A question that many young researchers ask is how to get your papers published in top conferences and journal. There are many answers to this question. In this blog post, I will discuss a strategy for carrying research called the “Blue Ocean Strategy”. This strategy was initially proposed in the field of marketing. But in this blog post, I will explain how it is also highly relevant in Academia.
The Blue Ocean Strategy was proposed in a 2007 book by Kim, C. W. and Mauborgne, R. The idea is quite simple. Let’s say that you want to start a new business and get rich. To start your business, you need to choose a market where your business will operate. Let’s say that you decide to start selling pens. However, there are already a lot of pen manufacturers that are well-established and thus this market is extremely competitive and profit margins are very low. Thus, it might be very difficult to become successful in this market if you just try to produce pens like every other manufacturers. It is like jumping in a shark tank!
The Blue Ocean Strategy indicates that rather than fighting for some existing market, it is better to create some new markets (what is called a “blue ocean“). By creating a new market, the competition becomes irrelevant and you may easily get many new customers rather than fighting for a small part of an existing market. Thus, instead of trying to compete with some very well established manufacturer in a very competitive market (a “red ocean“), it is more worthy to start a new market (a “blue ocean”). This could be for example, a new type of pens that has some additional features.
Now let me explain how this strategy is relevant for academia.
In general, there are two main types of research projects:
- a researcher try to provide a solution to an existing research problem,
- the researcher works on a new research problem.
The first case can be seen as a red ocean, since many researchers may be already working on that existing problem and it may be hard to publish something better. The second case is a blue ocean, since the researcher is the first one to work on a new topic. In that case, it can be easy to publish something since you do not need to do something better than other people, since you are the first one on that topic.
For example, I work in the field of data mining. In this field, many researchers work on publishing faster or more memory efficient algorithms for existing data mining problems. Although this research is needed, it can be viewed in some cases as lacking originality, and it can be very competitive to publish a faster algorithm. On the other hand, if researchers instead work on proposing some new problems, then the research appears more original, and it becomes much more easy to publish an algorithm as it does not need to be more efficient than some previous algorithms. Besides, from my observation, top conferences/journal often prefer papers on new problems to incremental work on existing problems.
Thus, it is not only easier to provide a solution to new research problem, but top conferences in some fields at least put a lot of value on papers that address new research problems. Thus, why fighting to be the best on an existing research problem?
Of course, there are some exceptions to this idea. If a researcher succeeds to publish some exceptional paper in a red ocean (on an existing research problem), his impact may actually be greater. This is especially if the research problem is very popular. But the point is that publishing in a red ocean may be harder than in a blue ocean. And of course, not all blue oceans are equal. It is thus also important to find some good idea for new research topics (good blue oceans).
Personally, for these reasons, I generally try to work on “blue ocean” research projects.
In this blog post, I have discussed how the “Blue Ocean Strategy” and how it can be applied in academia to help in publishing in top conferences/journals. Of course, there are also a lot of other things to consider to write a good paper. You can read the follow up blog post on this topic here, where the opportunity cost of research is discussed.
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