How Innovators Choose Their Next Career Move
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Careers Aug 2, 2017

How Inno­va­tors Choose Their Next Career Move

There is an over­ar­ch­ing pat­tern in how inno­va­tors, like Elon Musk, shift their focus over time.

Due to the recency effect, an innovator bases her next career move off of previous experience in a similar knowledge space.

Lisa Röper

Based on the research of

Tao Jia

Dashun Wang

Boleslaw K. Szymanski

What do online finan­cial ser­vices, com­mer­cial space­craft, and mass-mar­ket elec­tric cars all have in com­mon? Oth­er than being indus­tries shaped by ser­i­al entre­pre­neur Elon Musk, not a whole lot.

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What prompt­ed Musk to choose to veer from Pay­Pal to SpaceX, to Tes­la? For that mat­ter, what makes any­body choose the next move in their career?

Dashun Wang, an asso­ciate pro­fes­sor of man­age­ment and orga­ni­za­tions at the Kel­logg School, won­dered if such seem­ing­ly mer­cu­r­ial choic­es could be mod­eled sci­en­tif­i­cal­ly. Musk, whom Wang con­sid­ers his per­son­al hero,” may be an extreme case. But every ser­i­al entre­pre­neur, artist, or sci­en­tist — that is, any­one whose job involves dis­cov­ery, exper­i­men­ta­tion, and decid­ing what to work on next — thrives on seek­ing out new challenges. 

And under­stand­ing how pro­fes­sion­al inno­va­tors move from project to project has impli­ca­tions beyond the mere­ly philosophical. 

If we know indi­vid­u­al­ly how these peo­ple tend to change direc­tion, then that deter­mines col­lec­tive­ly where things are going,” Wang says. 

A Method to the Madness

So how do inno­v­a­tive peo­ple choose their next career move? Is it real­ly as ran­dom as it often seems? 

Wang, who is also a fac­ul­ty mem­ber at the North­west­ern Insti­tute on Com­plex Sys­tems, is used to tack­ling ques­tions that might seem too ambigu­ous to yield to quan­ti­ta­tive analy­sis. We’re the unrea­son­able opti­mists,” Wang says. We fig­ured that there’s got to be some pat­tern that we can document.” 

Let’s first under­stand how our inter­ests change dur­ing the course of our careers — then we can debate about what the best strat­e­gy is for mak­ing those changes.” 

Wang and his coau­thors Tao Jia of South­west Uni­ver­si­ty in Chi­na and Boleslaw K. Szy­man­s­ki of Rens­se­laer Poly­tech­nic Insti­tute began by nar­row­ing their inves­ti­ga­tion to one par­tic­u­lar domain of inno­va­tors: physicists.

This sci­en­tif­ic com­mu­ni­ty uses a detailed set of numer­i­cal codes — the Physics and Astron­o­my Clas­si­fi­ca­tion Scheme (PACS) — to define research top­ics, much like the Dewey Dec­i­mal Sys­tem uses num­bers to cat­e­go­rize the sub­ject mat­ter of library books. The researchers used these codes to ana­lyze how the work of the approx­i­mate­ly 10,000 physi­cists in the data­base changed from project to project. 

We real­ized that there’s a remark­able amount of reg­u­lar­i­ty in what we choose to do next in our careers,” Wang says. 

Choos­ing Your Next Career Move

Sev­er­al trends emerged from their analy­sis. The first is that most physi­cists’ research stayed rel­a­tive­ly con­strained with­in par­tic­u­lar dis­ci­plines or domains. 

What this means is that most peo­ple” — even inno­va­tors — don’t change” as much as they con­ceiv­ably could, explains Wang, who holds a PhD in physics himself. 

When researchers did shift to a new project, it tend­ed to be one that was very close to a pre­vi­ous project in terms of what Wang calls knowl­edge space.” 

But which pre­vi­ous project? Some­what unex­pect­ed­ly, accord­ing to the data, the physi­cists’ most recent projects exert­ed the most dom­i­nant influ­ence on what project they chose next. 

Accord­ing to Wang, the physi­cists’ deci­sions go against a com­mon intu­ition about inno­va­tors’ deci­sion-mak­ing. That idea posits that the more time is spent mov­ing in one direc­tion, the fur­ther one gets ahead of the com­pe­ti­tion, and the more incen­tive there is to choose new projects that align with that ini­tial direc­tion. If physi­cists were try­ing to max­i­mize their hard-won knowl­edge about that top­ic, they would dou­ble down on projects in sim­i­lar domains as their ear­li­est efforts. 

So your fourth project should more like­ly be sim­i­lar to your first project than your third,” Wang says. 

Instead, he says, what we find in the data is the oth­er way around. If you study some­thing, the next top­ic you study is pre­dom­i­nant­ly deter­mined by what you stud­ied last—not what you stud­ied first.” 

Next Moves

This recen­cy effect like­ly applies even to appar­ent out­liers like Musk. Take his lat­est ven­ture: a start­up called Neu­ralink, spe­cial­iz­ing in brain – com­put­er inter­face tech­nol­o­gy with the goal of allow­ing human brains to keep up with arti­fi­cial intel­li­gence. It seems whol­ly unre­lat­ed to Musk’s pri­or suc­cess­es with online pay­ments, elec­tric cars, and rock­etry. How­ev­er, one of Musk’s less­er-known projects — a non­prof­it research com­pa­ny called Ope­nAI, focus­ing on arti­fi­cial intel­li­gence — does share clear sim­i­lar­i­ties with Neu­ralink. Musk’s involve­ment in Ope­nAI began just two years before he launched Neuralink. 

Still, while Wang’s mod­el sheds new light on how inno­va­tors choose their next projects, it doesn’t speak to how they should choose — both to move for­ward an entire field and their own careers. 

Let’s first under­stand how our inter­ests change dur­ing the course of our careers — then we can debate about what the best strat­e­gy is for mak­ing those changes,” says Wang. 

Featured Faculty

Dashun Wang

Associate Professor of Management & Organizations

About the Writer

John Pavlus is a writer and filmmaker focusing on science, technology, and design topics. He lives in Portland, Oregon.

About the Research

Jia, Tao, Dashun Wang, and Boleslaw K. Szymanski. 2017. “Quantifying Patterns of Research-Interest Evolution.” Nature Human Behaviour. doi:10.1038/s41562-017-0078

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