The So­cial As­pects of In­no­va­tion

Rotman Management Magazine - - FACULTY FOCUS - In­ter­view by Karen Christensen

You have stud­ied the ways in which in­no­va­tion is con­tin­gent upon so­cial struc­tures. What are some of your key find­ings?

First off, it’s worth not­ing that this stream of re­search con­sti­tutes a rad­i­cal de­par­ture from the tra­di­tional heroic, ‘great man’ view of in­no­va­tion. From Alexan­der Graham Bell to Thomas Edi­son — right up to con­tem­po­rary ex­am­ples like Steve Jobs — in­no­va­tion has been closely as­so­ci­ated with ‘rare gifted vi­sion­ar­ies’, who see the fu­ture in ways most peo­ple can­not. But in­no­va­tion doesn’t oc­cur in a vac­uum: we for­get that Bell had Thomas Wat­son, Edi­son had a lab, and Jobs had Woz­niak, Ive and oth­ers.

Too of­ten, we ne­glect the so­cial mi­lieu within which in­no­va­tion is cul­ti­vated — the con­crete, on­go­ing, ev­ery­day in­ter­ac­tions in­ven­tors have with their col­leagues. This ecosys­tem is what pro­vides the raw in­gre­di­ents for in­no­va­tion, and there is or­der to it: there are pat­terns to the in­ter­ac­tions, and by record­ing them — for in­stance, who goes to whom for in­for­ma­tion, ad­vice, prob­lem solv­ing, friend­ship, etc.? — we can con­struct a ‘map’ that rep­re­sents the ‘col­lec­tive in­tel­li­gence sys­tem’ of an or­ga­ni­za­tion.

It turns out that such maps tell us some very im­por­tant things about the dy­nam­ics of in­no­va­tion. I’ve been con­duct­ing this re­search with Pro­fes­sors Ray Rea­gans (MIT) and Marco Tor­to­riello (IESE), and we have found that in­no­va­tion is truly a col­lec­tive en­deav­our. While not dis­miss­ing in­di­vid­ual in­tel­li­gence and ef­fort, the way you are con­nected to oth­ers also mat­ters. We have also learned that there is tremen­dous vari­a­tion across in­di­vid­u­als in terms of how they are con­nected to their col­leagues, and that there is not just one type of ‘op­ti­mal net­work’ for in­no­va­tion. In­stead, there are sev­eral dis­tinct fea­tures of net­works that de­scribe the dif­fer­ent po­si­tions that in­di­vid­u­als oc­cupy, and each mat­ters in dif­fer­ent ways. My co-au­thors and I are paint­ing a very dif­fer­ent pic­ture of in­no­va­tion: rather than be­ing the province of bril­liant sa­vants, we are show­ing that it is deeply and in­ex­tri­ca­bly em­bed­ded in net­works of so­cial re­la­tion­ships.

How do you de­fine ‘bro­ker­age’ and ‘clo­sure’ in net­works, and how do they re­late to in­no­va­tion?

These are two of the most im­por­tant con­cepts in our re­search, and in­ter­est­ingly, they have also been shown to be of crit­i­cal im­por­tance in a di­verse ar­ray of other aca­demic fields — from As­tro­physics to Ge­net­ics to Neu­ro­science. In sim­plest terms, the con­cept of bro­ker­age de­scribes ‘be­ing po­si­tioned in a net­work such that you are in be­tween two other peo­ple who are not them­selves di­rectly (or in­di­rectly) con­nected’. In net­work terms, we re­fer to this as a ‘bridg­ing tie’. If the only way for me to reach one of your con­tacts is through you, you are a bro­ker, and your ties to me and your other con­tact are ‘bridges’ in the net­work.

Bridges are ex­tremely im­por­tant, be­cause they are con­duits through which in­for­ma­tion and re­sources travel be­tween parts of a net­work that are oth­er­wise un­able to in­ter­act. The bro­ker who sits at the in­ter­sec­tion of the bridge oc­cu­pies a strate­gic lo­ca­tion, be­cause she has a rel­a­tively unique view over the rest of the net­work that no one else en­joys: she is privy to the in­for­ma­tion, ideas, trends, dis­cov­er­ies and op­por­tu­ni­ties that are cir­cu­lat­ing in my part of the net­work, and at the same time, she is able to ac­cess the same type of re­sources from her other con­tact(s). These con­tacts and I do not en­joy that same priv­i­leged ac­cess: we do not know what each other knows, ex­cept to the ex­tent that the bro­ker passes along in­for­ma­tion.

As a re­sult, bro­kers may see pat­terns that oth­ers don’t, and sense trends and op­por­tu­ni­ties sooner than oth­ers. They also have more op­por­tu­ni­ties to put un­re­lated ideas to­gether. From a strate­gic per­spec­tive, they can act as a fil­ter that de­ter­mines what, if any­thing, peo­ple in one part of the net­work know and un­der­stand about peo­ple in other parts of the net­work. How­ever, be­ing a bro­ker is not all good: there are costs in­volved in in­vest­ing in learn­ing about dif­fer­ent tech­no­log­i­cal and func­tional ar­eas, trans­lat­ing ideas, in­ter­act­ing with peo­ple who have very dif­fer­ent lan­guages, cus­toms and tra­di­tions, and deal­ing with con­flict­ing pres­sures and de­mands from peo­ple whose pri­or­i­ties, goals and pref­er­ences di­verge.

The con­cept of clo­sure is the po­lar op­po­site of bro­ker­age. It boils down to two peo­ple who are con­nected to each other, and who are both also con­nected to one or more of the same other peo­ple in the net­work. In net­work ter­mi­nol­ogy, these same other peo­ple are known as ‘mu­tual third par­ties’, and the net­work po­si­tion of clo­sure is iden­ti­fied by a ‘closed triad’ con­sist­ing of three peo­ple who all have ties to each other. In closed net­works, in­for­ma­tion cir­cu­lates rapidly: ev­ery­one knows what ev­ery­one else knows rel­a­tively quickly, and as a re­sult, it is rel­a­tively easy to co­or­di­nate with oth­ers, to cal­i­brate ex­pec­ta­tions, and to val­i­date the ac­cu­racy of in­for­ma­tion about not only ‘who knows what’, but crit­i­cally, who did what.

In closed net­works, norms and rep­u­ta­tion take on height­ened im­por­tance. What con­sti­tutes ‘good’ and ‘bad’ be­hav­iour is less a mat­ter of per­sonal opin­ion, and more a mat­ter of what the net­work de­fines as be­ing in ev­ery­one’s col­lec­tive best in­ter­est. More­over, the cir­cu­la­tion of gos­sip about who did (or did not) do what to whom is rapid, mak­ing one’s rep­u­ta­tion a par­tic­u­larly po­tent force. When you do a favour for me, word of your help­ful­ness of­ten ex­tends to our mu­tual third par­ties — as does your dis­plea­sure with me, should I de­cline to re­cip­ro­cate. So, my de­ci­sion about how co­op­er­a­tive and re­spon­sive to be to­wards you takes on the added di­men­sion of how my be­hav­iour will be viewed by oth­ers. As a re­sult, there is a pro­nounced ten­dency to­wards co­op­er­a­tion in net­works that are char­ac­ter­ized by clo­sure.

How does the net­work po­si­tion of a par­tic­u­lar in­di­vid­ual con­trib­ute to their in­no­va­tive­ness?

This is the ques­tion pro­fes­sor Rea­gans and I set out to ad­dress when we em­barked on what has be­come a 15-year re­search pro­gram. Our ap­proach has been to view in­no­va­tion in or­ga­ni­za­tions as be­ing grounded in learn­ing and knowl­edge shar­ing. Be­fore we be­gan, the few ex­ist­ing stud­ies had pri­mar­ily fo­cused on the type of ties that peo­ple had (‘strong’ ver­sus ‘weak’), with the pre­sump­tion that ‘type of

ties’ is a good ap­prox­i­ma­tion of ‘type of net­work’. We were able to show that that is not nec­es­sar­ily the case — and that the net­work po­si­tions of bro­ker­age and clo­sure have sep­a­rate and in­de­pen­dent ef­fects. The cru­cial ques­tion was, In what dis­tinct ways do bro­ker­age and clo­sure af­fect learn­ing and knowl­edge shar­ing?

Given their seem­ingly-op­pos­ing con­fig­u­ra­tions, it was widely be­lieved that bro­ker­age and clo­sure would have op­pos­ing ef­fects on learn­ing and knowl­edge shar­ing, but the literature was di­vided on which would be pos­i­tive and which would be neg­a­tive. In­ter­est­ingly, our re­sults showed that both are con­ducive to learn­ing and knowl­edge shar­ing — but in dif­fer­ent ways. The ben­e­fit of bro­ker­age is be­ing able to ac­cess di­verse knowl­edge, broaden your own knowl­edge base, and learn how to trans­late new knowl­edge into a lan­guage that oth­ers can un­der­stand and ap­pre­ci­ate. Bro­kers are ex­posed to greater op­por­tu­ni­ties to make novel com­bi­na­tions across dis­tinct pools of knowl­edge and, as in­di­cated, of­ten ac­quire the crit­i­cal skill of re­lat­ing some­thing new to some­thing that is well known.

On the other hand, the ben­e­fit of clo­sure is the greater will­ing­ness of in­di­vid­u­als to co­op­er­ate with each other. Knowl­edge shar­ing con­sti­tutes a ‘dis­cre­tionary favour’ by the sender on be­half of the re­cip­i­ent. Specif­i­cally, the sender takes time out from her own ac­tiv­i­ties to ex­plain to the re­ceiver some­thing that may al­low the re­ceiver to solve a prob­lem or ad­vance their own work. The favour is dis­cre­tionary in the sense that we of­ten de­cide for our­selves whether to pass on knowl­edge, re­spond to re­quests for in­for­ma­tion, elab­o­rate on nu­ances, or il­lus­trate how to ap­ply a con­cept. We have found that peo­ple in closed net­works are more likely to un­der­take such ac­tiv­i­ties.

Our find­ings in­di­cate that bro­ker­age and clo­sure are not nec­es­sar­ily in op­po­si­tion. The trade-off be­tween the two de­pends on how you de­fine the net­work. If you in­tro­duce two of your con­tacts that pre­vi­ously did not know each other, you have in­creased the level of clo­sure in your net­work while at the same time, de­creas­ing the level of bro­ker­age in the net­work. Yet, if you in­tro­duce a new hire to ev­ery­one on your team — but that new hire doesn’t know any of your con­tacts in the rest of the or­ga­ni­za­tion — the level of clo­sure on the team has in­creased, and your level of bro­ker­age in the rest of the or­ga­ni­za­tion has also in­creased.

In your latest work, you fo­cused on a par­tic­u­lar role in the in­no­va­tion process. How do you de­fine a ‘cat­a­lyst’ of in­no­va­tion?

If in­no­va­tion is not all about lone in­ven­tors, then what other roles mat­ter? This is the ques­tion that led Prof. Tor­to­riello and I, along with Carnegie Mel­lon’s David Krack­hardt, to de­velop the no­tion of cat­a­lysts. We think of cat­a­lysts as the ‘helpers’ who are of­ten hid­den in the shad­ows of star in­ven­tors — but who nonethe­less per­form an es­sen­tial role in the in­no­va­tion process. Analo­gies in the sports arena would in­clude bas­ket­ball player Dennis Rod­man, whose pres­ence on the court in­creased the scor­ing of Michael Jor­dan, but who was not a high scorer him­self; the hockey player Adam Oates, who is #6 in all-time as­sists, but #146 in all­time goals; and soc­cer player Cesc Fàbre­gas, who is #1 in all-time as­sists, but #128 in all-time goals. Cat­a­lysts usu­ally

don’t cre­ate in­no­va­tions of their own, but they pro­vide key in­puts and as­sis­tance to those who do. Some in­di­vid­u­als do a bit of in­no­vat­ing and a bit of cat­alyz­ing, but most in­no­va­tors are not cat­a­lysts, and vice versa.

This in­di­cates that these roles in­volve rather dis­tinct ac­tiv­i­ties: whereas in­no­va­tors are draw­ing on their net­work of con­tacts to ac­cess di­verse ideas, then trans­lat­ing that into novel out­puts, cat­a­lysts are more likely to ‘feed’ use­ful ideas to their con­tacts to en­able oth­ers to pro­duce cre­ative out­puts. The cat­a­lyst’s ‘job’, then, is to know their con­tacts well — what their ar­eas of ex­per­tise are, what their pri­or­i­ties are — and to be on the look­out for knowl­edge that is rel­e­vant and use­ful to them.

De­scribe the role of knowl­edge di­ver­sity in all of this.

Knowl­edge di­ver­sity is a ba­sic in­gre­di­ent in in­no­va­tion, be­cause it in­creases op­por­tu­ni­ties for novel com­bi­na­tions. Knowl­edge ac­quired from sources ex­ter­nal to the or­ga­ni­za­tion is a key source of such knowl­edge, which is why, not sur­pris­ingly, in­ter­nal­iz­ing ex­ter­nal knowl­edge is a key pri­or­ity for so many re­search-in­ten­sive or­ga­ni­za­tions. The more di­verse your or­ga­ni­za­tion’s knowl­edge base is, the more read­ily it can learn about re­lated ar­eas; yet the more di­verse the knowl­edge base is, the more dis­persed across spe­cial­ized groups it can be­come — mak­ing it in­creas­ingly dif­fi­cult for those pos­sess­ing di­verse knowl­edge to in­te­grate it.

This is where cat­a­lysts come in. A big part of what makes them ef­fec­tive is hav­ing con­tacts that pos­sess di­verse knowl­edge. They tend to have a keen sense of not only who knows what, but also, who needs what? Both types of aware­ness stem from the cat­a­lyst’s po­si­tion in closed net­works, which tend to in­volve fre­quent and re­peated in­ter­ac­tions among mu­tu­ally con­nected con­tacts. This fos­ters the de­vel­op­ment of a ‘shared lan­guage’, com­mon un­der­stand­ing, and the iden­ti­fi­ca­tion of ar­eas of ex­per­tise.

How do you de­fine ‘Model-based Prob­lem Solv­ing’, and how does it re­late to your work?

I would de­fine it as, ‘A sys­tem­atic way to an­swer the ques­tion, Why?, guided by a the­ory of cause(s) and val­i­dated with rel­e­vant data and ro­bust anal­y­sis’. Hav­ing started my ca­reer at Carnegie Mel­lon, I be­came steeped in what is known as ‘the Carnegie School tra­di­tion’ of or­ga­ni­za­tional re­search. A key tenet of the ap­proach is that or­ga­ni­za­tions are far from per­fect, ra­tio­nal con­struc­tions, but rather, works-in-progress that are bet­ter un­der­stood as ‘adap­tive learn­ing sys­tems’. Pro­fes­sor Rea­gans and I — who had deep ex­per­tise in so­cial net­work anal­y­sis—saw a num­ber of par­al­lels be­tween the learn­ing- and net­work-based views of or­ga­ni­za­tions, and we be­came in­ter­ested in the ques­tion of why there are dif­fer­ences in knowl­edge flows across or­ga­ni­za­tions.

Net­work the­ory pro­vided us with a novel way of con­cep­tu­al­iz­ing and study­ing learn­ing in or­ga­ni­za­tions that iden­ti­fied dis­tinct causal mech­a­nisms rooted in so­cial con­text and as­so­ci­ated with dif­fer­ent net­work po­si­tions. The net­work literature also pro­vided us with a so­phis­ti­cated method­ol­ogy specif­i­cally de­vel­oped for map­ping and an­a­lyz­ing net­works based on re­la­tional data. By tak­ing this sys­tem­atic, model-based ap­proach, our re­search not only added to our un­der­stand­ing of learn­ing, knowl­edge shar­ing and in­no­va­tion in or­ga­ni­za­tions, it helped cre­ate the foun­da­tion and in­fra­struc­ture for a pro­gram of re­search that is now be­ing pur­sued by a com­mu­nity of re­searchers around the world.

Dennis Rod­man, Cat­a­lyst, with Michael Jor­dan, In­no­va­tor.

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