How Collective Learning Improves
Innovation
App developers listen
to their customers and competitors to learn how to succeed.
Where
“entrepreneurial ecosystems” exist, there are hotbeds of rapid entry and
experimentation. These ecosystems consist of mostly small organisations that
share a technological architecture and a set of norms. Take the Apple App
Store, where app developers often rely on users who make concise suggestions on
improvements, point out bugs and cite competing products in their feedback.
This not only gives the app developer the consumer’s perspective, but also an
industry landscape view of the competition and competing apps.
Typically, legacy
businesses focus on individual-level learning with sporadic surveys and polls,
which give mixed data on their products and services from respondents who might
not be the most engaged or knowledgeable. App developers, on the other hand,
get feedback from active users and strive to achieve the coveted 5-star
ratings, which push their apps to the top of the store.
But does this rapid feedback and learning lead to more innovation?
To quantify how
beneficial this “population level-learning” is, we looked at a population of
more than 390,000 apps listed on the U.S. iTunes App Store market over 15 months, combined with the detailed data on
rankings and consumer comments, for our paper, Collective Intelligence of
Market-Categories in Entrepreneurial Ecosystems: Evidence of Population-Level
Learning in Mobile Applications. We find a considerable
difference in learning across and within each category.
Learning from the
community
The Games category
suggests more innovative learning, and gleaned more modifications from user
feedback than other categories. The following chart shows each category with
the number of apps, firms and top downloaded apps:
The Games category
surpasses the others in terms of downloads. Although there are many apps in the
Books category there isn’t much innovation and learning in this space.
Consumers already understand how to use electronic books and most book apps are
electronic modifications of the offline publications.
Those who download
games, however, are more willing to spend money on apps. This makes app
developers hyper-competitive and keen to learn from their competitors to
outcompete them. For example, when the first games for the App Store were
developed in 1997, there was a lot of debate about the best way to earn money
out of then-new platform. Games were the frontrunner of in-app purchases,
offering a free download followed by enhancements for sale.
Reviews in the Games
category are highly detailed. The learning loop is very well developed between
active consumers and active feedback. Because only users who have downloaded an
app can make comments, the feedback is valid. As reviewers’ comments often
compare features with similar apps, developers can investigate their
competitors – who they are and what they are good at, and then download
competitors’ apps for reverse engineering.
Charting group
learning
In addition we find
that as consumer feedback increases, their intelligence is able to merge. This
becomes clearer from the following chart, in which we ‘combined’ important
performance measures (the number of apps for a week relative to the mean number
of apps) to highlight the changing patterns of learning within apps’ Games and
Weather categories.
Looking at the
different categories offered in the App Store and how users interact with them,
we find considerable differences between categories in terms of how much they
improve over time. In the above charts, which show learning factor adjusted for
the number of apps in a category, we can see that while weather apps
experienced very little learning over the set period, apps in the Games
category improved significantly. This is thanks, in part, to active consumer
feedback prompting Games developers to make changes and users to learn new ways
to use their apps, increasing collective intelligence.
Spreading too thin
This active feedback
also helps developers spread across categories. They apply what they learned
from one category to other categories. App developers generally spread their
creations across two or three different categories. But there is a limit to
this; we found that the potential for developers to learn from feedback is diluted
as the collective learning is spread out.
In summary, our study suggests the
importance of facilitating active feedback between consumers and producers as
well as using that feedback for diversification. It is clear from our findings
that an entrepreneurial ecosystem facilitates learning as well as innovation,
including customers in the journey.
Jaemin Lee, Assistant Professor at Imperial College Business School, and Jason Davis, INSEAD Associate Professor of Entrepreneurship and Family Enterprise |
Read more at http://knowledge.insead.edu/entrepreneurship/how-collective-learning-improves-innovation-4950?utm_source=INSEAD+Knowledge&utm_campaign=cd6348080e-29_Sept_mailer9_29_2016&utm_medium=email&utm_term=0_e079141ebb-cd6348080e-249840429#HmrbHT8hBJkBDgMg.99
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