has benefited considerably from analyzing the research project dataset. This hard evidence about the benefits and challenges of open innovation was a powerful instrument in reporting to top management and it helps to legitimize open innovation initiatives in a corporate context. We encourage other companies to set up similar databases and to share it with open innovation experts. The analysis by our research team proves that this type of data also advances our academic understanding of open innovation considerably.

 

Wim Vanhaverbeke

Jingshu Du

Bart Leten

www.exnovate.org

Originally published in the Exnovate Newsletter 1/12 on 18th December 2012.

Uncategorized | Leave a comment Permalink