When History Repeats
Outsell published an analysis this past week citing Nature’s widely circulated article about Sci-Hub. It described shockingly high usage of an illegitimate and illegal site, demonstrating that Sci-Hub continues to pose challenges to the scholarly communications industry even while end-users love the site, even if some of them know what they’re doing is wrong.
Make no mistake — I’m not condoning Sci-Hub or the user behavior here. In our assessment, we call for improved user experiences, more responsive business models, and educating researchers. When I read our analysis and took note of our findings, I couldn’t help but be reminded of Napster and its upending of the music industry years ago.
There was a lot of handwringing then, and rightfully so, but what the music industry failed to recognize at that time was that their business models were broken. There was no Pandora or Apple iTunes back then; customers had to deal with forced bundling of music in the form of albums, did not have convenient access to their music, and were unable to mix and match songs using playlists. One of the reasons Napster succeeded was that it provided a better music experience that resonated with users.
Napster was shut down, but the music industry learned a lesson. It seized on the eggshells that Napster broke and created new models that were more conducive to how users wished to listen to music: frictionless, unbundled, and with the tools to let them be their own aggregators.
I’m not saying what Napster did was right, just like I’m not saying what Sci-Hub is doing is right. It was wrong and is wrong to steal other companies’ IP. But what isn’t wrong is the way users want to engage with music and with scholarly content. Our industry needs to take lessons from what’s happening here, just like the music industry did.
All too often, innovation comes from outside the industry and not from within. It is simply too hard to break one’s own business models or risk revenue loss while opening up a new model when shareholders get so comfortable with returns and the quarterly march of Wall Street persists.
There are great lessons outside our industry from those who have gone before us. It is simply one of the things we love about looking across various sectors — what we call “neighborhoods” of the data, information, and analytics industry. They converge and collide, but what we love most is that there are always lessons in other neighborhoods that prove helpful and relevant examples. Who would have thought that music would have a bearing on scholarly research? Yet here we are.