Technology Analysis & S a egic Managemen , 2015
h p://dx.doi.o g/10.1080/09537325.2015.1061121
Cus ome p e e ences e sus manage ial
decision-making in open inno a ion
communi ies: he case o S a bucks
Ma ía del Rocío Ma ínez-To esa∗, F. Rod iguez-Piñe oaand Se gio L. To alb
aFacul ad de Tu ismo y Finanzas, Uni e si y o Se ille, A da. San F ancisco Ja ie s/n, Se ille 41018, Spain;
bE. S. Ingenie os, Uni e si y o Se ille, A da. Camino de los Descub imien os s/n, Se ille 41092, Spain
Cus ome s can pa icipa e in open inno a ion communi ies pos ing inno a ion ideas, which
in u n can ecei e commen s and o es om he es o he communi y, highligh ing use
p e e ences. Howe e , he inal decision abou implemen ing inno a ions co esponds o he
company. This pape is ocused on he cus ome s’ ac i i y in open inno a ion communi ies.
The aim is o iden i y he main opics o cus ome s’ in e es s in o de o compa e hese opics
wi h manage ial decision-making. The esul s ob ained e eal i s ha bo h o es and com-
men s can be used o p edic use p e e ences; and second, ha cus ome s end o p omo e
hose inno a ions by epo ing mo e com o and bene i s. In con as , manage ial decisions
a e mo e ocused on he dis inc i e ea u es associa ed wi h he b and image.
Keywo ds: open inno a ion; inno a ion policies; cus ome communi ies; collec i e in elli-
gence; decision-making
1. In oduc ion
O ganisa ions ha e widely acknowledged he ole o inno a ions in economic g ow h. Techno-
logical de elopmen s ha e o ced highe compe i i eness and sho e inno a ion cycles and, as
a esul , companies inc ease hei e o s in inno a ion ac i i ies (Hekke and Neg o 2009). As
a u he s ep, companies ha e begun o open hei inno a ion p ocesses by inco po a ing bo h
in e nal and ex e nal esou ces, leading o he so-called open inno a ion pa adigm (Ch esb ough
2003). Open Inno a ion is a ecen s a egy ela ed o he managemen o in o ma ion in o ganisa-
ions, and elies on he idea ha po en ial oppo uni ies and ad an ages can be gained ou side he
o mal bounda ies o o ganisa ions (Huizingh 2011; Ma inez-To es 2013; Holzmann, Saile ,
and Galb ai h 2014). This is especially impo an in companies o e ing daily-use p oduc s,
which equi e a cons an ly upda ed ex e nal eedback o measu e i s p og ess and de elopmen .
This pape is ocused on a ep esen a i e example o his kind o o ganisa ion: S a bucks. The
dis inc i e elemen o his company wi h espec o he compe i o s is o o e i s clien s a qual-
i y se ice a all le els. In his line, S a bucks CEO and chai man, Howa d Schul z, de e mines
∗Co esponding au ho . Email: [email p o ec ed]
© 2015 Taylo & F ancis
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2M.d.R. Ma ínez-To es e al.
he necessi y o eno a e he company’s image by e acing he company’s s eps in he same
di ec ion i did om i s o igin: o ien ing i o gi ing pe sonalised a en ion o each cus ome .
S a bucks, like mos companies, is awa e o he impo ance o he new echnologies and he di -
usion o he In e ne as a ool ha can be eached by many cus ome s (Sigala 2012). The open
inno a ion websi e is ac ually a undamen al elemen in he s a egy o es uc u ing. Th ough
he ‘My S a bucks Idea’ websi e, no only use s can pos and sha e ideas wi h he es o use s,
bu commen and o e o he p e iously pos ed ideas. These wo las o ms o pa icipa ion, com-
men ing and o ing, allow use s o exe some p essu e on he o ganisa ion highligh ing hei
p e e ences. Howe e , he o ganisa ion ecei es housands o ideas and mus indi idually asses
each one. Mo eo e , no all he pos ed ideas, e en i hey a e qui e popula , can be implemen ed
by he o ganisa ion since hey can be p ohibi i e due o i s high cos o hey can be in con lic
wi h he image and he mission o he o ganisa ion.
This pape in es iga es cus ome s’ p e e ences and S a bucks decision-making when adop ing
ideas. Mo e speci ically, he pape ies o es o wha ex en he p e e ences o cus ome s a e
in luencing he adop ion o ideas. Al hough his esea ch is es ic ed o he case s udy o My
S a bucks Idea, which is a well-known open inno a ion pla o m, he p oposed me hodology can
be easily ex ended o o he simila consume pla o ms.
The main con ibu ion o his esea ch is he analysis o open inno a ion communi ies om
he double pe spec i e o he cus ome s and he company, which can explain some biases in he
inno a ion policy o companies o o wha ex en cus ome s can in luence u u e inno a ions.
The emainde o he pape is s uc u ed as ollows: he nex sec ion explains he concep o open
inno a ion and i s implemen a ion h ough open inno a ion communi ies. Sec ion 3 p oposes he
hypo heses o his s udy. Sec ion 4 de ails he me hodology o ex ac ing he da a om he ‘My
S a bucks Idea’ websi e and he a iables conside ed. Sec ion 5 shows he esul s ob ained ha
a e nex discussed in Sec ion 6. Finally Sec ion 7 concludes he pape .
2. Li e a u e e iew
The e m open inno a ion was coined by P o . Chesb ough (2003) and e e s o he use o pu po-
si e in lows and ou lows o knowledge o accele a e in e nal inno a ion, and expand he ma ke s
o ex e nal use o inno a ion, espec i ely. This pa adigm assumes ha i ms can use ex e nal
ideas and in e nal ideas, as well as in e nal and ex e nal pa hs o ma ke in o de o ad ance
hei echnology. In con as o he adi ional inno a ion model, his pa adigm also assumes ha
he isks de i ed om opening he inno a ion, such as he access o aluable in o ma ion by
compe i o s o he loss o con ol o e he inno a ion p ocess, can be compensa ed by a iche
numbe o inno a i e ideas.
Se e al classi ica ions ha e been p oposed in he li e a u e abou open inno a ion. To al,
Ma inez-To es, and Di Gangi (2011) dis inguish be ween p oduc and p ocess inno a ions.
Acco ding o he deg ee o openness in inno a ion, open inno a ion s a egies can also be clas-
si ied as ou sou cing, c owdsou cing and online con es s (Hu , Moslein, and Reichwald 2013).
Online con es s a e in ended as compe i ions among use s in o de o each he bes idea/p oposal
and he winne is ewa ded (Ha land and Nienabe 2014). Howe e , he gene a ion o ideas
h ough a websi e can be conside ed as a o m o c owdsou cing, which is no in ended as a com-
pe i ion (Ma inez-To es 2014a). They ha e been popula ised, hanks o he eme gence o Web
2.0 (Bayus 2013). Fi ms such as Mic oso , Dell, IBM, BMW and Nokia inc easingly in es in
i ual communi ies o solici use con ibu ions as pa o hei inno a ion p ocesses. This end
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Open inno a ion decision-making 3
is explained by he inc ease in digi alisa ion and he dec ease in he cos s o communica ion ha
ha e led o an exponen ial g ow h o use inno a ion pla o ms (Mah and Lie ens 2012).
Howe e , some majo un esol ed issues ega ding open inno a ion s ill emain open. One o
hem e e s o he selec ion o he bes s age in which open inno a ion can be mo e e ec i e.
In he case o new se ice de elopmen p ocesses, use in ol emen is o en mo e in ense a he
ini ial s ages o idea gene a ion and sc eening, and again a he la e s ages o es ma ke ing and
comme cialisa ion. Se e al s udies conclude ha i is be e in ol ing cus ome s a he ea lies
s ages as hey can p o ide la e subs an ial educ ions in ime, cos s and co ec ions (Coope
and Kleinschmid 1994;Alam2006). Ano he impo an ques ion e e s o manage ial decision-
making. Gassmann, Enkel, and Chesb ough (2010) a gue ha he in e nal p ocess by which
companies manage open inno a ion is s ill mo e ial and e o han a p o essionally managed
p ocess. Open inno a ion can be seen as a suppo o manage ial decision-making, p oblem
sol ing and oppo uni y exploi ing (Chiu, Liang, and Tu ban 2014). The collec i e e alua ion
sys em o ideas ypically implemen ed by open inno a ion communi ies allows o dis inguish
cus ome p e e ences, and also e eals misma ching be ween cus ome p e e ences and compa-
nies’ decision-making (Ma inez-To es 2014a). This s udy goes u he in his analysis by i s
conside ing he main opics chosen by cus ome s, and hen compa ing hem wi h he inal com-
pany decision-making. In con as o p e ious pape s in his opic ha use a quali a i e app oach
(Sigala 2012), his pape p oposes a quan i a i e app oach. Thousands o ideas mus be collec ed
and analysed o ob ain he ca ego ies o opics hey belong o. Al hough da a a e publicly a ail-
able, no all he in o ma ion con ained in web pages is use ul and meaning ul, and da a ha e
o be au oma ically ex ac ed o each one o he housands o pos ed inno a ions. These da a
ex ac ion me hods can be amed wi hin he Big Da a me hodologies, which ep esen an eme -
gen end wi hin social sciences (A enas-Ma quez, Ma inez-To es, and To al 2014; Chang,
Kau man, and Kwon 2014; Ma inez-To es 2014b).
3. Hypo heses
The wo p ima y o ms o pa icipa ion in online communi ies consis o commen ing and o ing.
P e ious wo ks suppo ha bo h o ms o pa icipa ion end o be co ela ed. Fo ins ance, his
is he case o Dell Ideas S o m, he open inno a ion communi y om Dell, whe e commen s,
p omo ions (posi i e sco es) and demo ions (nega i e sco es) ha e been p o ed o be co ela ed
(Ma inez-To es 2014a). Ob iously, i is cogni i ely mo e complex pos ing a commen han
pos ing a sco e, whe e no jus i ica ion is equi ed. Howe e , Bajic and Lyons (2011) p oposed
ha collabo a i e websi es allow use s o ind sugges ions simila o hei own, hence esul ing
in mo e o es and commen s pe sugges ion. Bo h o es and commen s ha e also been used
in open inno a ion con es s as a measu e o de e mine use s’ design p e e ences and p e-selec
he mos p omising designs o he ju y (Fülle e al. 2010). Dahlande and Piezunka (2014)
ob ained a posi i e ela ionship be ween he numbe o sugges ions om ex e nal con ibu o s
and p oac i e a en ion. O he au ho s ha e s udied how he cogni i e and a ec i e eelings
in luence he e alua ion o con ibu ions. Reade s o a message will espond o he asse i eness
in used in o he message as well as o he message i sel , and he manne in which he message is
p esen ed. Consequen ly, when messages communica e nega i e eeling, hey a e likely o a ac
nega i e eac ions om he communi y in e ms o o es and commen s (Kim and Mi anda 2011).
Al hough o es and commen s a e publicly a ailable, he e a e only ew examples o s udies
ha ha e explo ed cus ome -gene a ed con en in new se ice de elopmen . Li e al. (2010)
p oposed a news ecommenda ion sys em based on use commen s unde he assump ion ha
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opic e olu ion in social media can be e lec ed by he commen s. The e o e, o es and commen s
can be used as he a iables o collec use p e e ences. Alam (2002) a gues ha a la ge numbe
o powe ul new se ice ideas need o be gene a ed wi h use con ac s and in e ac ions, and
cus ome pa icipa ion is impo an o designing dis inguishable and unique se ices. Howe e ,
cus ome s a e able o sugges new se ices which p o ide hem wi h alues and solu ions o hei
daily p oblems (Sigala 2012). I is widely held ha se ice quali y is pe cei ed by cus ome s
h ough a compa ison be ween se ice- ela ed expec a ions and expe iences (G ön oos 2000).
These expe iences a e always ela i e o wha cus ome s conside easonable based on hei
p io expe iences, se ice p o ide ’s communica ions, and hei own needs and aspi a ions in a
pa icula si ua ion (Kuusis o 2008). Acco ding o Va go and Lusch’s (2004), se ices p o ide
cus ome s wi h alue only when hey a e used. Cus ome alue is hence ied o a cus ome ’s
meaning a ached o he expe ience wi h a se ice. This implies ha mos cus ome s’ p oposed
inno a ions a e biased by hei p e ious expe ience, and hey a e mainly guided by hei own
needs. Acco ding o his, we p opose he ollowing hypo hesis:
H1: Use s’ p e e ences end o ocus on he co e ac i i y o he company and on hose ideas ha
epo mo e com o and bene i s.
C owdsou cing has been s a ed as a sou ce o suppo o manage ial decision-making (B ab-
ham 2013), and open inno a ion is ac ually one o m o c owdsou cing (Chiu, Liang, and Tu ban
2014). Howe e , human biases can a ec he idea gene a ion p ocess (Bonabeau 2009). Fo
ins ance and in he case o he hospi ali y se ices, social in e e ence o he consume s’ desi e
o inding a solu ion i ing hei speci ic needs can lead o ideas a away om he company’s
expec a ions (Sigala 2012). In some cases, he company’s expec a ions a e also d i ed by he
esis ance o change, o ins ance, selling wha we make a he han esponding o cus ome s’
equi emen s. Online ma ke ing manage s o en base hei decisions on simple heu is ics, com-
bined wi h pe sonal expe ise. Pe sonal p e e ences a e s ill p e alen despi e he olume o da a
a ailable (Ande l e al. 2013). In he case o hospi ali y companies, he expe ience s a es ha
hey can inc ease hei ma ke sha e and g ow h a es by inc easing hei b and loyal cus ome s
(Tepeci 1999). This is because he hospi ali y business is a ma u e indus y whe e i is cheape
o se e cu en cus ome s a he han acqui ing new cus ome s h ough ad e ising, p omo ion
and s a -up ope a ing expenses. The e a e se e al s udies ha show he posi i e ela ionship
be ween he b and image and cus ome s’ pe cei ed alue and pu chase beha iou (C e u and
B odie 2007;Wu2008). Thus, we p opose he ollowing hypo hesis:
H2: Manage ial decision-making end o ocus on he dis inc i e ea u es associa ed wi h he b and
image.
4. Me hodology
This s udy ollows a g ounded heo y app oach, which is a gene al me hodology o de eloping
heo y ha is g ounded in da a sys ema ically ga he ed and analysed (S auss and Co bin 1998;
To al, Ma ínez-To es, and Ba e o 2009). This me hodology has been used as a ma ke ing
esea ch me hodology o s udying cus ome in ol emen in new se ice de elopmen s (Sigala
2012) o o analysing he publicly a ailable in o ma ion in online communi ies (Kozine s 2002).
The i s s ep o apply his me hodology consis s o inding an online communi y app op ia e o
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Open inno a ion decision-making 5
Table 1. Ca ego ies and subca ego ies o pos ed ideas.
P oduc ideas Co ee & Esp esso D inks
F appuccino & Be e ages
Tea & o he d inks
Food
Me chandise & Music
S a bucks Ca d
New Technology
O he P oduc Ideas
Expe ience ideas O de ing o Paymen & Pick-Up
A mosphe e & Loca ions
O he Expe ience Ideas
In ol emen ideas Building Communi y
Social Responsibili y
O he In ol emen Ideas
Ou side USA
he esea ch aims. This is he case o My S a bucks Idea, which is an open inno a ion web-
si e whe e use s can sco e and commen inno a ions, and whe e he company makes public
and isible hose ideas inally adop ed. The second s ep consis s o da a collec ion. S a bucks’
open inno a ion websi e iden i ies membe s’ con ibu ions as ideas. When pos ing an idea, eg-
is e ed use s ha e o choose one o he 15 subca ego ies ha espond o h ee basic aspec s o he
company: p oduc , expe ience and in ol emen ideas (Table 1).
Once an idea is submi ed and sha ed, communi y use s can o e and commen pos ed ideas.
Commen ing an idea means ha he use s a ach commen s below he pos ed idea in he o m
o a h ead o discussion. In gene al, commen s can suppo , c i icise o e ine he idea sha ed
and, as a esul , a deba e among use s can eme ge h ough hese commen s. E en he o iginal
au ho o he idea can pa icipa e answe ing some ques ions. Vo ing an idea consis s o adding o
sub ac ing 10 poin s o i s cu en sco e. As long as ideas ecei e mo e o es, hey a e p omo ed
o op posi ions in e ms o popula i y wi hin he web. The e is a sepa a e ca ego y, called Ideas
in Ac ion, which shows hose ideas ha ei he ha e al eady been launched (adop ed by he com-
pany) o ha a e cu en ly coming soon. The e o e, his ca ego y includes hose ideas ha ha e
been conside ed by S a bucks o hei implemen a ion.
Th ee a iables ha e been conside ed in his s udy: o es, which e e s o he cu en sco e
o each idea; commen s, de ined as he numbe o ecei ed commen s by each idea sha ed; and
size, which e e s o he numbe o cha ac e s o he ideas sha ed. The h ee a iables ha e been
ex ac ed using ou own c awle . A c awle is a compu e p og am ha ollows he hype link
s uc u e o he web. In his case, he c awle is used o collec da a om a speci ic websi e
(You ie e al. 2012). As he sou ce code o each websi e has a di e en s uc u e and s yle, he e
is no s anda d way o b owsing h ough hem. As a consequence, i is necessa y o p og am a
hand-made c awle . In his pape , he c awle was p og ammed in R, which is a ee so wa e
en i onmen o s a is ical compu ing. The base package o R con ains he unc ion eadLines(),
which eads da a om a URL. This unc ion was used o access he sha ed e iews. Howe e ,
webpages a e o ma ed in HTML code, and accessed da a con ain bo h he webpage con en and
he HTML ags. The e o e, i is necessa y o pa se he HTML ile using he h mlPa se() unc ion.
This unc ion gene a es an R s uc u e ep esen ing he HTML ee. Once online webpages a e
a ailable as an R s uc u e, meaning ul da a can be easily iden i ied using egula exp essions
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6M.d.R. Ma ínez-To es e al.
ha a e also suppo ed in R, o ins ance, in packages such as XML. As a esul , a o al o 99,528
ideas dis ibu ed o e he 15 ca ego ies o Table 1we e collec ed. Addi ionally, he ca ego y o
Ideas in Ac ion was also c awled. In his case, he numbe o ideas is 897. Fo each one o hem,
he numbe o commen s ecei ed and i s size we e cap u ed ( he numbe o o es is no a ailable
in his case), as well as he ca ego ies unde which hese ideas we e classi ied by S a bucks. Once
da a a e collec ed, he pape i s analyses he ac i i y o use s in open inno a ion communi ies in
he di e en ca ego ies whe e hey can pos inno a ions. The esul s ob ained a e hen compa ed
wi h hose ideas ac ually implemen ed by he company.
5. Resul s
A co ela ion analysis among he h ee ex ac ed a iables o he 15 ca ego ies o ideas has been
pe o med. Resul s ob ained in Table 2show ha pa icipa ion h ough o ing and commen ing is
posi i ely co ela ed, while he size o ideas sha ed is no co ela ed wi h he o he wo a iables.
This esul sugges s ha hose ideas ha ecei e a highe numbe o o es a e also gene a ing a
deba e a ound hem. The e o e, bo h o es and commen s can be conside ed ele an in o ma ion
o iden i y use s’ p e e ences. Howe e , he size o ideas is no ele an o iden i ying good
ideas.
The dis ibu ion o he 3 a iables conside ed o e he 15 ca ego ies o ideas has been i s
analysed. Figu e 1illus a es he mean alue and con idence in e als o he a iable o es in
each o he 15 ca ego ies o ideas. This igu e highligh s ha he ca ego ies S a bucks Ca ds,
O de ing, Paymen & Pick up and Co ee & Esp esso D inks a e he h ee ones ha ecei e mo e
o es, while New Technology is clea ly he wo s e alua ed ca ego y by use s. These esul s
sugges ha S a bucks cus ome s a e mo e biased owa ds he co e ac i i y o S a bucks, which
a e basically co ee and he o de ing p ocesses. S a bucks Ca d e e s o he loyal y p og am o
he company and i s associa ed ad an ages. Taking in o accoun ha he S a bucks Ca d is he
ewa ding sys em o he loyal y o use s and he ac ha he majo i y o he ideas p o ided by he
communi y in his ca ego y demands ex ending i s owne ’s bene i s, he e is an ob ious endency
among My S a bucks Idea membe s o suppo and o e hese ideas, as s a ed in hypo hesis H1.
Figu e 2de ails he mean alue and con idence in e als o he a iable size. In his case,
h ee ca ego ies (F apuccino,New Technology and Ou side USA) show he highes alues. The
es o hem a e mo e o less simila in size. This esul can be explained because hese pa icula
ca ego ies ha e a wide scope, and consequen ly ideas need o be mo e p ecise and equi e longe
explana ions.
Finally, Figu e 3shows he mean alue and he con idence in e als o he a iable commen s.
The mos popula ca ego ies in his case a e Co ee & Esp esso D inks,F apuccino and New
Technology. I is in e es ing o no ice ha Co ee & Esp esso D inks occupies a ele an posi ion
in bo h o es and commen s. This could be because co ee is he main p oduc o S a bucks, and
Table 2. Co ela ion among a iables.
Vo es Size Commen s
Vo es 1000 −.027** .487**
Size −.027** 1000 .101**
Commen s .487** .101** 1000
**Co ela ion is signi ican a he .01 le el ( wo- ailed).
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Open inno a ion decision-making 7
Figu e 1. Mean alue and con idence in e als o Vo es.
people end o associa e he image b and wi h co ee. The e o e, his is pe haps he main ca ego y
in which use s a e mo e in ol ed in. I is also in e es ing o see ha New Technology is in gene al
he wo s e alua ed/sco ed ca ego y, al hough i a ouses an impo an deba e among use s. This
poin can be explained by he speci ici y o con ibu ions ela ed o his ca ego y. In con as , he
deba e in he ca ego ies Ou side USA,Food and Me chandise & Music is no iceably lowe .
A K uskal–Wallis es has been pe o med o es he equali y o means o he h ee a iables
conside ed in each o he 15 ca ego ies o ideas. The K uskal–Wallis es is a nonpa ame ic
e sion o one-way analysis o a iance. The assump ion behind his es is ha he measu emen s
come om a con inuous dis ibu ion, bu no necessa ily a no mal dis ibu ion. The es is based
on an analysis o a iance using he anks o he da a alues, no he da a alues hemsel es. The
low p- alue in Table 3 o each a iable sugges s ha he null hypo hesis can be ejec ed, so i
can be concluded ha he ob ained mean alues in Figu es 1–3a e signi ican ly di e en .
Any o he p e ious ideas belonging o he 15 ca ego ies ha e he oppo uni y o becoming
a eali y. I he con ibu ion is iable and i is conside ed in e es ing by S a buck’s quali y eam
suppo , i can each he Idea in Ac ion s a us. This ca ego y ac ually ep esen s he manage ial
decision-making, as ideas each his s a us a e being e alua ed by he inno a ion depa men o
some expe s o he company. Al hough he inal decision abou ideas can be in luenced by he
communi y e alua ion, i is ac ually an independen and au onomous decision o he company.
Figu e 4shows he dis ibu ion o he numbe o Ideas in Ac ion pe ca ego y o Ideas. Co ee
& Esp esso D inks, wi h 190 ideas in Ac ion, is clea ly he ca ego y in which mo e ideas ha e
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8M.d.R. Ma ínez-To es e al.
Figu e 2. Mean alue and con idence in e als o Size.
been selec ed by S a bucks. Again, his esul is in line wi h he main p oduc o e ed by he
company, which is also he mos closely associa ed wi h he b and image. The second and hi d
places co espond o O he Expe ience Ideas and Social Responsibili y.
O he Expe ience Ideas ca ego y p o ides space o hose commen s no ins inc i ely classi i-
able in he o he ca ego ies, such as pa ne s (wo ke s, ba is as), o he ypes o ewa ding loyal y
o deco a ion changes. This ca ego y includes he eeling o use s abou S a bucks, and his is an
issue p io i ised by he company, which conside s he expe ience o aking a co ee in S a bucks
as a dis inc i e expe ience. The same can be said abou social esponsibili y. S a bucks aims o
be an en i onmen al- iendly g een b and, conce ned abou social p oblems bo h in he whole
wo ld and in e e y single neighbou hood. The h ee mos adop ed ca ego ies a e hose mo e
closely ela ed o he b and image o he company, as hypo hesised in H2.
6. Discussion and implica ions
Al hough he e a e se e al me hods o impo ing ex e nal ideas h ough he scheme o open
inno a ion, his s udy is speci ically ocused on open inno a ion web communi ies, which ha e
gained popula i y wi h he eme gence o use -gene a ed con en (Ma inez-To es 2015).
Resul s ob ained show he e is a gap be ween cus ome p e e ences and manage ial decision-
making in open inno a ion communi ies. This gap can be explained because companies in ol ed
in open inno a ion a e no s ill comple ely con iden abou he open inno a ion esul s. How-
e e , his is p ecisely con a y o wha he li e a u e has es ablished in he sense ha use s a e
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Open inno a ion decision-making 9
Figu e 3. Mean alue and con idence in e als o Commen .
Table 3. K uskal–Wallis es .
Vo es Size Commen s
Chi-squa e 4713.32 6046.31 2507.39
d 14 14 14
P.000 .000 .000
be e in iden i ying use ul ideas because hese a e no usually easy o implemen by i ms (Poe z
and Sch eie 2012). Al hough cus ome p e e ences unde he scheme o open inno a ion can
o e come some esis ance o change, he e is s ill some biases in manage ial decision-making,
as i can be obse ed in he esul s ob ained. As a esul , companies can miss some impo an
dis up i e inno a ions ha can be compe i i e ad an ages o he u u e.
In o de o o e come hese p oblems, i is impo an o companies pe o ming open inno a-
ion schemes o in oduce some moni o ing ac i i ies abou he decision-making p ocesses, able
o de ec some biases. A his poin , his pape o e s a me hodological con ibu ion by using
some me hods o da a collec ion in social media. The main ad an age o he p oposed me hod
is ha i can wo k wi h he whole da a se ins ead o a sample, as in o ma ion abou all p e ious
pos ed inno a ion can be easily accessed using compu e -based ools. The compa ison be ween
cus ome and company p e e ences can de ec a eas o inno a ions no conside ed p e iously.
Mo eo e , cus ome p e e ences can also be analysed h ough he di e en ca ego ies in which
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