Monday, November 27, 2017

Improving Evidence Presentation: An Example and Some Tips

This is an unusual blog post because it is on how to do research, not on what we have learned from research that has been done. Well, there is also something on what we have learned. I think it is very important that researchers show the data in their manuscripts by making graphs that show the reader the phenomenon and their explanation of it. This is not easy to do currently because management journals like models more than they like graphs, and models don’t show the data as clearly as graphs do.

As the editor of Administrative Science Quarterly, I am encouraging authors, associate editors, and reviewers to use more graphs. I also do it as an author, and this blog post is about a paper in Advances in Strategic Management that I wrote with Seo Yeon Song. We analyzed the ebook business, and our starting point was that there is a big movement toward self-publishing and indie (independent) publishing there, with an increased market share relative to the Big 5 publishers. Here is the graph showing this change:

How did we explain the change? Big 5 publishers can pay for advertisements, unlike indies, so indies must have some other advantage. We thought it was their readers rewarding good indie books with tweets and reviews on the website. Here is a comparison of how Amazon reviews affect sales of Big 5 and indie ebooks:

See the difference? Indies don’t have advertisements to support sales, so each new review increases their sales more. This is something that can be seen from the data without any modeling. Of course we also modeled the data. I won’t show the model here, but instead show a graph comparing the effect of Amazon reviews (the count), Amazon review score, tweets (the count), and sentiment (how positive they were). It is easy to see the results, right? Amazon reviews have a much stronger effect than Twitter posts.

Finally, here is a graph that shows the review effect in a model that extracts all other effects we could control for, such as the tweets. This is called a residual graph, and it can be used to check how much of the relation between the reviews and sales is explained by other factors. The answer is… almost nothing. This graph (a residual graph) is visually nearly the same as the earlier one. It also shows how much is left to explain by other factors that are not yet in the model, which is clearly a lot.

Well, this was a short story about ebook sales, but the more important point is that researchers can show their findings well just by graphing the data. If you want to see the program that made these graphs and some sample data to use it on, click here and here.

Thursday, November 9, 2017

Going Back and Doing Good: When Foreign Workers Return Home

Here is an interesting contradiction: Some politicians say that relying less on foreign workers will make their nation more competitive, but in fact it makes the workers’ home country more competitive. Notice that I said contradiction, not paradox, because it is not a paradox at all. It is logical, and it is supported by recent research.

Here is how it works, as explained in an article by Dan Wang in Administrative Science Quarterly. Foreign workers are often used by highly advanced and competitive firms, because those firms are best positioned to take advantage of a worker’s skill wherever it is found, and to transfer it to wherever it is needed. They also have excellent production processes, advanced technologies, and knowledge on how to best operate these. Sometimes their foreign workers go back to their home countries (usually voluntarily). What happens then?

The start is quite simple. These workers may be holding knowledge of great value to firms in their home countries, so the key is whether they can make a knowledge transfer back home. The firms that hire them, or the new firms they form if they become entrepreneurs, will benefit from their knowledge. But the full story is not as simple as the start. These workers differ in how well connected they are to others, in the companies they worked with abroad, and in the companies they work with after returning. Their personal networks differ in how many people they know and how well they know them. It turns out that knowledge transfer depends greatly on these connections, because the greatest transfers happen when a worker is highly connected both abroad and after returning home.

The conclusion is clear. Playing the competitiveness card may be a good way to cater to xenophobia among voters, because those who prefer fewer foreigners around like to hear reasons for their dislike. (Even if the excuse isn’t true, it is nice to have an excuse.) But competitiveness is not a valid reason to send foreign workers home.

Wang’s research had one more important conclusion: it was not just personal networks that made knowledge transfers effective, but also an absence of xenophobia in the home country. Now the contradiction becomes even more interesting. Xenophobic policies of sending people home may be phrased as helping competitiveness, but they usually hurt it — except when the workers come from a country with xenophobic people, because then the knowledge they have won’t transfer back. Xenophobia is a lose–lose proposition.