The Complex Relationship Between Crowdsourcing and Innovation

Innovation and Crowdsourcing are complex topics by nature, each with their own set of unique challenges; for insight professionals, this means working out how to carefully navigate both topics when they arise. Taking them on their own isn’t too much of an issue, but if we are looking to use crowdsourcing as a way of ideating and developing an innovation, then that’s when things start to get a bit tricky.

There are articles relaying both the positive and negative effects of crowdsourcing on the pursuit of innovation, exploring success stories and tales of woe, all with their own merits and points that we should take into account when using the crowdsourcing method to strive for innovative insights. My aim in this blog is to bring some of that information together to explore why crowdsourcing and innovation have such a complex relationship and see what we can do to mitigate any issues that may occur.

The Trouble with Crowdsourcing and Innovation

First of all, crowdsourcing is a method that outsources a task generally assigned to a professional to a large group of customers and consumers in the form of an open call. So they participate in research that identifies innovation and brings it to life. There have been many positive impacts on crowdsourcing on innovation such as greater opportunity identification and attainment, but opportunity isn’t always realised to the best of its potential. There are also a few drawbacks that make us really think about whether this method is worth the insights it produces.

Here are some of the issues that have been identified with crowdsourcing:

1. What happens in regards to intellectual property? Who owns what if you’re putting ideas out into the air for all to see and comment on and improve?
2. What about the data security and protection of the participants and the company? With nothing confidential, how can you promise that the safety of their personal data?
3. Participants are more prone to derailment and tangents when chatting together, thus we sometimes don’t actually get any answers at all because they’ve gone off on a different topic.
4. They don’t know how to contribute properly, thus allowing poor quality data to filter through the cracks and flood the datasets generated.
5. They are not experts in your field, they do not know the precision required to keep the business afloat, nor the context in which your business is thriving, they can lead you in the wrong direction at whim and they won’t even realise they’re doing it. An even worse scenario would be that they think they know all about the landscape you’re operating in, and actively if still unintentionally derail your entire operation with their opinions.

These ethical and operational issues mean that navigating crowdsourced research in the name of innovation is tricky, and most of the insights generated are essentially useless.

But this doesn’t mean that crowdsourcing is useless in innovative efforts, in fact, there are some crucial success stories that help us understand how to navigate this minefield, like Apple and their app creation techniques, Lego’s fantastically successful Ideas Platform, and Dell’s IdeaStorm site that allows customers a direct link to the technology company for developmental suggestions. And with examples like these fuelling the fire, we need to talk about how we manage the research process to mitigate the risk posed by crowdsourcing to both participants and organisations?

Mitigating the Risk

The first issue to mitigate is consumer desire vs. consumer need. With crowdsourcing research panels primarily made up of strangers rather than professionals, while they can complete tasks and problem-solve to the best of their ability they don’t have the knowledge to take advantage of each research opportunity like insight professionals do. While this sometimes leads to innovative ideas that wouldn’t have been discovered otherwise, most of the time, it takes a while for those ideas to emerge as fully-dependable insights.

Allowing professionals to either moderate the discussions going on or recruit professionals to balance out the mix of participants could help enormously with this issue. Moderation is the most commonly used fix for this issue, which allows us to evaluate any ideas and insights that spark conversation. Checking to see if the response is objectively considered across the board (hard with consumers who all have their own lives to consider if this innovation is brought to life) so all ideas are treated with equal weight while discussions happen can help with this issue. While moderating is mainly used fix to this issue, there are studies to suggest that experimentation of using professionals as participants could benefit crowdsourced projects even if they do need more research into that particular topic.

Regular moderation can also be very useful to help keep participants on track with discussions; derailments are a waste of time and resources in research, but is a common occurrence, especially in collaborative research tasks. With crowdsourced research communities, constant moderation while they’re working on tasks or chatting amongst each other is a mammoth unenviable task, but will be worth the work put in when the innovative ideas start flowing under the watchful eye of insight professionals in both in and out of research tasks. One thing you do need to be careful of with moderation, is not to stifle any creativity within the discussions taking place. With too rigid structures in place, it's hard for natural conversations to take place, and thus natural innovative insights to emerge.

But even if crowdsourced research does generate some innovative ideas, it’s commonly known that you can’t please everyone - a sentiment that people-pleasers would happily dispute, if it didn’t mean they’d be disagreeing with you! However, it’s a fact that we encounter on a daily basis as people and as insight professionals, with participant discussions rarely 100% agreeing with each other and the data reflecting that.

It’s only natural that we have conflicting opinions, but that makes it hard for us to innovate effectively when consumers change their mind, or the crowdsourced panel evolves with new participants disagreeing with those that have come before them, thus muddying the water and making those valuable insights that are generated hard to see clearly. Working with the majority to produce valuable insights works most of the time, but the need for continuous agile testing is strong in this case, and will help us decide if an innovative insight is worth the time to pursue.

The last issue to discuss here, is the issue of intellectual property and data protection. If these innovative ideas, designs, products and services are created with the input of consumers, who then is entitled to the results? These crowds are not governed by the laws and contract of employment, they are operating freely on behalf of themselves rather than the company benefitting from this research. Another issue with intellectual property, is that how do we know the ideas being put forwards are original, and not stolen from another platform and company trying to achieve the same thing?

With some forethought and extra planning, there are a few ways that scientists and researchers are dealing with this issue: the first is crafting the terms and conditions to reflect the needs of the organisation, and help them gain some semblance of control over the content consumers produce while also acknowledging their contribution through fair recognition (it has been argued in this study that “participants who submit content do so with the expectation that their work may be used by the soliciting organization”; the second is through legal contract, acquiring the rights to the participants’ intellectual property, or at the very least, acquiring a license to the content.

The Complex Relationship

When crowdsourcing innovation, there are a number of unique challenges insight professionals must overcome, or at least be aware of when entering into this complex relationship. It’s a lot of work, but there are a myriad of benefits to it when implemented in the right way; taking a look at success stories like Apple, Lego, and Dell, allow us to understand, firstly how it can be achieved on a long-term basis, and the impacts on the organisations who utilise this tricky methodology.