open innovation case study

Being open about the problems that you have might be a scary idea, after all, not only possible collaborators but your competitors will be able to see what you are working on.

Despite this, there are great benefits about opening up about your needs to companies that could solve your problems. The collaborative relationships can also last for decades, ending up being helpful on more than one occasion.

P&G often refers to collaboration with long-lasting partnerships as being efficient because you already know each other's working practices inside and out.

On top of the fact that being completely open gives you the best chance to get outside-the-box solutions that you wouldn’t have thought yourself, it’s also possible to create your own trusted community that you can share your problems with.

11. Nivea - Involving users in product development

Nivea’s B&W deodorant is a strong example of activating users throughout new product development from ideation to implementation.

The intimately open collaboration with possible partnering companies happens via Beiensdorf’s pearlfinder , which might be interesting to take a look at. However, the real case that we can learn from here is Nivea’s B&W deodorant’s development.

The idea for Nivea’s B&W deodorant was coined together with Nivea’s users through social media. The way Nivea collaborated with its users throughout the R&D process is very interesting.

They pretty much said that “okay, we know that our current product can be connected to stains in clothes. Could you share your stories and home remedies so that we can develop a better product?”

They basically shared what kind of product was needed, what seemed to be the reason behind these stains in the first place, and how they could be prevented. The resulting B&W deodorant then became the first deodorant on the market that prevents white and yellow stains from appearing.

Beiersdorf then partnered up with a company they found via pearlfinder and developed, together with the users, the B&W deodorant. This admittance of issues in their product could have been seen as a sign of weakness, however, users were very active in collaborating with Nivea and the end-product ended up being a great success.

Your users might have surprising problems that you could solve . User involvement and activation might be a good idea in engaging them to collaborate with you.

Even if open innovation collaboration may seem frightening to you, listening to your customers needs is common sense. So keep your eyes and ears open (also online!) for all ideas that come from your potential and existing users.

Of course, Nivea could have invented the invisible B&W deodorant by traditional means, but it would probably have cost more time and money. Nivea also gained visibility and committed customers by including them in the innovation process, which thus doubled as marketing.

Open innovation is not just a cool way of doing things. It can have major cost benefits too.

Do you know what your users are thinking of your product?

Maybe there is a way of involving your users in the process, thus giving you helpful insight into their needs, wants and ways of bettering your product.

12. Philips - The High Technology Campus

Philips has a wide range of open innovation activities. It has the platform , the challenges and it activates its own employees to think openly .

Philips also established its own open innovation campus at Eindhoven in 2003. The High Tech Campus is open for a variety of companies to work in. It offers them tools to help accelerate their business and research projects.

Currently, the campus works on its own, but Philips’ presence is still there and it continues to gain from the physical open innovation space.

The possibility to actually work physically together has created an innovation ecosystem in Eindhoven with over 140 companies with varying sizes working in the same small area.

Creating and being present in spaces where there is a possibility to collaborate together is great for open innovation.

Nothing builds collaborative and trusting relationships between companies and research groups like the possibility to visit one another casually on your coffee breaks to talk about what you’ve just been working on.

Even though technology gives you the possibility to open up your innovation to the masses, physical proximity is still a key factor in one-on-one close collaboration.

However, physical proximity is not always necessary. Companies can have active platform collaboration online and hold open innovation challenges worldwide.

Still, if you work closely with others and want to establish close personal connections, you could benefit from these physical spaces that enable concrete encounters.

Could proximity with collaborators be beneficial to your company?

13. Apple - Value creation through open platforms

Apple is a typical example of a company that is very closed and secretive of its R&D, so what is it doing on this list?

Despite the closedness of Apple, they still use open innovation on their own terms when they think open innovation is suitable.

Take the apps on iOS-products as an example. Even if Apple’s product quality is top notch, it is first and foremost the quality and wide range of applications available for Apple products that makes them so valuable.

The logic behind the idea, that others can create applications on your platform is very smart.

Think about iPhone users (and all other smartphone users for that matter). They have all kinds of needs that phone makers or operating system makers don’t have a clue about. Some are interested in their health, others are into mobile gaming, some in news, books and music etc.

With all the possible apps, users can customize their user experience exactly like they want to, and all of this benefits both the platform and the users.

The key learning point here is that you can restrict the amount of openness in open innovation . What Apple does is that it regulates and controls its open innovation so that application developers can create their products to work in the Apple environment. This way they can be distributed through Apple’s channels with little to no visibility in the other aspects of Apple’s internal R&D.

However, achieving this kind of a position and control is not easy, nor is it always feasible. You have to think if having control actually gives you enough value.

Regulating the openness means that also the outcome possibilities of collaboration are restricted one way or the other. For example, the collaboration can fail to create breakthrough innovations.

Moreover, convincing others to engage in open innovation with you despite restraining the collaboration can be hard unless you clearly have a winning platform. So be careful on how you control the cooperation.

Due to Apple having a great brand and a winning platform, a high level of control is possible for them, but how about you?

14. General Assembly - Re-thinking education

General Assembly is a school, that provides online and on-campus courses, that help people gain work life relevant know-how.

Typically the course contents are related to the modern needs and technologies in today’s work life .

There are courses for example on:

The offering is both for students that simply want to learn and make connections in the community, and for companies that want to provide their employees with courses.

Valuable Community

General Assembly incorporates open innovation through their community , developing their offering based on the needs of the changing work life. They are also actively seeking new partnerships. The community is used for connecting talented young professionals (current and former students) with companies looking for such.

General Assembly teaches people skills, that lead them to paying jobs and offers tools to help in building their own companies. So in a way, General Assembly is a personal accelerator for its students!

As an example, there is General Assembly’s Web Development Immersive (WDI) course . Through this course, students learn how to develop web software professionally.

The students also complete several projects that provide them with the skills applicable for real work life situations. For example, Nathan Maas created his own service based on what he learned on the WDI course and now has a company called pennypost .

The open community of General Assembly made it possible for Maas to start his own company with people that liked his idea. Several fellow students and instructors actually joined Maas to create pennypost after the WDI course. The fact that these kind of innovative companies and individuals have their roots in the General Assembly means that they get visibility. Thus more and more companies want to hire from their alumni, in turn attracting more students.

General Assembly listens to and reacts to what its customers want to learn and teach.

The skills that they teach are very much related to the new needs of working life , skills that companies want to hire and teach their employees. Being nimble and developing its offering to match the needs of the society is what makes General Assembly more attractive to students.

The problem with traditional schools is that the curriculum tends to change slowly in them.

While the demands of businesses are changing rapidly, what you learned in school doesn’t often prepare you for real work-life situations with adequate skills.

General Assembly is a very good example of being open to the needs of its users and meeting constantly changing requirements.It's also open to new skills and instructors who have something to add to their offering.

15. Telegram - Enabling users to create content

Telegram is a messenger application that works on computers and smartphones very much like WhatsApp. What makes it different is how much users can contribute to its content openly. What makes Telegram interesting and popular is the fact that you can customize it to your liking.

Users with any developing skills can create their own stickers and bots (i.e. their own content) on the platform. While Telegram can be used like a regular messenger, it’s also possible for users to customize their user experience by themselves.

Even though instant messaging is a very competitive market, Telegram has gained a lot of users because of its ease, openness and the fact that you can create your own content. The Telegram company sometimes even grant prizes for new content.

Custom stickers makes it possible for you and your friends to make stickers of the funniest moments you’ve shared. Telegram also promotes the best stickers updating an in-app list of the trending ones.

The main giveaway of this case is that openness can:

When users can create almost any kind of feature they want, you can benefit from the best innovations .

Telegram can even bring the most popular features forward to all users.

So it might be a good idea to let your users create what they want to on your platform as it might just be enough to differentiate your product from all the others!

Also, having the users configure a product to exactly match their needs creates a positive lock-in effect. Why would you want to change the product to a substitute, when others can’t be made to match your preferences?

16. Lilly - Gathering information

Lilly is a pharmaceutical company, that has applied open innovation via its Open Innovation Drug Discovery program (OIDD) . In OIDD scientists (at universities as well as companies) can safely share biological data that aims for new drug discovery . Lilly then offers modern tools and help in screening and researching the data. So basically OIDD is a platform that enables companies to find new drug compounds faster.

OIDD is also a platform for generating R&D partnerships with Lilly. With the OIDD Lilly has generated a wide, Open Innovation 2.0 like open innovation network. In the network, all parties can benefit from the new value created within the network, in this case, from the new drug discoveries. Hence, new discoveries lead to new medicines faster.

This open way of working enables Lilly and other scientists to find new possible drugs and cures for severe diseases faster than before. Researchers apart from Lilly get the tools to test their compounds, while Lilly gets a great database of compounds. On the other hand, Lilly obtains connections that it can use when possible drug solutions are found.

The Lilly example demonstrates an open innovation network, and how that positively affects Lilly’s own R&D. The OIDD platform speeds up new drug discoveries, and when new drug compounds are found, Lilly can collaborate with the finding company.

In the end, Lilly might be able to manufacture the end product and be the one to sell it. This kind of open innovation network can be issued in many ways, like via this case’s open innovation platform. You might not even need to create the network yourself as there are many existing open innovation networks that you might be able to join.

If you are still apprehensive about open innovation because of problems with potential intellectual property rights, you might want to reconsider. Lilly is a big company in the pharmaceutical field, a highly patented and IPR restricted industry and while they’ve certainly had to address these issues, they’ve made it work. And if they can make it work in the pharmaceutical industry, why couldn’t you?

An Open Blog Post

Hopefully this post has inspired you to come up with ways to incorporate open innovation to your business!

In the spirit of open innovation, our aim is to keep this listing up-to-date and as inclusive as possible!

If you come across examples of open innovation that haven’t been listed here, even though they definitely should be, please write to us !

In the meanwhile, we encourage you to get started with open innovative with Viima. It's free for unlimited users!

This post is a part of our Open Innovation blog-series. In this series, we dive deep into the different areas of open innovation and cover the aspects we think are the most important to understand about open innovation.

You can read the rest of the articles in our series covering open innovation by clicking on the button below. Don’t forget to subscribe to our blog to receive updates for more of our upcoming content!

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My Starbucks Idea : an Open Innovation Case-Study

How Starbucks gathered more than 150,000 ideas and created new products and services by engaging its community.

Since opening its first store back in 1996, the Seattle-based coffee behemoth now includes almost 30,000 coffee shops around the world and is valued at a staggering $30 billion . A strong commitment to open innovation has helped to drive this incredible growth.

This zest for open innovation led to the "My Starbucks Idea" platform, a customer participation experiment. Now that the company has retired this platform after nearly ten years, we thought it would be a good opportunity to look back and ask a few questions.

So, what can a case study of the "My Starbucks Idea" platform tell us about open innovation? What were the benefits to Starbucks from managing this platform, and how can other businesses take advantage of these benefits?

Pour yourself a little cold brew, and we’ll show you how it all came together.

How did they do it?

As a company, Starbucks has always placed a lot of value on refining its products and procedures following customer feedback. In the company’s earliest years, this was reflected in simple systems like suggestion boxes and customer surveys.

In 2008, however, founder Howard Schultz launched the open innovation platform "My Starbucks Idea". This platform encouraged customers and fans to share their ideas and suggestions for how to make the company’s beloved products even better.

“We need to put ourselves in the shoes of our customers,” Schultz said at the time . “That is my new battle cry. Live and breathe Starbucks the way our customers do.”

As it turns out, the as-yet undecided 2020 Presidential candidate was on to something. Starbucks customers had a real thirst to share their ideas: over the first five years of operation, the platform received over 150,000 ideas, and the company put hundreds of them to use.

The format was simple. All customers had to do was create a profile, write (and categorize) their suggestion, and submit it for others to comment on. If the idea gathered enough steam, or if the Starbucks administrators liked the looks of it, it could then be adopted by the company.

Plenty of companies crowdsource product ideas via customers. So, what made "My Starbucks Idea" unique?

What made "My Starbucks Idea" unique?

"My Starbucks Idea" was a lot more than just a fancy suggestion box.

To help encourage a community of fans, Starbucks enabled users to vote and comment on ideas they liked. There was also a public leaderboard showing the most dedicated fans, as well as those with the most popular ideas.

Users could also see profiles for the Starbucks ‘Idea Partners’ - the company representatives tasked with managing and monitoring online discussion and working with customers on their suggestions. This helped put a human face on the company.

My Starbucks Idea Original Platform Featured

This blend of open innovation, customer co-creation, and fan community site proved immensely popular. Not only did it create a lot of great product innovations for Starbucks, but it also helped to drive increased customer loyalty.

Through actively managing the "My Starbucks Idea" platform, Starbucks engaged customers, making them feel they were being listened to. By rolling out fan-driven ideas like cake pops and pumpkin spice lattes, Starbucks created greater product diversity.

In June 2018, after almost a decade, the company retired the "My Starbucks Idea" platform. Starbucks still encourages its customers and fans to submit their suggestions for new products on Twitter , of course, as well as via its website .

Now, let’s dig into the detail a little more. What were the results of the "My Starbucks Idea" platform, and what can these results tell us about customer-driven innovation?

Cake pops and pumpkin spice: the results of "My Starbucks Idea"

By paying attention to customer preferences, Starbucks was able to hold its spot as the market leader, even in a rapidly changing industry like the food and beverage sector.

The "My Starbucks Idea" concept was based on a core belief: customers know what they want.

The company’s commitment to this concept led to fans submitting over 150,000 ideas, of which hundreds were adopted. And these weren’t just run-of-the-mill suggestions, either - they include fan favorites like hazelnut macchiatos.

More than just receiving product suggestions, however, the platform also led to suggestions around process improvements, including finessing Starbucks’ mobile payment systems and offering free Wi-Fi.

For a better overview of the range of innovations submitted through the "My Starbucks Idea" platform, check out the following infographic, published in 2013:

These examples demonstrate the value of open innovation. By handing power over to customers, and by giving them an incentive to participate by recognizing their ideas, Starbucks was able to channel a lot of crowd creativity.

So, it’s clear that the "My Starbucks Idea" platform led to a huge amount of valuable product innovation. But beyond just suggesting new drink flavors, what were the wider benefits of this experiment?

What were the benefits for Starbucks?

First, there’s the obvious benefit: empowering customers to make suggestions for product improvements meant that Starbucks had access to new, and potentially very valuable, ideas. Many of these were things that would only have occurred to dedicated customers.

The "My Starbucks Idea" platform created significant benefits for Starbucks.

Besides the simple value of these ideas, however, the platform also helped generate a lot of media attention and free advertising. By encouraging die-hard fans to engage online, Starbucks created a new way to market their products to their most valuable customer segment.

The platform also served as a market research tool, too. Many thousands of fans signed up to submit their ideas, and in doing so provided the company with demographic information. This allowed Starbucks to build detailed customer profiles.

The benefit didn’t go just one way, though: Starbucks customers also got plenty of value out of the platform. By engaging with the company, customers experienced a greater sense of inclusiveness, with the platform helping to build a real community.

It wasn’t all sunshine and rainbows, however. As significant as the benefits were for both Starbucks and its customers, the "My Starbucks Idea" platform still created a number of risks to be managed.

How did Starbucks manage the risks of open innovation?

As we’ve seen in the examples of General Motors crowdsourcing advertisement suggestions on Twitter, handing the keyboard over to the customer can open a company up for online criticism, and even harassment.

Sometimes, open innovation can be something of a double-edged sword.

For "My Starbucks Idea", this risk was definitely present. Starbucks employees were required to sift through the online ideas and comments on a regular basis to weed out any trolling or abuse , and to prevent the platform from becoming a tool for corporate mockery.

Managing the risks of online abuse and platform misuse took a lot of dedicated resources, requiring an active approach from the company. Despite a strict set of community guidelines, the platform still required a lot of moderating.

But enough about the negative stuff. What can "My Starbucks Idea" tell us as a case study in open innovation, and how can you put these ideas to use in your own business?

How to make open innovation work for you

As we’ve seen in our article on ten companies showing us how to get it right when it comes to customer co-creation, successful open innovation takes careful planning and foresight.

If you want to make open innovation work for you and your customers, you need to:

Embrace open communication

Let’s take a closer look at these things one by one.

Appeal to intrinsic motivations

Whether it’s a beer fan giving Anheuser-Busch suggestions on their new line of craft lager , or the LEGO community suggesting new toy sets , nobody ever wants to give away a great idea for free. If a product suggestion is valuable, it’s only fair that a company should pay for it.

So, before you ask your customers and fans to put on their thinking hats, give them a reason to participate in the exercise. Whether it’s monetary compensation, public recognition, or a mixture of the two, there should be something on the line if their idea succeeds.

This is something Starbucks got right with its open innovation platform. Not only did fans get the public recognition associated with having their names attached to the ideas, they were also eligible for monetary compensation, too.

Set clear guidelines and limits

Sometimes, a little structure can be a great way to encourage creativity.

Rather than simply asking fans for ideas, "My Starbucks Idea" included guiding categories for suggestions. These included ‘products’, ‘atmosphere & location’, ‘service systems’, and others. These categories helped guide the submissions process and helped stimulate fan creativity.

Starbucks also set clear community guidelines to help manage online exchanges, too. This not only gave customers a more secure environment in which to share their ideas, it also cut down the amount of work for Starbucks employees managing the site. Win-win!

Encourage different perspectives

The true value of open innovation rests in the ability to encourage customers to share different perspectives on a product or service. After all, you can have the world’s most amazing thinkers on your payroll, but having access to a broad pool of opinions will still get the best ideas.

Starbucks really got this right with "My Starbucks Idea". By actively encouraging and rewarding a range of perspectives and values, the company was able to unlock the innovative potential of a huge number of people, with great results.

If you’re thinking about how to embrace open innovation, make it a priority to encourage people with a range of different perspectives, opinions, and life experience to participate.

When it comes to encouraging innovation , open communication is a must - especially given the rise of company representation on social media platforms like Twitter and Instagram.

Businesses need to engage their customers with transparency, consistency, and, above all else, respect. If open innovation participants feel they’re being listened to, and are getting a trusted source of information, they’re more likely to contribute.

Once again, Starbucks really nailed this with "My Starbucks Idea". By communicating regularly with fans, and providing a clear source of dependable information about the platform, Starbucks was able to build trust and reward fans for their ideas.

For Starbucks, open innovation is much more than just cake pops.

Don't just take it from us - here is Matthew Guiste, one-time Director of Social Media at Starbucks, talking about the value of open innovation for the company.

https://www.youtube.com/watch?v=pjNM8drAqG0

By unleashing the creative potential of its diverse customer base via the "My Starbucks Idea" platform, Starbucks was able to source valuable new product ideas, engage with their most dedicated fans and gather market information to stay on top of trends.

All it took was a little investment in a web platform, some community guidelines, and a commitment to open communication. That’s a small price to pay for such a valuable tool.

Jonathan Livescault

Former Strategy Consultant turned Entrepreneur. Excited to help every day corporate innovation teams get results and build their company's future.

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Bror Salmelin,

European Commission

"we can`t solve problems by using the same kind of thinking we used when we created them" Albert Einstein

Recent publications

The objective of Open Innovation 2.0 Yearbook 2013 is to create a manifesto and platform for sustainable growth enabled through Innovation 2.0. The main goal is to build towards a collective vision/ambition and leverage the collaborative intelligence and muscle to create a virtuous circle of sustainable growth which enables new services, sustainability, improved quality of life and new jobs.

Why Now Is the Time for “Open Innovation”

Covid-19 has shown how companies can work together to solve problems.

As companies struggled to adapt to the fallout of the Covid-19 crisis, many turned to open innovation — a collaborative approach that plays to the strengths of all companies involved and can produce creative, unexpected solutions. It’s a kind of collaboration, the authors argue, that’s worth pursuing whether or not you’re in a crisis. Making it work, however, requires that companies: momentarily put aside traditional concerns over IP to focus on other approaches to creating value; leverage their partners’ motivations effectively to maintain a productive working relationship; embrace new partners; and commit to the projects they pursue through open innovation to reap their benefits. This approach can be extremely fruitful, and not just in the middle of a crisis.

In these difficult times, we’ve made a number of our coronavirus articles free for all readers. To get all of HBR’s content delivered to your inbox, sign up for the Daily Alert newsletter.

Amidst the gloom and doom of the early months of the Covid-19 crisis, something surprisingly uplifting started to happen: Companies began to come together to work openly at an unprecedented level, putting the ability to create value before the opportunity to make a buck. The German multinational Siemens, for instance, opened up its Additive Manufacturing Network to anyone who needs help in medical device design. Heavy truck maker Scania and the Karolinska University Hospital have partnered, too: Scania is not only converting trailers into mobile testing stations, but also directed some 20 highly skilled purchasing and logistics experts to locate, acquire, and deliver personal protective equipment to health care workers. Similarly, Ford is working together with the United Auto Workers, GE Healthcare, and 3M to build ventilators in Michigan using F-150 seat fans, portable battery packs, and 3D printed parts.

Collaboration can obviously save human lives, but it can also produce huge benefits for companies — even though it’s often overlooked in normal circumstances. For more than a decade, we’ve studied open innovation and have taught thousands of executives and students how to innovate in a more distributed, decentralized and participatory way. The classroom response is usually, “My company needs more of this!” But despite the enthusiasm, companies rarely follow through. We have also witnessed how companies have used hackathons and other forms of open innovation to generate heaps of creative ideas that never reach the point of implementation, leading to frustration among employees and partners. At many companies this kind of distributed, decentralized, and participatory way of innovating remains an ambition that hasn’t yet come true.

Coronavirus: Leadership and Recovery

The recent burst of open innovation, however, reminds us of the massive potential that open innovation comes with — whether you’re in a crisis or not. Open innovation has the potential to widen the space for value creation: It allows for many more ways to create value, be it through new partners with complementary skills or by unlocking hidden potential in long-lasting relationships. In a crisis, open innovation can help organizations find new ways to solve pressing problems and at the same time build a positive reputation. Most importantly it can serve as a foundation for future collaboration — in line with sociological research demonstrating that trust develops when partners voluntarily go the extra mile, providing unexpected favors to each other.

While concerns over intellectual property, return on investments, and various unforeseen consequences of open innovation are all valid, what we are experiencing now is an opportunity to innovate through and beyond the crisis. We have discovered a number of lessons that can help companies to not only take advantage of open innovation during the Covid-19 crisis, but to embrace open innovation once the pandemic is over. Here’s how companies can overcome some well-known challenges in open innovation:

Forget about the IP for the moment.

Earlier research has found that many companies are extremely worried about value “leaking” from collaborations with outsiders. As a result, they often stick to their knitting and collaborate on a few peripheral tasks, but not on the most important business issues. For example, we are aware of several chemical companies in Europe and the U.S. that made it practically impossible for their open innovation partners to provide help and advice. How? They wouldn’t reveal what their most critical problems entailed, as that could endanger future patenting. Instead the innovation partnerships slipped into irrelevance.

These intellectual property concerns are of course real and important, but they risk blocking any open innovation initiative from gaining momentum. However, during the Covid-19 crisis it could be wise to focus more on creating value than capturing value.

Smart companies take a leap of faith, collaborating on important stuff, without risking negative exposure. For example, if heavy truck maker Scania — a company known for its world-class manufacturing system — sends some of its best manufacturing experts half an hour north to work at Stockholm-based Getinge to ramp up their ventilator production, it risks none of its core technological assets but by contributing to the effort to build medical capacity and combat the virus, hopefully it’s speeding up how quickly it own plant will be back up and running.

Leverage two-sided motivation.

As the initial open innovation enthusiasm has settled, companies often realize that they rely on voluntary and active participation of employees and partners to succeed — traditional means of command and control have little reach. Instead companies need to rely on a combination of hard and soft incentives to motivate internal and external collaborators. Companies need to identify — and respond to — their partners’ true motivation.

For example, our own research on open-source software development has demonstrated a diverse set of motivations among developers. Some developers are motivated to freely share their code because of labor market signaling. Other developers are driven by strong ethical concerns, vigorously opposing any move to develop software that cannot be inspected, modified, and openly shared. And some companies are motivated to donate time and resources because it is an effective means to access complementary skills and assets . Aligning all of these motivations with what companies wish to achieve takes effort, curiosity and a portion of humbleness. While this might be easy in the early stages of a collaboration that’s responding to the pandemic, companies should not expect collaboration beyond the pandemic to go as smooth. Instead, it’s worth putting the work in ahead of time to discover — and potentially nudge — partners’ motivation.

Embrace new partners.

A common challenge in open innovation is to take on new partners. New partners always entail costs in terms of search, validation, and compliance, as well as the forming of new social relationships between people. And we know that when it comes to big thorny problems like Covid-19, new partners are necessary to provide complementary skills and perspectives.

The massive scale of the Covid-19 crisis may have alleviated these challenges in at least two ways. First, top management has assumed a lot of the risk associated with new partners, by sending strong messages that open innovation is the way to go. For example, Jim Hackett, Ford’s president and CEO says he has empowered his engineers and designers to be “scrappy and creative” when collaborating with GE Healthcare to find solutions to the crisis.

Second, not only the spread of the virus has grown exponentially but the pool of potential partners as well. When companies across the globe are affected by the same crisis, and many are searching for new ways to conduct business, a combinatorial exercise suggests that there are many better partners available now than a month ago. A crisis can prompt companies to explore a greater number and even new kinds of partners. Preserving some of that open-minded attitude towards new partners after the crisis can help companies stay on top of innovation.

Urgency leads transformation.

The initial steps towards open innovation in “normal times” are relatively simple. For example, hire some consultants, set up an innovation tournament, wait for ideas to come in. The results though are usually quite meager. To fully reap the rewards from open innovation, companies need to recognize the transformational challenge ahead. These initiatives are often the tip of the iceberg, and successful open innovation often requires operational and structural changes to how business is done. Such changes are difficult for any one employee, team, or even business unit to undertake.

In a time of crisis, the necessary executive focus is suddenly there. Smart companies seize this opportunity to rethink their innovation infrastructure. Perhaps our own sector, higher education, could stand as a beacon of hope that open innovation can work on a truly grand scale — and that a conservative sector can change. Many of us were told that classes starting the day after had to be replaced by digital alternatives. Much was left for individual teachers to figure out, but university presidents sent reassuring messages endorsing experimentation and clearing bureaucratic hurdles. In the past few weeks, academics across the globe have been collaborating, sharing tips, tricks, teaching plans, and experiences to turn an often slow-moving colossus into an agile digital sprinter. It shows that often the biggest barrier to successful open innovation is simply the reticence to commit to it.

Looking ahead.

These are promising developments. But to what extent will these observations hold true in the future? As business will one day go back to normal, how many of the altered ways of innovating will stick inside companies? And how will we as a society face other grand challenges, such as global warming, that are no longer looming on the horizon but are already here? We hope that the world’s response to the novel coronavirus has taught us that a truly shared experience of a common enemy can unlock the speed, strength and creativity needed to address even the greatest challenges.

For managers, an important reflection is to think about what needs to be delivered after the crisis. A big crisis often alters the behavior of customers, employees, and partners. Perhaps you have reason to believe the customer preferences will stay the same, but often they do not. Having established new ways of doing open innovation during a crisis can then bring much-needed flexibility and, in the end, secure the company’s viability. Don’t waste those experiences by planning for how to get back to the old normal. Plan for a new normal.

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The past, present and future of open innovation

European Journal of Innovation Management

ISSN : 1460-1060

Article publication date: 24 June 2020

Issue publication date: 13 July 2021

Through a comprehensive review of the literature on open innovation (OI), this study aimed to achieve two objectives: (1) to identify the main thematic areas discussed in the past and track their evolution over time; and (2) to provide recommendations for future research avenues.

Design/methodology/approach

To achieve the first objective, a method based on text mining was implemented, with the analysis focusing on 1,772 journal articles published between 2003 and 2018. For the second objective, a review based on recent and relevant papers was conducted for each thematic area.

The paper identified nine thematic areas explored in existing research: (1) context-dependency of OI, (2) collaborative frameworks, (3) organizational dimensions of OI, (4) performance and OI, (5) external search for OI, (6) OI in small and medium-sized enterprises, (7) OI in the pharmaceutical industry, (8) OI and intellectual property rights, and (9) technology. The analysis of the most recent papers belonging to the more investigated areas offers suitable suggestions for future research avenues.

Originality/value

To the best of the authors’ knowledge, no review has yet been undertaken to reorganize the OI literature.

Bigliardi, B. , Ferraro, G. , Filippelli, S. and Galati, F. (2021), "The past, present and future of open innovation", European Journal of Innovation Management , Vol. 24 No. 4, pp. 1130-1161. https://doi.org/10.1108/EJIM-10-2019-0296

Emerald Publishing Limited

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Since the 2003 publication of Chesbrough’s seminal work, Open Innovation: The New Imperative for Creating and Profiting from Technology, the concept of open innovation (OI) has garnered increasing attention from academics and practitioners alike. As a demonstration of its importance, Chesbrough’s study has gained more than 19,000 citations in 16 years, according to Google Scholar (Google Scholar, July 2019), with this number growing on a daily basis. OI was developed based on the observation of large innovative companies and their deviations from traditional ways of innovating ( Chesbrough, 2003 , 2006 ). The original definition of OI stressed that “ valuable ideas can come from inside or outside the company and can go to market from inside or outside the company as well. This approach places external ideas and external paths to market on the same level of importance as that reserved for internal ideas and paths ” ( Chesbrough, 2003 , p. 43). The antecedents of OI, however, have to be found prior to the publication of Chesbrough’s book. Since the 1970s, scholars such as Freeman ( 1974 , 1979 ), Rothwell et al. (1974) and Allen (1977) have understood that sources of innovative ideas often come from outside the firm. Later, innovation scholars stressed the “two faces of research and development” (R&D); in other words, that the internal R&D investments of a company not only allow for the generation of new knowledge in general but an increased ability to assimilate external knowledge ( Cohen and Levinthal, 1989 , 1990 ). Based on this notion, studies then investigated the ways in which firms during the late 1990s/early 2000s leveraged the internet to develop new value chains and revenue models (e.g. Timmers, 1998 ; Magretta, 2002 ). Thus, OI can be seen as a call to a return to the late twentieth and early twenty-first century model of innovation ( Mowery, 2009 ).

Since the term OI was initially coined, both innovation scholars and Chesbrough have modified its original definition, with the latter stressing the intentionality of knowledge inflows and outflows: “ 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 ” ( Chesbrough, 2006 , p. 1). More recently, in 2014, Chesbrough proposed the following definition of OI based on the concept of business models: “ We define open innovation as a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and non-pecuniary mechanisms in line with the organization ’ s business model ” ( Chesbrough and Bogers, 2014 , p. 17).

Regardless of the definition, OI is based on the idea that, within the modern competitive context in which firms have to operate, the linear model of innovation ( von Hippel, 1988 ) is no longer able to adequately explain innovation activities. Today’s organizations have to collaborate with external stakeholders through the iterative exchange of knowledge, technology, and resources across their boundaries ( Galati, 2015 ; Galati and Bigliardi, 2017 ). In other words, to stay abreast of competition, a single organization cannot innovate in isolation; rather, it has to engage with different types of partners, ranging from suppliers to customers, as well as universities, research centers and competitors ( Bigliardi and Galati, 2013 ), in order to acquire ideas and resources from the external environment ( Laursen and Salter, 2006 ). Thus, according to this paradigm, the boundaries of an organization have to become permeable rather than closed, since innovation developed through intentional inflows and outflows of knowledge exists within a system of relationships, including external partners ( Bogers and West, 2012 ; Enkel et al. , 2009 ; Gassmann and Enkel, 2004 ).

OI has become one of the most researched topics in innovation management, attracting attention from both academics and managers. Interest from academics is not only highlighted by the steady increase in conferences, special issues and books on the topic, but by the hundreds of published articles: a search on Google Scholar for the keyword “open innovation” results in more than 145,000 documents, and the same search on Scopus in the article title, abstract and keywords results in around 5,000 documents (data retrieved on June 2019). Moreover, and perhaps surprisingly, different disciplines have also shown interest in OI: these range from “business, management and accounting,” to “engineering” and “decision science,” as well as “medicine,” “psychology,” “chemistry,” and even “physics and astronomy.” In terms of managers’ interest, one need only consider the growing number of papers describing the adoption of OI by companies, within both high-tech (e.g. Bianchi et al. , 2011 ; Bigliardi et al. , 2012 ; Galati and Bigliardi, 2016 ; Henkel, 2006 ; Remneland-Wikhamn et al. , 2011 ) and low-tech (e.g. Bigliardi and Galati, 2013 ) industries, and within both large companies ( Dodgson et al. , 2006 ; Kirschbaum, 2005 ) and small and medium-sized enterprises (SMEs) (e.g. Galati and Bigliardi, 2016 ; Spithoven et al. , 2013 ; van de Vrande et al. , 2009 ). In addition, governments have recently started aligning their policy frameworks with OI ( West et al. , 2014 ).

As a natural and negative consequence of the rapid growth of the literature, OI is evolving into a diverse and fragmented body of knowledge within the field of innovation literature ( Huizingh, 2011 ; van de Vrande et al ., 2010 ). As such, scholars are beginning to recognize the need for a full understanding of the topic to bring about further clarity ( Dahlander and Gann, 2010 ; Lichtenthaler, 2011 ), reviewing and synthesizing the literature into different aspects of OI research. In Table 1 , some of the more recent reviews available in the literature since 2010 are provided as an example.

Previous reviews have adopted different methods to analyze the literature. For example, while Huizingh (2011) adopted a qualitative approach, Remneland-Wikhamn and Wikhamn (2013) adopted an empirical one. Similarly, Fraceto et al. (2016) combined a bibliometric technique with content analysis, while Natalicchio et al. (2017a) systematically reviewed the extant literature.

However, in focusing on specific issues, existing reviews consider only a part of the OI literature. As such, the OI paradigm has evolved over time, encompassing different perspectives. Notwithstanding the huge amount of literature on OI, there is a lack of systematic reorganization of previous research, with no past review tracking the evolution of the OI literature in a comprehensive manner. This study posits that tracking the evolution of OI over the years could be crucial to identifying: the most debated areas; the maturity reached by each thematic area; whether some thematic areas were abandoned, and the potential opportunities to make further steps in the research. The objective of this paper is twofold: (1) to identify the main thematic areas discussed in the past and track their evolution over time, and (2) to identify avenues for future research on this topic. For achieving the first objective, a method based on text mining was implemented, with the analysis focusing on 1,772 journal articles published between 2003 and 2018. For achieving the second objective, a review based on recent and relevant papers was made for each thematic area. In so doing, it is hoped that this study could help to bring further clarity to the broad and scattered field of innovation literature.

The paper is structured as follows: Section 2 presents the aim of the study and describes in detail the text mining methodology adopted. Section 3 outlines the results obtained in terms of patterns interpretation, evaluation, validation, and identification of future research avenues. Conclusions, policy implications and limitations of the study conclude the paper.

2. Objective and methodology

In order to achieve the research aim, this study adopts a three-step methodology, as depicted in Figure 1 .

2.1 Preprocessing

Following the validation of clusters, the process began by following the methodology proposed by Delen and Crossland (2008) in order to achieve the first objective of the study (i.e. to identify the main thematic areas discussed in the past and track their evolution over time). Since the text mining method adopted by these authors does not rely on predetermined notions regarding topics from texts, subjective bias is removed from the analysis. Such a method was adopted since it is able to extract the contextual-usage meaning of words and discover hidden, latent topics that facilitate modeling synonyms, as well as to detect multiple words that have a similar meaning

For doing this, the Scopus database was used as the source of peer-reviewed articles. All of the papers dealing with the topic were included, providing a robust empirical analysis of the existing research and key OI concepts. Numerous scholars consider the Scopus database a reliable source of information (e.g. Fahimnia et al. , 2015 ; Malviya and Kant, 2015 ) due to the fact it integrates all major publishers such as Emerald, Taylor and Francis, Springer, and Willey ( Galati and Bigliardi, 2019a ; Tseng et al. , 2019 ).

For the substep “text collection,” we included all Scopus scientific journal publications containing the caption “open innovation” in the abstract, title or keywords. The decision to include only journal manuscripts and not others (such as conference proceedings, books, and so on) was due to the willingness to consider high-quality publications. More specifically, we chose to use only the abstract of an article as the source of the text mining analysis.

A total of 1,772 journal articles published between 2003 and March 2018 were considered. For each publication, we extracted the title, year of publication, journal, authors, and abstract. We then loaded these into a Microsoft Excel file.

Text cleaning was implemented based on the notion that not all terms are important in the characterization of documents: some of them (terms used in almost all of the documents in the corpus, articles, auxiliary verbs, etc.) have no differentiating power and, consequently, should be omitted in the indexing process.

For the substep “text transformation,” the list of documents (abstracts) was converted into a term-by-document matrix. The columns were populated with the list of all the unique terms identified in the abstracts, and the rows contained all of the abstracts; the occurrence count (frequency) of each term for each abstract (cells) was included. However, the large number of documents (and thus abstracts) considered in our study resulted in a term-by-document matrix with a very large number of terms (columns). This implies that processing such a large matrix is time-consuming, and more importantly, can lead to inaccurate patterns.

2.2 Text mining operations

Once the input abstracts had been indexed and the term-by-document matrix computed, it was possible to calculate the inverse document frequencies to create the “term frequency-inverse document frequency (TF-IDF) matrix.” This step was necessary to improve the accuracy of the analysis.

Then, in order to reduce the dimensionality of the TF-IDF matrix to a manageable size, we implemented singular value decomposition (SVD). This technique, like principal components analysis, reduces the overall dimensionality of the TF-IDF matrix to a lower dimensional space, in which each consecutive dimension represents the largest degree of variability possible.

After the implementation of the SVD on the TF-IDF matrix, an expectation-maximization algorithm was used to create clusters. Several experimental runs were conducted to identify the “optimal” number of clusters, which was determined following three main criteria: (1) clusters’ sizes, (2) the plausibility of the clusters identified, and (3) the statistical properties in terms of the relationship between within-cluster and between-cluster variance ( Calinski and Harabasz, 1974 ; Bigliardi and Galati, 2016 ).

Several cluster analyses were performed (2 clusters, then 3 clusters, 4 clusters, and so on) until a solution characterized by 15 clusters was reached. Then, each clustering solution was evaluated according to the above-mentioned criteria. As for criteria number 2, for each clustering solution obtained, an iterative process of word selection for each cluster was performed, aimed at identifying the most descriptive terms. This technique was based on the relative frequency of each term in each cluster, measured considering the mean of the number of times each term appears in the cluster and the number of documents in the cluster, including that term. The process started by deleting the terms that frequently occurred in more than one cluster. Then, the process calculated a descending rank of terms for each cluster based on the relative frequency measure. To be considered as a descriptive term of a given cluster, a term needed to be in a high position in the rank and to appear only in one cluster, or to be in a high position only in one cluster and in a low position (third or fourth quartile) in the others. After obtaining the main descriptive terms for each cluster and in each clustering solution, their convergence toward a given topic or a meaningless association was evaluated, allowing us to judge whether a given clustering solution was plausible or not. This was done for each cluster analysis solution. At the end of this process, the best solution was identified.

2.3 Postprocessing

The last step (postprocessing) consists of the following substeps. Clusters’ interpretation was performed to interpret the meaning of each cluster and to analyze the evolution of each thematic area over time, highlighting and thus allowing us to understand whether a topic was relevant or outdated. In more detail, based on the main descriptive terms that emerged, the contents of the clusters were presented, thus revealing the main issues discussed in the papers included in each cluster.

Then, to further validate the results obtained in the previous steps and to reach the second objective of the present study (i.e. to propose a future research agenda), a review of recent and relevant studies was performed. It soon became apparent that effective validation of the analysis could be achieved only by reading each of the selected studies and evaluating whether each paper was included in the correct cluster. However, the implementation of such a manual and time-consuming process would render useless the text mining methodology itself. As such, based on the work of Kobayashi et al. (2018) and Galati and Bigliardi (2019a) , we combined qualitative experts’ evaluations of the plausibility of the resulting clusters and extracted (for clusters referring to relevant issues) ten of the most recent papers published in specific journals. It is worth noting how this method should not be deemed a way with which to achieve strong validation, but a first empirical attempt to judge the consistency of the clustering results.

In the third substep of postprocessing, papers considered in the previous phase were analyzed to derive future research avenues. The decision to develop future research avenues from the most recent papers included in the relevant thematic areas were derived from the need to identify recent research conducted on the topic ( Galati and Bigliardi, 2019a ). Therefore, other approaches (e.g. selecting the most cited papers) could have favored older manuscripts, meaning any suggestions for future research could have already been addressed in subsequent studies.

In the following section, the results of the adopted methodology are presented in detail.

3.1 Patterns interpretation and evaluation

As a solid starting point, the analysis allows us to portray the broad trends of research on OI. Specifically, Figure 2 shows the number of publications per year, from 2003 (when the first study by Chesbrough was published) to 2018 [ 1 ]: as shown, the number of investigated papers dealing with the topic has grown exponentially. Table 2 lists the top ten journals by the number of papers on OI published.

In order to identify the natural groupings of the articles (by putting them into separate clusters) and then list the most descriptive terms that characterize those clusters, we conducted a cluster analysis, representing the most commonly used analysis technique in text mining studies. We conducted several experimental runs to identify the “optimal” number of clusters, which was identified as 9, each of them representing a thematic area. To identify the descriptive terms for each cluster, we implemented an iterative process. We deleted the terms that frequently occur in more than one cluster (e.g. “Open,” “Innovation,” “Collaborat,” etc., where terms like “Collaborat” is the root of a group of words such as “Collaboration” or “Collaborative.”. Then, we calculated a descending rank of terms for each cluster based on the relative frequency measure. The descriptive terms associated with each cluster gave us an idea about the possible interpretation of the corresponding topics. The results of the clustering experiment are illustrated in Table 3 : in the first two columns represent the cluster number and label, respectively, while the third column lists the most descriptive terms for each cluster.

Figure 3 shows the size of each cluster (in terms of the number of papers). The most numerous clusters are, in order, clusters 5, 1, 9, and 2.

Figure 4 presents the evolution over time of the corresponding thematic areas for each cluster. The figure reveals how some investigated thematic areas have arisen since the emergence of the OI paradigm (i.e. the thematic areas of clusters 4 and 5), other thematic areas started after a few years of delay (i.e. clusters 1, 2, 8, and 9), while for the remaining clusters 3, 6, and 7, the first papers appeared after 2009. In addition, the same figure also shows how the number of papers has grown within each cluster. Almost all the thematic areas have grown, as shown by the growing number of publications.

In the following section, we briefly describe the contents of each thematic cluster identified through the analysis.

A number of papers focused on the “context dependency of OI” (cluster 1), whereby the most evident external context characteristic is that of industry. The adoption of OI was at first investigated within pioneering high-tech industries such as software, electronics, telecommunication (e.g. Chesbrough, 2003 ; Bigliardi et al. , 2012 ), and only after within other low-tech industries (e.g. Bigliardi and Galati, 2013 ). Numerous studies refer to specific industries: automotive (e.g. Ili et al. , 2010 ), biotechnology (e.g. Fetterhoff and Voelkel, 2006 ), consumer electronics (e.g. Chesbrough, 2003 ), food (e.g. Sarkar and Costa, 2008 ), and so on. Other studies have investigated differences in OI adoption between industries (e.g. Keupp and Gassmann, 2009 ; Lichtenthaler, 2008 ). However, a number of studies claim that the adoption of OI is more related to business strategy than to industry trends (e.g. Keupp and Gassmann, 2009 ), thus suggesting that internal factors are more important than external ones in explaining the adoption of OI. Some scholars went beyond industry and suggested that the adoption of OI is influenced by other factors such as globalization, competitive intensity, technological aggressiveness, technology intensity and knowledge leveraging (e.g. Lichtenthaler, 2008 ; Poot et al. , 2009 ; Reed et al. , 2012 ), technology fusion, and new business models. Specifically, this paradigm is more appropriate in contexts characterized by the above-mentioned factors. For instance, in contexts with a high degree of globalization, companies are more likely to adopt OI. Other relevant characteristics include the importance of patenting and other forms of intellectual property protection, market and technology turbulence, and market competitive intensity (e.g. Arbussã and Llach, 2017 ).

A second highly investigated topic within the literature is the “collaborative frameworks” that companies may adopt when opening up their innovation process (cluster 2). Over time, this topic has garnered increasing attention, indicating that OI research has moved from the firm to network level (e.g. Rampersad et al. , 2010 ) by focusing on how collaboration can be managed (e.g. Crespin-Mazet et al. , 2013 ; Huggins, 2010 ; Michelfelder and Kratzer, 2013 ). Collaboration has been studied in relation to two main aspects: the actors of collaboration, and the stages of collaboration. As for the latter, different models have been proposed (e.g. Lee et al. , 2010 ; Wallin and Von Krogh, 2010 ), while, for the former, numerous papers have investigated the outside players with whom a company can collaborate when innovating (e.g. Bigliardi and Galati, 2018 ). These players range from suppliers, customers, and competitors to universities, research institutions, and organizations (e.g. Sieg et al. , 2010 ; Spithoven et al. , 2010 ). Specifically, different studies have stressed that it is important to maintain a diverse partner base over time (e.g. Dahlander and Gann, 2010 ), with researchers paying particular attention to collaboration with value chain stakeholders (namely, suppliers, customers, and partners) (e.g. Clausen, 2013 ; Obal and Lancioni, 2013 ). Moreover, the roles of institutional networks, the public sector, and government policymaking are also emerging as topics (e.g. Freitas et al. , 2013 ; Lee et al. , 2012 ).

The third stream of research investigated the “organizational dimension of OI” (cluster 3). OI is, by nature, a business model (e.g. Badawy, 2011 ) and a form of organizational innovation in itself (e.g. Christensen, 2006 ). By exploring the antecedents leading to a company opening up its business model, some studies have proposed different organizational dimensions (e.g. interorganizational networks, organizational structures, evaluation processes and knowledge management systems) or stages (unfreezing, moving and institutionalizing) as a way to move from a closed to open innovation model (e.g. Chiaroni et al. , 2010 ). Other publications have used the enabling role of top management and the promotional role of a champion as an example of organizational change (e.g. Bianchi et al. , 2011 ; Mortara et al. , 2009 ). However, few studies have dealt with the “human side” of OI (e.g. Chatenier et al. , 2010 ). Some of these scant available studies have investigated the OI challenges faced by R&D professionals and their coping strategies (e.g. West et al. , 2014 ), the link between individuals’ openness to external knowledge sources and the innovation performance (e.g. Dahlander et al. , 2016 ; Salter et al. , 2015 ), or the ways in which specific CEOs’ characteristics can facilitate the adoption of OI (e.g. Ahn et al. , 2016 ).

Another highly investigated topic is that of the link between “performance and OI” (cluster 4), in particular, how OI impacts performance and the aspects of OI that make it effective (e.g. Dahlander and Piezunka, 2014 ). OI performance has been measured in different ways: in addition to the obvious financial benefits (such as lower costs), performances driven by OI may also be nonfinancial. For example, a shorter time to market and more sales, innovativeness, number of innovations, gaining access to new markets, and enhancing a firm’s technological position (e.g. Lichtenthaler, 2007 ; Nagaoka and Kwon, 2006 ; Rigby and Zook, 2002 ) have been proposed as measures of OI success. Contrasting results have been provided on this matter: on the one hand, some studies have found that OI has a positive impact on innovative performance (e.g. Tomlinson, 2010 ); on the other hand, too much OI has been found to hurt firm performance (e.g. Laursen and Salter, 2006 ). In addition, research has focused on the cost of openness, referring to the management of the networks of experts involved when adopting the OI paradigm, which increases with the number of interdependencies and relationships (e.g. Kim and Park, 2010 ).

Receiving the most attention, “external search for OI” (cluster 5) (e.g. Christensen et al. , 2005 ; Fey and Birkinshaw, 2005 ) relates to different types of openness; specifically, the three knowledge processes of knowledge exploration, retention, and exploitation, which can be performed either inside or outside a firm’s boundaries (e.g. Lichtenthaler and Lichtenthaler, 2009 ). With the term “inbound OI,” scholars refer to the internal use of external knowledge; conversely, “outbound OI” refers to the external exploitation of internal knowledge. In general, empirical studies have highlighted that companies perform more inbound than outbound activities (e.g. Lichtenthaler, 2010 ). A number of studies have investigated the possible reasons for external exploitation of internal knowledge (e.g. Rivette and Kline, 2000 ; Kline, 2003 ), highlighting (among others) historical reasons, the possibility of using existing relationships, and the fear of diffusing relevant knowledge. Some studies (e.g. Chesbrough et al. , 2006 ) claim that companies scan the external environment and/or marketplace, and if ideas and technologies they need are available, they use (source) or acquire them. Even if scholars agree on recognizing the importance of openness in terms of external sources, it is not apparent whether all companies rely on them (e.g. Christensen et al. , 2005 ). Moreover, different studies (e.g. von Zedtwitz and Gassmann, 2002 ; Huang et al. , 2015 ; Tortoriello, 2015 ) stress that, regardless of the benefits derived from adopting external sources, companies need the expertise to search for and evaluate them.

Since size allows one to characterize the evolution of research on OI (cluster 6), it is unsurprising that “OI in SMEs” is one of the most studied company characteristics. While early works mostly investigated the adoption of OI in large multinational companies (e.g. Chesbrough, 2006 ; Lichtenthaler and Ernst, 2009 ; van de Vrande et al. , 2009 ), recent years have seen SMEs begin to open up their innovation processes (e.g. Gassmann et al. , 2010 ; van de Vrande et al. , 2009 ). Numerous studies (e.g. Bigliardi and Galati, 2016 ; Henttonen and Lehtimäki, 2017 ; Oakey, 2013 ; Spithoven et al. , 2013 ; Wynarczyk et al. , 2013 ) claim that SMEs are hindered by both internal and external structural impediments, such as smallness, managerial capacity, skills, awareness of and access to external knowledge and finance, and fewer technological assets. However, in spite of the fact that adoption of OI is highly critical, SMEs adopt OI far less than multinationals do due to resource constraints and scale limitations (e.g. Brouthers and Nakos, 2004 ; Lee et al. , 2010 ). Interestingly, although this lack of resources can force SMEs to engage in OI practices, and thus, overcome these liabilities, by opening up their innovation processes (e.g. Barney and Clark, 2007 ; Gassmann and Keupp, 2007 ), they have fewer resources to build and maintain collaborative networks and to create and enforce intellectual property rights. This side effect has been found to affect both inbound and outbound activities (e.g. Lichtenthaler and Ernst, 2009 ).

Although the adoption of “OI in the pharmaceutical industry” has attracted limited research focus when compared to other areas of research, it does emerge as the main investigated industry with regard to the topic (cluster 7). Such interest is due to the high levels of R&D investments that initially characterized the industry, as well as the importance of the discovery process for innovation purposes, two aspects that are particularly appealing to innovation management scholars. More specifically, interest in this industry started almost six years after the introduction of the OI concept, when the pharmaceutical industry underwent a shift from a product-based economy to a service economy. Consequently, many companies—hoping to discover new drug candidates while remaining innovative and maintaining their market position—started to collaborate with academic partners (e.g. Kaitin, 2010 ; Paul et al. , 2010 ; Galati and Bigliardi, 2016 ). In other words, recognizing the potential, the pharmaceutical industry responded to the increasing difficulties and expenses of discovering new medicines by embracing OI to access external ideas (e.g. Hunter and Stephens, 2010 ; Schuhmacher et al. , 2013 ). All in all, pharmaceutical companies adopt different forms of OI, from licensing and acquisition of drug candidates to acquisition of entire companies, outsourcing, and so on (e.g. Schuhmacher et al. , 2013 ). The openness of the drug discovery process was enabled by the advent of the internet, which allowed ideas to be obtained from a wider scientific crowd (e.g. Glen et al. , 2017 ). For example, Living Labs have grown rapidly to support companies based on five basic principles, namely, value, openness, realism, influence, and sustainability. These labs represent a neutral arena where stakeholders meet to codevelop innovations in real-world contexts (e.g. Almirall et al. , 2012 ). These and similar models of OI adoption have become a standard in the pharmaceutical industry and an example for other industries (e.g. Gassman et al. , 2010 ).

Cluster 8 groups together 81 papers investigating two (seemingly) contrasting topics: “OI and intellectual property rights” (IPRs). Indeed, OI refers to an innovation system where ideas and knowledge flow across the permeable firm boundaries. In contrast, IPRs are generally used to exclude others from using a firm’s ideas and inventions (e.g. West, 2006 ). Specifically, IPRs refer to “ unique, value-adding creations of the human intellect that results from human ingenuity, creativity, and inventiveness ” ( Kalanje, 2006 , p. 1). IPRs are mainly used to prevent unintended knowledge drain and to allow companies to profit from OI collaborations (e.g. Bogers et al. , 2012 ). The adoption of IPRs has been investigated both within large companies and SMEs (e.g. Andries and Faems, 2013 ; Brem et al. , 2017 ). In order to protect innovation and to capture value from it, companies may adopt both formal methods (i.e. patent, trademark, or copyright protection) and informal methods (i.e. lead times, first-mover advantages, and lock-ins) (e.g. Luoma et al. , 2010 ). Patents are the most commonly used IPR (e.g. Thomä and Bizer, 2013 ), and a number of studies claim that IPR protection through patents is positively related to performance in terms of commercial success (e.g. Andries and Faems, 2013 ). However, other studies have shown that high sales performances are not automatically assured by having many patents (e.g. Agostini et al. , 2015 ). In addition to patents, industrial designs and trademarks (e.g. Kalanje, 2006 ), as well as copyrights (e.g. Depoorter, 2004 ), are considered important tools for bringing new products to the market and for preserving creative and innovative work.

Finally, the literature highlighted the central role of “technology” when adopting OI (cluster 9). Many authors have highlighted the role of technology in supporting those companies looking to find new sources for ideas and solutions (e.g. Chesbrough and Crowther, 2006 ; Dodgson et al. , 2006 ; Gassmann, 2006 ) and increasing their ability to work across different geographic and organizational boundaries (e.g. Pavitt, 2003 ). In other words, technology has enabled the shift toward OI (e.g. Dodgson et al. , 2006 ). Overall, the focus of research has moved away from the role of classic information and communications technology (ICT) (i.e. computers and the internet) toward new technologies, such as simulation, virtual reality, data mining, and rapid prototyping (e.g. Dodgson et al. , 2006 ). Recently, scholars have shown that companies are increasingly adopting crowd-based platforms to develop new products and outcome-based services (e.g. Frey et al. , 2011 ). Crowdsourcing is defined as an OI approach where companies broadcast innovation challenges in the form of open calls to usually unknown and undefined groups of external contributors (e.g. Howe, 2008 ). Specifically, it refers to outsourcing the function of idea generation to these large groups of external contributors (e.g. Howe, 2008 ; Kozinets et al. , 2008 ; Surowiecki, 2005 ). Companies use the crowd as a source of innovation; for instance, by disclosing through the online OI platforms the problems they face when adopting OI (e.g. Jeppesen and Lakhani, 2010 ; Sawhney et al. , 2003 ).

As highlighted in Figure 5 , four out of the nine clusters identified by our analysis have grown more than the others; namely, clusters 1, 2, 5 and 9. These represent hot thematic areas. For the further steps of the methodology (validation and identification of future research directions), we decided to consider only these clusters and to select the ten most recent studies, published in the first ten journals listed in Table 2 , for each of these clusters. This was done to select more reliable studies, given that they were issued in journals that often publish papers dealing with the topic. Table 4 lists such papers.

3.2 Validation

As for the initial validation of the results (i.e. the second subphase of Step 3 of the methodological process), nine experts (academic scholars conducting research on OI from different perspectives) were included, having been judged as “plausible” in terms of both the resulting descriptive terms and the clusters’ size. All experts involved were academicians who had written at least three papers on the OI topic throughout their career.

In addition to this “preliminary” validation step, a further validation test related to the resulting clusters was conducted, representing a first empirical attempt to validate the proposed method. This was performed by reading the abstract of the latest ten papers belonging to clusters representing relevant issues (papers presented in Table 4 ) and by evaluating whether they correctly fit the cluster in which they were classified. This analysis led us to affirm that only five papers (representing the 12.5% of the subsample) did not perfectly fit the cluster in which they were classified. In spite of the limited evidence, according to Galati and Bigliardi (2019a) , this percentage of error can be considered a good result in terms of methodological effectiveness, given the complexity of the methodology adopted.

3.3 Identification of future research avenues

In order to reach the second research objective (to identify future research avenues for each relevant issue), papers belonging to each cluster and selected in the previous validation stage were analyzed separately. The results of this phase are detailed in the following section.

3.3.1 Context dependency of OI (cluster 1)

What critical factors help organizations participate in Global Innovation Networks?

How can formal and informal, intra and interorganizational, local and global mechanisms of project-based learning and capability development in international project network organizations be balanced and combined?

What country-level or region-specific attributes represent central contingencies for the OI paradigm?

What influence will a global, mobile, more diverse workforce exert on the OI paradigm?

3.3.2 Collaborative frameworks (cluster 2)

How do public subsidies influence OI efficiency in different countries or industries?

What is the role of OI intermediaries in terms of the transfer and appropriation of ideas?

How effective are strategic human resource management practices in enhancing a firm’s R&D human capital?

3.3.3 External search for OI (cluster 5)

How does new ICT influence external search breadth and depth?

How does the absorptive capacity modify firms’ resources and capabilities?

What is the relationship between individual efforts in assimilating external knowledge and firms’ innovation?

What organizational capabilities are necessary for sustaining the assimilation of external knowledge at the individual level?

3.3.4 Technology (cluster 9)

What are the implications deriving from the involvement of crowdfunding backers in crowdfunding campaigns?

What are the reasons that drive people to act unethically in idea competitions?

What self-governing practices emerge through relational participant interactions in idea competition communities?

How can one define an ROI that measures the impact of social networking sites on the process of knowledge search in innovation activities?

4. Conclusions

4.1 discussion.

The OI paradigm is shaping the world we live in, and scholars belonging to a wide variety of academic disciplines are investigating the opportunities deriving from such a revolution. Through a comprehensive review of the literature on OI, the objective of this study is twofold: (1) to identify the main thematic areas discussed in the past and track their evolution over time; and (2) to provide recommendations for future research avenues.

In terms of the first objective, by applying the text mining technique developed by Delen and Crossland (2008) , we identified nine clusters that, based on the respective descriptive terms from the clustering analysis, were labeled as “context dependency of OI,” “collaborative frameworks,” “organizational dimension of OI,” “performance and OI,” “external search for OI,” “OI in SMEs,” “OI in the pharmaceutical industry,” “OI and intellectual property rights” and “technology.” Considering the clusters’ size and preliminary validation steps performed, we believe that each of them can be seen as a relevant thematic area in which scholars interested in the topic of OI could classify their research. The paper also made it possible to track the evolution of the thematic areas over time, highlighting the years in which they emerged, as well as their growth and/or decline. We observed that while thematic areas referring to clusters 4 and 5 — namely, “performance and OI” and “external search for OI,” respectively — had been investigated since the emergence of the OI paradigm, others started to be investigated later, thus highlighting how the topic has evolved and grown over time. The debate surrounding the OI paradigm started when scholars began discussing inward possibilities and the potential performance improvements deriving from its implementation. Then, other issues emerged, such as the role of technology, the role of context, the adoption of IPRs, and the development of collaboration frameworks. These topics have improved the level of the debate by exploring further issues that, over time, have been considered central in the discourse. Last, the research has deepened the investigation of the implications deriving from the adoption of the OI paradigm in more detailed settings, through which organizational consequences, considerations concerning SMEs, and specific industries (the most debated being pharmaceutical) have been explored. We believe that these findings could help bring further clarity to the broad and scattered field of innovation literature.

As for the second objective of the study, on the basis of the analysis of the most recent papers, we suggested several future research avenues that could be useful in guiding forthcoming investigations into the topic; in particular, for the four relevant issues of “context dependency of OI,” “collaborative frameworks,” “external search for OI” and “technology.” It is possible to claim that recent themes are intertwined with other research streams, a process that shapes the OI debate. First, globalization issues are shaping the centralized and local system of the R&D process, and firms are starting to allocate their innovation processes through a network of external partners and sites across the world. Second, the evolution and the mobility of the workforce poses challenges and needs in terms of strategic human resource management practices, which have implications for the enhancement of firms’ human capital. Finally, the digital revolution calls for reflections on the impact of such technologies on the OI paradigm, considering not only the possibility to improve the inward flows of ideas but also the implications for people’s behavior. As such, this paper suggests that the OI debate is evolving by combining its idiosyncrasies with other academic and political discussions, a development that calls for a huge research effort.

To the best of our knowledge, the present study represents one of the few attempts to implement text mining methodologies to conduct a comprehensive literature review on a given issue. We are confident about the usefulness of this tool for a wide variety of academic purposes, and we hope that our study can inspire not only scholars interested in the OI phenomenon but also those who understand the potential of text-based methodologies.

4.2 Policy implications

The analysis of the most recent papers investigating the four aforementioned relevant issues made it possible to identify different policy implications. First, to foster the development of novel innovations, policymakers should work on policies aimed at forming cooperation networks that involve research institutions and universities. Particular emphasis should be put on those companies that have never been involved in collaboration activities, as current policy measures tend to support companies that have already experienced cooperation. Second, policy measures should consider OI efficiency, particularly for SMEs, with a particular focus on the diminishing returns of OI in relation to innovation performance ( Greco et al. , 2017 ; Ardito et al. , 2019 ). Such a process may challenge the usefulness of establishing several and merely formal collaborations aimed at matching the constraints requested by public authorities, which can be ineffective in terms of innovation performance in that funds are dispersed. For example, in response to a recent European call, firms and universities chose to involve unnecessary partners with narrow contributions but with a strong international reputation and/or strategic geographic base. This reflects the inadequacy with which European subsidies are designed and the subsequent limited power of such subsidies in enhancing collaboration intensity; in short, there is not a proportional increase in turnover share from radical innovations. Thus, policymakers need to recognize the proper way to allocate their limited financial resources to improve firms’ innovation performance. Third, policymakers and authorities should find a better way to integrate subsidies with consultancy services that can provide the necessary support to firms in terms of searching for and coordinating with partners. In addition, such consultancy services can help companies to identify partners in possession of complementary R&D competencies and technologies. Finally, given the recognized value of culture as a source of competitive advantage at the country level (considered a nontradable resource), policymakers should consider adopting an innovation strategy that includes cultural contingencies. This would involve a reconsideration of countries’ idiosyncrasies, which can be turned into a competitive advantage by specializing in their innovation efforts as a function of their cultural endowment. This cultural alignment could be relevant for several kinds of innovation strategies incorporating different degrees of openness.

4.3 Limitations

We are conscious of the limitations of our methodological approach. Findings are influenced by the scope and nature of the underlying research design and methods. The main limitation relates to the fact that it was impossible to perfectly cluster together studies dealing with a wide variety of detailed topics. This implies the presence of studies that do not perfectly fit the overall meaning of the specific cluster. However, one of the aims of the research was to highlight the main thematic areas related to OI. In our opinion, the fact that a limited number of papers do not fit the right cluster is barely significant for the objectives of the study. In addition, for the development of future research avenues, only studies in journals that have published several papers on the topic were considered, a choice motivated by the will to include relevant and reliable research. However, this approach could be contested by those who believe that it is also possible to consider citations as a proxy of relevancy. Finally, the preliminary nature of the validation steps should be considered. As previously stated, they were aimed at ensuring the plausibility of the results emerging from the analysis; as such, they cannot be considered complete.

The methodological process (in the upper-right side of the figure (objective 1), the methodology adopted refers to the one proposed by Delen and Crossland, 2008 )

Number of papers published on OI over time

Number of papers in each cluster

The temporal evolution of thematic areas

The evolution of thematic areas over time

Some examples of recent reviews on OI

Most common outlet journals

Clustering results

The ten most recent studies for each hot cluster

Note(s) : *When the “text collection” phase was performed, these studies were in press, while during the subsequent phases they were published and assigned to a volume of the year 2019

The current study included, also graphically, articles published in 2018, even if their number is low due to the fact text collection was performed at the beginning of the year (see Figure 1 ).

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Innovation Blog

Open innovation: what it is and models to inspire your business.

The COVID-19 pandemic has brought open innovation to the foreground, with experts from different industries putting their heads together for the greater good.

For instance, Ford, United Auto Workers, 3M, and GE Healthcare have combined forces to build ventilators using F-150 seat fans, 3D printed parts, and portable battery packs.

While it may sound like the latest buzzword, open innovation has been around for almost 20 years. But now, as digital transformation and the startup age kick into high gear, C-suite members are finally taking note of the theory first coined by a University professor.

Read on as we find out what open innovation is, and why it matters more now. We’ll explore key benefits, types, and examples of the concept in action, and will study several open innovation models you can experiment with to drive progress and transformation in your business.

What is Open Innovation?

Open innovation is the practice of a company opening its R&D department to input from people outside the company, or, to employees from other departments within the organization. By breaking down traditional silos between departments, and welcoming external experts and researchers, companies remove the limitations that a classic, secretive model may place on innovative efforts.

The concept of open innovation was created in 2003, by Henry Chesbrough , a research professor at the University of Berkeley. Today, useful knowledge is widely distributed, and there is no company, no matter how big, that can maximize the potential of innovation entirely on its own. Chesborough explains that this model offers companies a decentralized approach to innovation that empowers growth.

Open vs Closed Innovation

Before we dive into open innovation, let’s first see how it compares to the opposite approach of closed innovation.

Closed innovation is the traditional model of ideation, research, and development that relies entirely on internal resources and expertise to generate, manage and sustain new business ideas. All information is contained within the company, often within the R&D department, with no sharing with external parties.

Open innovation , by comparison, refutes the classic approach by welcoming creative talent, research professionals, and subject matter experts from outside the company. The open innovation strategy is rooted in the belief that increased information sharing and collaboration will invariably deliver better results.

Types of Open Innovation

Companies can use open innovation in myriad forms, putting the philosophy to practical use in a way that best suits its business objectives. As we consider the different types of open innovation , we can develop a clear understanding of how best to use it.

We can classify open innovation according to four levels of inclusion:

After we determine the level of inclusion, we can define the purpose of use as one of the following:

When we cross-reference the level of inclusion with the use case, we have 16 types of open innovation.

For example, let’s say your business wanted to fill new positions quickly. With an intracompany approach to finding talent, you can identify people within your ranks who possess valuable skill sets.

If you want to develop products that reach the broadest possible audience, you can use a publicly open model to get insight from experts and potential customers during development.

The level of inclusion you wish to work with, and the primary goal are vital aspects that will determine which type of open innovation your business should choose. With these parameters in mind to guide your open innovation strategy, you can reap great rewards.

Benefits of Open Innovation

So, why should your business consider this method of innovation? Here are five benefits of open innovation for corporations that wish to use outside talent for R&D:

Access latest industry talent

Despite a push to train more developers and IT professionals in emerging fields like fintech , cybersecurity, healthtech , blockchain, and mixed reality technologies, there simply isn’t enough talent available to satisfy the market demand. If your business puts its stock in closed innovation, it limits the chances of innovating even more.

Open innovation enables companies to collaborate with as many bright minds as possible so that you can keep your company at the bleeding edge.

Access essential infrastructure and technology

While some startups can bootstrap their way to the big time, many are crushed by a lack of resources. Without the capital or technology to properly develop a concept, it often proves impossible to get an idea off the ground.

Open innovation presents new partnership opportunities, enabling startups to build relationships with university research facilities or larger enterprises that have the resources to bring products to market.

Develop additional revenue streams

Sometimes, certain projects don’t fit with the core business model. Rather than letting them go completely, a company can use open innovation to develop unrelated ideas externally. In doing this, they can establish new revenue streams and start new partner businesses that are separate from the primary business model.

Leverage co-creation

User-generated content is a powerful channel where companies leverage the feedback and ideas of their customer base to build brand awareness and social proof. But co-creation doesn’t need to stop at marketing—you can use it to innovate, too.

By involving customers in early stages of product development, you connect with the community you are trying to serve and implement their ideas in a way that helps ensure the final products and services are aligned with what matters most to your customers.

Reduce costs and development timescales

Larger enterprises can get stuck in their ways, held back by red tape and stringent processes. Meanwhile, smaller startups may have talent but often struggle because of a lack of resources and financial muscle.

By coming together, a partnership reduces costs for small startups and accelerates product life cycles for bigger companies. The union also spreads the risk for both companies.

Electrolux’s Head of Open Innovation, Lucia Chierchia , reiterates that such partnerships are not a one-way street, as the bigger company providing the financial and infrastructure support also gains a lot.

“There is big value when it comes to innovations from outside our company, and we needed to find a way to capture this value and build something new together with smart, external people.”

Examples of Open Innovation

Rapid developments in technology have encouraged more companies to experiment with new methods in the last decade.

Here are some open innovation examples from some of the most well-known companies in the world:

Facebook (Intracompany)

The dominant force in social media nurtures innovation on the inside with company hackathons , which help Facebook surface some of its best ideas from within its own ranks. These events give employees the opportunity to discuss and develop initial versions of product ideas they might have before product teams take on the

By opening the floor to everyone—not just developers—Facebook offers all employees the chance to think creatively, and it can even prove to be a springboard for some budding entrepreneurs to launch new ideas or segway into new career paths.

Philips (Intercompany)

Dutch technology corporation, Philips, is an early adopter of open innovation, as the company made a shift in that direction back in 1998 when it opened the R & D ecosystem now known as the High Tech Campus Eindhoven .

The campus is home to over 200 companies, with entrepreneurs, researchers, and product developers from all over the world coming together to create new ventures .

Many of the projects created on campus focus on solving real-world problems, such as the challenges posed by overpopulation, climate change, and failing healthcare systems.

Samsung (Intercompany)

Another intercompany example is the Samsung Accelerator program , which brings together entrepreneurs, designers, innovators, and experts, and offers them office spaces, capital, and product support to yield exciting new solutions.

The initiative provides a platform for four potential avenues of open innovation:

UNDP (For Experts)

The United Nations Development Programme (UNDP) promotes investment and collaboration for social good on an international scale. As mentioned earlier, the global pandemic has been a catalyst for innovation, with experts from all fields working overtime to figure out solutions that can help the world in the new normal.

As Rwanda struggled to cope with rising cases, the UNDP Accelerator Lab brought health professionals together with the Ministry of ICT and Innovation to design and deploy anti-epidemic robots.

These advanced robots help screen people and detect COVID-19 cases at Kigali International airport, and provide additional services in the local hospitals and treatment centers, including food and medication delivery.

Coca-Cola (Publicly Open)

When it comes to innovation, Coca-Cola is no stranger. The soft drinks company famously launched “ New Coke ” in the mid-80s. The rebranded product and new taste formula was a disaster. Sales plummeted, and the company was inundated with complaint calls. Eventually, the product was discontinued in the face of public backlash.

Today, “New Coke” is recognized as one of the worst marketing mistakes in history, offering companies a cautionary tale on why they shouldn’t mess with a working formula.

Nowadays, the company involves customers by allowing them to suggest and mix their own flavors in a freestyle dispenser machine . With the mobile app, customers can record their favorite new tastes, and get it from other machines in different locations.

Coca-Cola learned its lesson, but it didn’t put the company off innovation completely. By putting customers at the heart of product development, Coca-Cola gets valuable insight from public opinion, before they dare to launch any new products.

Different Open Innovation Models

Now that you understand the key benefits of open innovation let’s look at some of the most effective models. Here are three ways you can apply open innovation at your business:

Open innovation challenge

An open innovation challenge is an event where entrepreneurs, researchers, and specialist teams compete against each other to try and solve a defined problem in the industry. One example is Unilever’s innovation portal , which seeks solutions to problems companies face with packaging, transportation, and storage of food products.

Innovation challenges help companies gather ideas and find solutions. Usually, the problems are well-defined, like the Unilever program. However, it can sometimes require a broader approach to encourage thinking around a more diverse range of ideas. For example, the AT&T Accelerator Challenge strives to find new ways of enhancing the engineering capabilities of students.

Open innovation partnerships

Crowdsourcing takes a similar approach to an open innovation challenge, where a corporation partners with an accelerator to source innovation. The company comes up with an initial problem, question, or theme, and encourages people from outside to put forward ideas or potential solutions.

This model reduces R&D costs, and cuts down on production time, as your company can maintain open communication channels with your audience, getting input at every stage of production. As this process fosters connections between internal stakeholders and external innovators, crowdsourcing is an excellent method of building partnerships that offer mutual value.

Open innovation labs

An open innovation lab is a dedicated workspace that operates outside the normal routines and practices of the business.

Quite often, the teams that work in these labs comprise new hires and external experts, who will collaborate to provide solutions to targeted problems or else come up with ways to improve the existing products, services, or systems at the company.

Liberty Mutual opened an innovation hub called Solaris Labs, located in a Boston branch of WeWork . The lab has the goal of building and testing new products based on emerging trends and customer research.

The team in the lab operate like a startup in several ways, as they:

By setting up the lab in a WeWork, the company breaks away from the corporate mold and gets access to the fast-paced energy of the startup space. In doing so, it enables the team to think and work differently.

How to Become an Open Innovation Company with MassChallenge

When success has come easy at an established enterprise for many years—or even decades—shifting the proven paradigm can seem folly. The prospect of transforming well-ingrained practices and processes in a massive company not only seems risky, but it is also a daunting project.

And yet, ignoring the changing tides in your industry is a mistake no company can afford. Even if your business is secure now, there is no guarantee traditional methods of innovation will keep you at the top.

Becoming an open innovation company allows you to spread risks, reduce costs, and tap into the raw power of new talent and technology. Instead of limiting your company’s progress with a traditional, in-house focus, you open the door to valuable partnerships, ideas, and expertise in your industry.

Ultimately, this approach can drive transformation and sustainable growth for your company. What an accelerator, like MassChallenge provides, is the hub for bold problem solving, innovation, and acceptional entrepreneurs who all team with a variety of essential stake holders. Becoming involved with accelerators facilitates access, supports emerging talents and technology, and bolsters any organizations goals by maximizing open innovation.

Learn more about MassChallenge and what it means to be a partner or an expert for the global network for innovators who are working to solve massive challenges.

About the Author

Robbie Richards is an expert contributor to the MassChallenge blog for over two years. He writes on innovation approach, entrepreneur resources, and business and marketing research. He has been published in Forbes, Ahrefs, WordStream, and more.

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