Zas empowski, Maciej
A icle
Small bu inno a i e: Un eiling he impac o mic o-
en ep eneu s' pe sonali y ai s on a spec um o
inno a ions
Jou nal o Inno a ion & Knowledge (JIK)
P o ided in Coope a ion wi h:
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Sugges ed Ci a ion: Zas empowski, Maciej (2024) : Small bu inno a i e: Un eiling he impac o
mic o-en ep eneu s' pe sonali y ai s on a spec um o inno a ions, Jou nal o Inno a ion &
Knowledge (JIK), ISSN 2444-569X, Else ie , Ams e dam, Vol. 9, Iss. 4, pp. 1-13,
h ps://doi.o g/10.1016/j.jik.2024.100552
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Small bu inno a i e: Un eiling he impac o mic o-en ep eneu s’
pe sonali y ai s on a spec um o inno a ions
Maciej Zas empowski
Facul y o Economic Sciences and Managemen , Nicolaus Cope nicus Uni e si y, Gaga ina 13A S ee , 87-100 To u
n, Poland
ARTICLE INFO
A icle His o y:
Recei ed 24 Janua y 2024
Accep ed 22 Augus 2024
A ailable online 2 Sep embe 2024
ABSTRACT
This a icle in es iga es whe he a mic o-en ep eneu ’s pe sonali y influences hei inno a i eness. Using
he Big Fi e heo y and a b oad defini ion o inno a ion om he 4 h e sion o he Oslo Manual ( wo ypes
and nine ca ego ies o inno a ions in o al), he s udy analyses da a om 1,848 Polish mic o-en ep eneu s.
Since inno a i eness is a complex p ocess wi h se e al in e dependencies, and p e ious esea ch shows ha
in oducing one ype o inno a ion is no independen o in oducing o he ypes, Mul i a ia e P obi (MVP)
eg ession was used o es ima e he models. The esul s allow wo conclusions o be d awn. Fi s ly, in he
case o p oduc inno a ions implemen ed by mic o-en ep eneu s, h ee pe sonali y ai s ha e a posi i e
impac . These a e Openness o expe ience, Conscien iousness and Ex o e sion. Secondly, in he case o busi-
ness p ocess inno a ions, all 7 ypes a e posi i ely influenced by wo pe sonali y ai s, namely Openness o
expe ience and Ex o e sion.
© 2024 The Au ho . Published by Else ie España, S.L.U. on behal o Jou nal o Inno a ion & Knowledge. This
is an open access a icle unde he CC BY-NC-ND license
(h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/)
Keywo ds:
Pe sonali y
Big Fi e
Mic o-en ep eneu s
Inno a i eness
P oduc inno a ion
Business p ocess inno a ion
Mul i a ia e p obi model
JEL classifica ion:
D91
L26
M19
O15
O31
In oduc ion
Al hough Schumpe e indica ed small, en ep eneu ial companies as
cons i u ing a ce ain sou ce o c ea i e des uc ion, and hus o inno a-
i e p ocesses in he economy (Schumpe e , 1912), he inno a i eness
o mic o-en ep eneu s is s ill on he ma gins o esea ch in o inno a-
ion (Mah o e al., 2018;Zas empowski, 2022). Despi e Schumache ’s
(1973) sugges ion ha "small is beau i ul" and Taleb’s (2012) opinion
indica ing ha small is less agile (o an i agile), companies wi h ewe
han en employees a e s ill o e looked in mos esea ch on inno a-
i eness (among o he s in he Eu os a Communi y Inno a ion Su ey
conduc ed in e e y Eu opean Union membe s a e). Rope and Hewi -
Dundas (2017, p. 559) e en sugges ha “(...) mic o-en e p ises a e a
neglec ed pa o Schumpe e ’sc ea i ea my”.
Based on esou ce heo y (Ba ney, 1991;Teece e al., 1997), he
li e a u e highligh s ha inno a i eness is s ongly ela ed o inno a-
ion capabili y (Ma ínez-Rom
an e al., 2011;Mendoza-Sil a, 2020,
2021). In he case o mic o-en e p ises, i is wo h ocusing on he
inno a ion capabili y o hei owne s, i.e. mic o-en ep eneu s. Thei
inno a i eness is, in u n, pe cei ed as one o he componen s o
human cha ac e and can be associa ed wi h mo e gene al pe sonal-
i y ai s (Ma ca i e al., 2008).
Pe sonali y is he unique way o hinking and eeling ha pe sis s
h oughou a pe son’s li e, and should no be con used wi h cha ac e
( he mo al and e hical alue judgmen s abou a pe son’s beha iou )
o empe amen (a pe son’s cons an , inna e cha ac e is ics, such as
impe uousness o adap abili y) (Kagan, 2010). Based on psychology,
pe sonali y heo y has e ol ed along ou adi ional app oaches: he
psychodynamic (Adle , 1954;F eud, 1904;Jung, 1933), he beha iou-
al (Bandu a, 1989;Dolla d & Mille , 1950;Ro e , 1990), he human-
is ic (Maslow, 1987;Roge s, 1961) and he ai -based (Allpo &
Odbe , 1936;Ca ell, 1950;McC ae & Cos a, 1997). In he con ex o
pe sonali y desc ip ion and he possibili y o p edic ing beha iou
based upon i (i.e., inno a i eness), he ai -based heo y seems o
be pa icula ly in e es ing. In ea ing a ai as a cohe en , pe ma-
nen way o hinking, eeling and beha ing, his heo y a emp s o
desc ibe pe sonali y based on indi idual human ai s.
Howe e , despi e he impo ance o pe sonali y in p edic ing pos-
sible inno a i e beha iou (Ahmed, 1998;Ali, 2019), only a limi ed
numbe o s udies ha e examined he ela ionship be ween
E-mail add ess: [email p o ec ed]
h ps://doi.o g/10.1016/j.jik.2024.100552
2444-569X/© 2024 The Au ho . Published by Else ie España, S.L.U. on behal o Jou nal o Inno a ion & Knowledge. This is an open access a icle unde he CC BY-NC-ND license
(h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/)
Jou nal o Inno a ion & Knowledge 9 (2024) 100552
Jou nal o Inno a ion
&Knowledge
h ps://www.jou nals.else ie .com/jou nal-o -inno a ion-and-knowledge
pe sonali y and inno a ion (Ma ca i e al., 2008;Shane & Nicolaou,
2015;S ock e al., 2016).
The e o e, o add ess he call o wo k on he e ec o pe sonali y
ai s on inno a ion capabili y (Mendoza-Sil a, 2020), and o seek
mo e p ecise pe sonali y associa ions wi h basic inno a ion ac i i ies
(S ock e al., 2016), his a icle aims o examine he ole o pe sonali y
in he implemen a ion o inno a ions by mic o-en ep eneu s. In
pa icula , pe cei ing pe sonali y in e ms o he fi e p ima y ac o s
o he widely accep ed pe sonali y heo y (Goldbe g, 1990;McC ae &
Cos a, 1987), we pose he ollowing esea ch ques ion: Does a mic o-
en ep eneu ’s pe sonali y a ec hei inno a i eness?
This pape is o ganised as ollows. As pa o he li e a u e e iew,
he fi s sec ion explo es he Fi e-Fac o Model o Pe sonali y, mic o-
en ep eneu s’inno a i eness and he links be ween pe sonali y
ai s and inno a i eness. The second and hi d sec ions p esen ,
espec i ely, he esea ch me hodology and he esul s, while he
final sec ion discusses he esul s and hei p ac ical implica ions.
Li e a u e e iew
The fi e- ac o model o pe sonali y
Resea ch on pe sonali y ai s da es back o he wo k o Allpo
and Odbe (1936), who indica ed 200 possible ai s. La e esea ch,
howe e , led o he de elopmen o a mo e concise app oach o he
desc ip ion o pe sonali y. Ca ell’s (1950) esea ch seems undamen-
al he e, di iding pe sonali y ai s in o wo basic ypes: su ace and
sou ce. The o me a e supe ficial beha iou al endencies ha exis
“on he su ace”and hus can be obse ed di ec ly. Meanwhile, he
la e ep esen deepe psychological s uc u es ha unde lie he
su ace ai s and explain hei co ela ions (Ma z e al., 2016). Ca -
ell’s (1973) esea ch led o he iden ifica ion o 16 such essen ial
sou ce ai s. Howe e , 16 ai s a e s ill oo many when i comes o
discussing and desc ibing someone’s pe sonali y. The e o e, u he
esea ch aimed o limi he numbe o dimensions o pe sonali y
ai s o alues ha a e easie o desc ibe. Se e al esea ch g oups
ob ained fi e simila dimensions o he ai s (Bo win & Buss, 1989;
Goldbe g, 1990;Jang e al., 1998;McC ae & Cos a, 1987). These make
up he so-called Fi e Fac o Model o he Big Fi e. This assumes he
exis ence o fi e independen pe sonali y ai s, including openness
o expe ience, conscien iousness, ex a e sion, ag eeableness and
neu o icism (McC ae & Cos a, 1999). A b ie desc ip ion o he Big
Fi e ai s is p esen ed below.
Openness o expe ience is desc ibed as he eadiness o y new
hings and openness o new expe iences (S ock e al., 2016;Zhao &
Seibe , 2006). In o he wo ds, openness dis inguishes imagina i e
and c ea i e people om hose who a e down- o-ea h and con en-
ional (Ma z e al., 2016). People who sco e high on openness o expe-
ience a e usually desc ibed as cu ious, seeking in ellec ual
challenges, endowed wi h an a is ic soul, c ea i e and non-con o m-
is (Faullan e al., 2016;Feis , 1998;McC ae & Cos a, 1997). They
ha e ich imagina ions and o en engage in an asies. Such people
also enjoy dayd eaming and hinking abou al e na i e eali ies
(Schnack e al., 2021). People who a e open o expe ience ypically
app ecia e beau y and a , and a e deeply in e es ed in music, li e a-
u e, isual a and o he o ms o a is ic exp ession (McC ae & Cos a,
1997;Schnack e al., 2021). Con e sely, low sco e s may be cha ac-
e ised as adi ionalis s and conse a i es who will likely p e e he
amilia o he unusual (McC ae & Cos a, 1987).
Conscien iousness e e s o a pe son’s o ganisa ion, mo i a ion,
pe se e ance and diligence in achie ing a goal (S ock e al., 2016).
The highe he le el o conscien iousness, he mo e o en we a e
dealing wi h us wo hy, ambi ious and pedan ic people (McC ae &
Cos a, 1990) who p e e planned beha iou s o e spon aneous ones
(Ba ick & Moun , 1991;Feis , 1998). Conscien ious people a e well-
o ganised and sys ema ic in planning hei ac i i ies. They a e
de e mined and pe sis en in pu suing hei goals (Saa ci & O aci,
2020). They pay g ea a en ion o de ails and y o pe o m hei
du ies as bes as possible. They a e also eliable and esponsible
(Abbas e al., 2018). I is wo h emphasising ha such people usually
ha e a s ong wo k e hic and a e willing o wo k ha d o achie e hei
goals. Conscien iousness also goes hand in hand wi h con olling
impulses and ocusing on long- e m goals (Faullan e al., 2016;Feis ,
1998;McC ae & Cos a, 1997). Indi iduals wi h low conscien iousness
ypically ha e di ficul ies wi h o ganisa ion and may be less eliable,
mo e impulsi e and less pe sis en in pu suing hei goals. They may
also be less inclined o wo k ha d and mo e p one o a oiding
esponsibili ies (McC ae & Cos a, 1990).
Ex o e sion (and i s opposi e, in o e sion) illus a es he le el o
subjec i e p edisposi ion (o a e sion) o social in e ac ion and ac i -
i y (Ma ca i e al., 2008). Ex o e s a e alka i e, op imis ic, sociable
and emo ional people. People who show high le els o ex o e sion
also end o be ac i e and ull o ene gy. They like o engage in a ious
ac i i ies and o en lead busy li es. They a e also usually sel -confi-
den and willing o exp ess hei opinions. They a e no a aid o lead
and make decisions. They a e asse i e (Jackson, 2014). Ex o e s
also seek ad en u e, a e open o new challenges, and can easily make
new iends and build ela ionships (Schnack e al., 2021). Thei
opposi e - in o e s - a e a he wi hd awn, "blend in o he back-
g ound", and like soli ude (McC ae & Cos a, 1990). In o e s o en
p e e quie e and less s imula ing en i onmen s. They may eel
d ained a e p olonged social in e ac ions, p e e o wo k indepen-
den ly, and usually alue ime spen alone o wi h a close-kni g oup
o iends (McC ae & Cos a, 1987).
Ag eeableness e e s o a pe son’s basic emo ional s yle (McC ae &
Cos a, 1990) and desc ibes hei in e pe sonal o ien a ion, including
he endency o p e e posi i e in e pe sonal ela ionships and coop-
e a ion (S ock e al., 2016;Zhao & Seibe , 2006). A high le el o
ag eeableness sugges s a ole an , iendly, poli e, well-disposed,
us ing and help ul pe son (Cholle e al., 2016;McC ae & Cos a,
1990). They a e also empa he ic people, i.e. sensi i e o he needs
and eelings o o he people, able o empa hise wi h hei si ua ion
and show hem unde s anding and compassion. They also alue ha -
mony in in e pe sonal ela ionships and a e willing o help o he s -
hey p e e coope a ion o e compe i ion (McC ae & Cos a, 1987).
Ag eeable people a e ela i ely modes and do no like o b ag, app e-
cia ing simplici y and au hen ici y (Ma ca i e al., 2008). They will
also y o a oid conflic s and dispu es, s i ing o sol e p oblems
peace ully and h ough comp omise (McC ae & Cos a, 1990). A he
same ime, a low le el o ag eeableness indica es a pe son who is
g umpy, sel -cen ed, suspicious, uncoope a i e, i i able, agg essi e
and hos ile (Feis , 1998;McC ae & Cos a, 1990).
Finally, neu o icism e e s o emo ional s abili y (o ins abili y).
People who a e wo ied, anxious, empe amen al and cap icious ge
a high sco e in his a ea. Neu o ic people also end o eel sad and
dep essed. They may be p one o pessimis ic hinking and low sel -
es eem. They can also o en be easily i i a ed and quick o ange
(McC ae & Cos a, 1990). Mino di ficul ies o obs acles can p o oke
s ong emo ional eac ions in hem. Neu o icism is also a ea u e o
indi iduals who end o be s ic wi h hemsel es, sel -c i ical and
o e -analyse hei mis akes and impe ec ions (S ock e al., 2016;
Zhao & Seibe , 2006). Indi iduals wi h low le els o neu o icism, o
hose who a e mo e emo ionally s able, gene ally handle s ess be -
e , ha e a mo e posi i e ou look on li e, and less equen ly expe i-
ence in ense nega i e emo ions (Schnack e al., 2021). They a e mo e
balanced, confiden , elaxed and calm, and ob ain a low sco e in his
a ea (Dille e al., 2020;McC ae & Cos a, 1990).
I is wo h emphasising ha despi e he deba e on he adequacy
o he Big Fi e model (Bo man, 2004;Ka imi e al., 2022;Pe in,
1994), he li e a u e p o ides se e al con incing ins ances o p oo o
i s obus ness (Conley, 1985;McC ae & Cos a, 1987). P e ious
esea ch has no only shown ha as pe sonali y ai s, he Big Fi e
M. Zas empowski Jou nal o Inno a ion & Knowledge 9 (2024) 100552
2
a e consis en ac oss cul u es (Ho s ede & McC ae, 2004), bu ha
hey also seem o be he edi a y (Jang e al., 1998) and a e s able in
ime (Wagne e al., 2019).
Mic o-en ep eneu s’inno a i eness
Al hough inno a ions a e cu en ly pe cei ed as an impo an ac-
o in inc easing compe i i eness and achie ing a compe i i e ad an-
age (B a ianu e al., 2023), and Fahe y and S ephens’s (2016)
esea ch has shown ha he inno a i eness o mic o-en ep eneu s
is mo e eali y han fic ion, he e a e s ill only a ew s udies ha
add ess he issue o mic o-en ep eneu s’inno a i eness. Plo niko a
e al. (2016), desc ibing he esul s o esea ch on sel -employed peo-
ple in Andalusia, indica e he exis ence o a ela ionship be ween
hei educa ion (seconda y and highe p o essional educa ion and
business educa ion), hei p e ious expe ience as a wo ke , and hei
p ocess inno a ion. Simila conclusions, only in ela ion o p oduc
inno a ions, a e sugges ed by Rome o and Ma ínez-Rom
an (2012)
in a Spanish s udy o he sel -employed. Rope and Hewi -Dundas
(2017) also d aw a en ion o he ole o educa ion. Based on a No h-
e n I eland su ey o inno a ion among mic o-en e p ises, hei
esea ch showed a link be ween a STEM backg ound and new- o-
he-ma ke inno a ion. Simila ly, s udies o mic o-en e p ises in
Indonesia sugges a ela ionship be ween he owne s’le el o educa-
ion and he digi al echnology inno a i eness o he company
(T inug oho e al., 2022).
Ano he indica ed ac o influencing mic o-en e p ises’inno a-
i eness is hei en ep eneu ial capi al (including, among o he s,
isk- aking, cou age and ini ia i e). C espo e al. (2021) sugges he
exis ence o a ela ionship be ween en ep eneu ial capi al and
abso p i e capaci y, which in u n was ound o be an an eceden o
inno a ion in B azilian mic o-en e p ises. In he opinion o Raghu-
anshi e al. (2019), componen s o he inno a i e capaci y o Indian
mic o-en e p ises a e also elemen s o en ep eneu ial capi al. The
key elemen s in he au ho s’mic o-en e p ise inno a ion capabili y
measu emen scale a e esou ces, isk- aking, ne wo king and
in ol emen . Also, Wahid e al. (2017) showed he ole o ne wo king
in he inno a i eness o Malaysian mic o-en e p ises. In e es ing
conclusions ega ding he inno a i eness o mic o-en e p ises a e
also shown in he esea ch o Baumann and K i ikos (2016), sugges -
ing ha in he case o mic o-en e p ises in es ing in R&D, he in en-
si y o inno a ion is g ea e he smalle he companies a e. In u n,
Aud e sch e al. (2020) show ha he ela ionship be ween R&D,
inno a i e ou pu and p oduc i i y di e s depending on whe he he
mic o-en e p ise deals wi h p oduc ion o ope a es in knowledge-
in ensi e se ices. Some s udies also sugges ha in he case o
mic o-en e p ises, hei inno a i eness is s ongly ela ed o he ole
o cul u al componen s in inno a ion p ocesses ( an Oos om &
Fe n
andez-Esquinas, 2017) and owne s’mo i a ion (Kozubíko
a&
Zoubko
a, 2016).
I is also wo h adding ha inno a i eness is a comp ehensi e
concep defined and measu ed in a ious ways. Fo example, Dziallas
and Blind’s (2019) analysis o scien ific publica ions on inno a ion
indica o s published in he yea s 1980−2015 iden ifies 82 unique
indica o s o assessing inno a ion. In u n, Mendoza-Sil a (2020),
also as a esul o a sys ema ic e iew o he li e a u e, indica es
se en inpu and eigh ou pu measu es o inno a i e capabili ies.
The e is also conside able defini ional di e si y in he field o inno a-
ion. Howe e , his p o es he cons an e olu ion o his concep .
The e o e, we decided o ocus on he way o unde s anding inno a-
ion p oposed in he las - ou h - edi ion o he OSLO Manual - a
me hodological guide on how o measu e inno a ion, widely used by
s a is ical o fices in OECD and EU coun ies. Acco ding o his, we dis-
inguish wo basic ypes o inno a ions: p oduc and business
p ocesses (OECD & Eu os a , 2018).
In his con ex , i is wo h asking whe he a mic o-en ep eneu ’s
pe sonali y a ec s hei inno a i eness.
Links be ween pe sonali y ai s and inno a i eness
P e ious esea ch conduc ed in a ious disciplines has shown ha
s able pe sonali y ai s can be used o iden i y people who beha e
c ea i ely and inno a i ely (Ahmed, 1998;Ali, 2019). Ne e heless,
he findings a e a om conclusi e (Ji asek & Sudzina, 2020), and
Mendoza-Sil a (2020) has called o esea ch in o he e ec o pe -
sonali y ai s on inno a ion capabili y as he e s ill emains a signifi-
can esea ch gap in his ega d. In line wi h hese ecommenda ions,
he possible impac o each Big Fi e pe sonali y ai on inno a-
i eness is p esen ed below.
Among he Big Fi e pe sonali y ai s, openness o expe ience exe s
he mos significan and ho oughly documen ed impac on inno a-
i eness. Openness o expe ience is a pe sonali y ai cha ac e is ic o
in ellec ually cu ious people wi h a b oad imagina ion and o iginali y
(Ali, 2019) who ha e wide in e es s and a e cons an ly looking o new
in o ma ion (Bozionelos e al., 2014). P e ious esea ch has shown he
exis ence o a posi i e ela ionship be ween openness o expe ience
and a ious aspec s o inno a ion, including inno a i eness, measu ed
as an elemen o he To ance C ea i i y Tes (Azami & Kaikha ani,
2017), indi idual inno a i eness endency (Ali, 2019), indi idual inno-
a ion compe encies (Saa ci & O aci, 2020), c ea i i y (I. Abdullah e al.,
2016;McC ae, 1987;Shalley e al., 2004;Zhou & Geo ge, 2001), inno a-
i e wo k beha iou (H. Abdullah e al., 2019), success o adical new
p oduc de elopmen eams (A onson e al., 2008), inno a ion capabil-
i y (Hsieh e al., 2011), inno a ions c ea ed in he open doing-using-
in e ac ing mode (Runs & Thom€
a, 2021), and na ional le els o inno a-
ion (S eel e al., 2012). In addi ion, p e ious s udies ha e also sugges ed
a ela ionship be ween openness o expe ience and inno a ion pe o -
mance (Weele, 2013). Fu he mo e, high le els o openness in eams
ha e also been ound o suppo inno a i e ask pe o mance
(Buchanan, 1998). Simila ly, openness has been ound o be a s ong
p edic o o inno a ion-suppo ing na ional cul u al p ac ices ega ding
inno a ion inpu s and ou comes (Rossbe ge , 2014).
I is also wo h poin ing ou ha some s udies show o he ela-
ionships. Con a y o expec a ions, Cia a ella e al. (2004) ound a
nega i e ela ionship be ween he en ep eneu ’s openness and
long- e m en u e su i al. In u n, Hsieh’s e al. (2011) esea ch
in e es ingly did no confi m he ela ionship be ween openness and
echnological (p ocesses) inno a ion.
Openness was also ound o be a mode a o o he ela ionship
be ween job sa is ac ion and inno a i e wo k beha iou , and be ween
job sa is ac ion and he sub-dimensions o idea gene a ion, idea p omo-
ion and idea ealisa ion (Mus a a e al., 2021). Finally, i is also wo h
poin ing ou s udies showing ha he mo i a ion o become sel -
employed based on he willingness o ake isks and seize oppo uni ies,
i.e. c ea i i y, a ea u e so c ucial o openness, is a significan p edic o
o he in oduc ion o p ocess inno a ions in SMEs (Ma ínez-Rom
an e
al., 2011) and among he sel -employed (Plo niko a e al., 2016). Conse-
quen ly, he fi s hypo heses we e p oposed:
H1 - Openness o expe ience, as a pe sonali y ai o mic oen ep e-
neu s, posi i ely impac s hei in oduc ion o p oduc inno a ions.
H2 - Openness o expe ience, as a pe sonali y ai o mic oen ep e-
neu s, posi i ely impac s hei in oduc ion o business p ocess
inno a ions.
Conscien iousness is a ea u e ha de e mines he le el o o gani-
sa ion, pe se e ance and mo i a ion a wo k. The lowe he le el o
conscien iousness, he mo e diso ganised a pe son is and he as e
he onse o discou agemen (Cholle e al., 2016). I is wo h no ing
ha he li e a u e on he impac o conscien iousness on inno a ion is
di ided (Ali, 2019). Al hough conscien ious indi iduals’p opensi y o
M. Zas empowski Jou nal o Inno a ion & Knowledge 9 (2024) 100552
3
plan, o ganise and ocus on achie emen (Weele, 2013) migh hinde
inno a i e beha iou , he quali ies o compe ence, pe sis ence and sel -
discipline (McC ae & Te acciano, 2005) a e c ucial o de eloping suc-
cess ul inno a ions. Among he li e a u e p o iding e idence o a posi-
i e ela ionship, we can poin o he ela ionship be ween
conscien iousness and inno a i eness as a ea u e o c ea i i y (Azami &
Kaikha ani, 2017), indi idual inno a i eness (Ali, 2019), indi idual
inno a ion compe encies (Saa ci & O aci, 2020), success o adical new
p oduc de elopmen eams (A onson e al., 2008), long- e m en u e
su i al (Cia a ella e al., 2004), and inno a ion capabili y (Hsieh e al.,
2011). Addi ionally, Buchanan (1998) disco e ed ha ele a ed consci-
en iousness significan ly p edic s a eam’s pe o mance on inno a i e
asks. In e es ingly, Hsieh e al. (2011) also showed a posi i e ela ion-
ship be ween conscien iousness and echnological (p ocesses) inno a-
ion. Howe e , based on a quali a i e li e a u e e iew, I. Abdullah e al.
(2016) showed ha people wi h high le els o conscien iousness a e
less c ea i e. Also, o he s udies ha e ound an insignifican ela ionship
be ween conscien iousness and inno a i eness (Ki on & De Cian is,
1986;S eel e al., 2012). Ul ima ely, we belie e ha posi i e ai s o
conscien iousness may be essen ial o b inging inno a i e p oduc and
business p ocess ideas o ui ion. Acco dingly, he ollowing hypo heses
we e o mula ed:
H3 - Conscien iousness, as a pe sonali y ai o mic oen ep eneu s,
posi i ely impac s hei in oduc ion o p oduc inno a ions.
H4 - Conscien iousness, as a pe sonali y ai o mic oen ep eneu s,
posi i ely impac s hei in oduc ion o business p ocess inno a ions.
Ex o e sion desc ibes a pe son’s ela ionship wi h he ou side
wo ld (Cholle e al., 2016). I cha ac e ises indi iduals who end o
be ou going, sociable, ene ge ic (Ma ca i e al., 2008), asse i e and
ac i e (Weele, 2013). These cha ac e is ics allow ex o e ed people
o e ec i ely build and engage wi h hei social ne wo k (Ali, 2019).
Consequen ly, his os e s oppo uni ies o knowledge explo a ion
and exploi a ion (Judge e al., 1999;Weele, 2013), which a e c ucial
o inno a ion. Mo eo e , he ai s o en husiasm and posi i e emo-
ions (Rossbe ge , 2014) empowe ex o e ed indi iduals o expe i-
men wi h new ideas. Some p io esea ch shows ha ex a e sion
has a posi i e impac on inno a i eness (Azami & Kaikha ani, 2017),
indi idual inno a i eness (Ali, 2019), indi idual inno a ion compe-
encies (Saa ci & O aci, 2020), c ea i i y (I. Abdullah e al., 2016),
inno a ion capabili y (Hsieh e al., 2011), inno a ions c ea ed in he
closed doing-using-in e ac ing mode (Runs & Thom€
a, 2021), and
s onge en ep eneu ial in en ions (Eas man e al., 2001). Addi ion-
ally, Buchanan (1998) disco e ed ha eams wi h mode a e le els o
ex a e sion end o excel in inno a i e ask pe o mance. Some
s udies, such as hose by Hsieh e al. (2011), ha e also sugges ed a
posi i e ela ionship be ween ex o e sion and echnological (p o-
cess) inno a ion. I is wo h no ing, howe e , ha some s udies ha e
no confi med ha ex a e sion has a significan impac on inno a-
i eness (Ki on & De Cian is, 1986;S eel e al., 2012). Taking in o
accoun he abo e, wo u he hypo heses we e p oposed:
H5 - Ex o e sion, as a pe sonali y ai o mic oen ep eneu s, posi-
i ely impac s hei in oduc ion o p oduc inno a ions.
H6 - Ex o e sion, as a pe sonali y ai o mic oen ep eneu s, posi-
i ely impac s hei in oduc ion o business p ocess inno a ions.
Ag eeableness as a pe sonali y ai indica es whe he a pe son is
us wo hy, al uis ic, ca ing, manipula i e, sel -cen ed, cau ious o
lacking in compassion (Cholle e al., 2016). The link be ween ag ee-
ableness and indi idual inno a i eness is a he complex. On he one
hand, ai s such as coope a i eness, good na u e and flexibili y
(Weele, 2013) seem o os e inno a ion, while on he o he hand,
ai s such as ole ance and compliance (McC ae & Te acciano, 2005)
migh hinde an indi idual’s inno a i e inclina ions. Consequen ly, i is
unsu p ising ha some s udies ha e iden ified ag eeableness as ha ing
an insignifican impac on inno a ion capabili y (Hsieh e al., 2011).
Meanwhile, he esul s p esen ed by I. Abdullah e al. (2016) sugges a
nega i e ela ionship - high le els o ag eeableness esul in less c ea i -
i y. Simila ly, Pa e son (2002) also showed he exis ence o a nega i e
ela ionship. On he o he hand, howe e , some p io s udies ha e indi-
ca ed a posi i e ela ionship be ween ag eeableness and indi idual
inno a i eness (Ali, 2019), echnology inno a ion (Hsieh e al., 2011)
and na ional le els o inno a ion (S eel e al., 2012). Ag eeableness is
also a significan p edic o o inno a ion-suppo i e na ional cul u al
p ac ices (Rossbe ge , 2014). While ce ain aspec s o ag eeableness
migh hinde inno a i e beha iou , success ully implemen ing inno a-
ions elies on managing social ne wo ks and business pa ne s e ec-
i ely, whe e he posi i e ai o ag eeableness plays a c ucial ole
(Rossbe ge , 2014). As Ali (2019) sugges s, ag eeableness plays a key
ole in whe he he indi idual is welcomed by social g oups and can
success ully sus ain social and business ela ionships, which a e essen-
ial o he success o inno a i e p ojec s. Finally, i is wo h emphasis-
ing ha Hsieh e al. (2011) showed ha he e is a posi i e ela ionship
be ween ag eeableness and echnological (p ocess) inno a ion. Conse-
quen ly, he ollowing hypo heses we e p oposed:
H7 - Ag eeableness, as a pe sonali y ai o mic oen ep eneu s, posi-
i ely impac s hei in oduc ion o p oduc inno a ions.
H8 - Ag eeableness, as a pe sonali y ai o mic oen ep eneu s, posi-
i ely impac s hei in oduc ion o business p ocess inno a ions.
Neu o icism poin s o indi idual di e ences in adjus men and
emo ional s abili y. The highe i s le el, he mo e o en people
end o ha e nega i e emo ions. A lowe le el cha ac e ises confi-
den , calm and balanced people (Cholle e al., 2016). The impac
o neu o icism on inno a i eness is mo e clea ly unde s ood.
T ai s such as anxie y, hos ili y and sel -consciousness (McC ae &
Te acciano, 2005), along wi h a endency o expe ience nega i e
emo ions (Rossbe ge , 2014), sugges ha indi iduals wi h high
le els o neu o icism may s uggle o engage in inno a i e beha -
iou s and pu sue new ideas (Eas man e al., 2001). Inno a i e
indi iduals a e o en sel -assu ed (Ki on & De Cian is, 1986)and
emo ionally s able (Hsieh e al., 2011), ai s ha a e linked o
lowe le els o neu o icism. P io esea ch has sugges ed a nega-
i e ela ionship be ween neu o icism and inno a i eness among
enginee s (Azami & Kaikha ani, 2017), indi idual inno a i eness
(Ali, 2019), c ea i i y (I. Abdullah e al., 2016), success o adical
and inc emen al new p oduc de elopmen eams (A onson e al.,
2008), inno a i e pe o mance (Rod igues & Rebelo, 2019), and
inno a ions c ea ed in he open doing-using-in e ac ing mode
(Runs & Thom€
a, 2021). In e es ingly, Hsieh’s e al. (2011)
esea ch did no confi m he ela ionship be ween emo ional s a-
bili y and echnological (p ocess) inno a ion. The a gumen s p e-
sen ed abo e lead o he o mula ion o he ollowing hypo heses:
H9 - Neu o icism, as a pe sonali y ai o mic oen ep eneu s, nega-
i ely impac s hei in oduc ion o p oduc inno a ions.
H10 - Neu o icism, as a pe sonali y ai o mic oen ep eneu s, nega-
i ely impac s hei in oduc ion o business p ocess inno a ions.
As a consequence, he ollowing concep ual model was p oposed,
as shown in Fig. 1.
Ma e ial and me hods
Da a se
The da a cons i u ing he basis o he analysis was collec ed in
he pe iod om Augus o Oc obe 2022. Based on he ecommenda-
ion o he Eu opean Commission (2003), en e p ises employing up
M. Zas empowski Jou nal o Inno a ion & Knowledge 9 (2024) 100552
4
o 9 people we e ea ed as mic o-en e p ises. The esea ch sample
was d awn by Poland’s S a is ical O fice om he Na ional O ficial
Regis e o Economy En i ies (NOREE). The sampling ame con-
sis ed o ac i e mic o-en e p ises, o which he e we e 4,497,099
in Poland in 2022. The sampling was ca ied ou using a s a ified
sampling scheme. The ollowing c i e ia dis inguished he laye s:
ac i i ies, adminis a i e egion and legal o m. The size o he
p ima y sample −1,850 uni s - was di ided in o sampling s a a
using an alloca ion p opo ional o he size in he sampling ame,
wi h a modifica ion o ensu e ha each s a um in he p ima y
sample was ep esen ed by a leas 1 uni . In addi ion o he p i-
ma y sample, a ese e sample was d awn o he same s uc u e
and size, co esponding o 19 imes he size o he p ima y sam-
ple. The ese e sample was d awn sepa a ely om he p ima y
sample. The o al numbe o andomly d awn samples - p ima y
and ese e - was 36,994. The final da a se co e ed 1,848 mic o-
en e p ises, which, wi h a ac ion size o 50% and a confidence
le el o 99%, makes i possible o d aw conclusions wi h a maxi-
mum e o o §3%.
The mic o-en e p ises in he s udy sample ep esen ed all ypes o
economic ac i i y (Table 1). De ia ions om he NOREE egis e
s uc u e sligh ly exceeded 3% in only one case −L - ac i i ies ela ed
o eal es a e.
Va iables
The inno a i eness o mic o-en ep eneu s − he dependen a i-
able - was measu ed in acco dance wi h he guidelines o he ou h
edi ion o he OSLO Manual (OECD & Eu os a , 2018). Acco ding o
he manual, inno a ions come in he ollowing wo ypes:
P oduc inno a ion −which “is a new o imp o ed good o se ice
ha di e s significan ly om he fi m’s p e ious goods o se ices
and ha has been in oduced on he ma ke ”(OECD & Eu os a ,
2018, p. 21),
Business p ocess inno a ion −which “is a new o imp o ed busi-
ness p ocess o one o mo e business unc ions ha di e s signifi-
can ly om he fi m’s p e ious business p ocesses and ha has been
b ough in o use by he fi m”(OECD & Eu os a , 2018, p. 21).
Consequen ly, mic o-en ep eneu s we e asked whe he hey had
in oduced a p oduc o business p ocess inno a ion in he p e ious
h ee yea s (2019-2021). As a esul , aking in o accoun he possible
ca ego ies o inno a ions, he nine ollowing dummy a iables we e
used, ela ing o he in oduc ion o new o imp o ed p oduc s o
p ocesses (OECD & Eu os a , 2018, pp. 70−74):
Wi hin p oduc inno a ions:
○y
1
−Goods,
○y
2
−Se ices,
Wi hin business p ocess inno a ions:
○y
3
−Me hods o p oducing goods o p o iding se ices
(including me hods o de eloping goods o se ices),
○y
4
−Logis ics, deli e y o dis ibu ion me hods,
○y
5
−Me hods o in o ma ion p ocessing o communica ion,
○y
6
−Me hods o accoun ing o o he adminis a i e ope a-
ions,
○y
7
−Business p ac ices o o ganising p ocedu es o ex e nal
ela ions,
○y
8
−Me hods o o ganising wo k esponsibili y, decision mak-
ing o human esou ce managemen ,
○y
9
−Ma ke ing me hods o p omo ion, packaging, p icing,
p oduc placemen o a e -sales se ices.
To examine he pe sonali y ai s o mic o-en ep eneu s −as
independen a iables - he Big Fi e In en o y de eloped by John and
S i as a a (1999) was used. This includes 44 s a emen s assessed on
Fig. 1. Concep ual model.
M. Zas empowski Jou nal o Inno a ion & Knowledge 9 (2024) 100552
5
afi e-poin Like scale. Indi idual pe sonali y ai s we e coded as
ollows:
x
1
−Openness o expe ience,
x
2
−Conscien iousness,
x
3
−Ex o e sion,
x
4
−Ag eeableness,
x
5
−Neu o icism.
The means o he co esponding i ems we e used as final mea-
su emen alues o he independen a iables.
In line wi h he subjec li e a u e, con ol a iables ha may
a ec mic o-en ep eneu s’inno a i eness we e also in oduced
in o he es ima ed models. He e, i was decided o use en e p ise
age (Dona e & Pe~
na, 2016;JiaHu e al., 2017)andsize(Guan e
al., 2006;Ma inez-Roman & Rome o, 2017). These a iables
we e coded as ollows:
x
6
- En e p ise age −mic o-en e p ise age measu ed by he num-
be o yea s since he business was ounded − his a iable was
nume ical, and a loga i hm was applied o he calcula ions;
x
7
- En e p ise size −mic o-en e p ise size measu ed by he num-
be o employees (nume ical).
The basic s a is ics desc ibing he examined a iables a e p e-
sen ed in Table 2.
Me hod
Inno a ion is a complex p ocess wi h se e al in e dependencies.
P e ious esea ch shows ha in oducing one ype o inno a ion is
no independen o in oducing o he ypes (Zas empowski, 2023).
The e o e, assuming ha his ela ionship also occu s in he case o
mic o-en ep eneu s, and d awing upon s a is ics li e a u e (Ash o d
& Sowden, 1970), a mul i a ia e p obi model (MVP) was used o
conside he co ela ion o e o e ms (Maie a, 2015;Wainaina e
al., 2016). The mul i a ia e p obi model was de eloped o eg ess a
se o co ela ed bina y a iables on a combina ion o con inuous and
disc e e p edic o s (Lesa e & Molenbe ghs, 1991). While his
me hod has been applied in biological (Kes eloo e al., 1989), eco-
nomic (Maie a, 2015;Wainaina e al., 2016), and psychosociological
Table 1
S uc u e o he sample.
Cha ac e is ics NOREE
a
(%)
Sample (%) Di e ence:
NOREE −Sample
(% poin )
Ac i i ies (PKD)
b
A - ag icul u e, o es y, hun ing and fishing 1.52 1.73 -0.21
B - mining and qua ying 0.10 1.84 -1.74
C - manu ac u ing 8.37 7.03 1.34
D - elec ici y, gas, s eam, ho wa e and ai condi ioning 0.26 1.57 -1.31
E - wa e supply; sewage and was e managemen and emedia ion ac i i ies 0.31 2.11 -1.80
F - building cons uc ion 13.50 13.64 -0.14
G - wholesale and e ail ade; epai o mo o ehicles, excluding mo o cycles 21.47 19.32 2.15
H - anspo and s o age 6.08 5.95 0.12
I - ac i i ies ela ed o accommoda ion and ca e ing se ices 3.22 3.08 0.14
J - in o ma ion and communica ion 4.30 5.84 -1.54
K-finance and insu ance 2.68 2.87 -0.19
L - ac i i ies ela ed o eal es a e 5.95 2.71 3.24
M - p o essional, scien ific and echnical ac i i y 10.68 11.47 -0.80
N - adminis a ion and suppo ac i i ies 3.37 3.46 -0.09
P - educa ion 3.40 3.35 0.04
Q - heal h ca e and social wel a e 5.84 7.31 -1.46
R - ac i i ies ela ed o cul u e, en e ainmen and ec ea ion 1.85 1.95 -0.10
S - O he se ice ac i i ies 6.82 4.76 2.06
No es:
a
Code lis o classifica ion o business ac i i ies in Poland.
b
Na ional o ficial egis e o economy en i ies.
Table 2
Desc ip ion o a iables.
Va iable % - yes C onbach’s
a
Mean S.E. M D S.D. SD
2
Min. Max.
y
1
4.654 - 0.047 0.005 0.000 0.000 0.211 0.044 0.000 1.000
y
2
8.496 - 0.085 0.006 0.000 0.000 0.279 0.078 0.000 1.000
y
3
10.335 - 0.103 0.007 0.000 0.000 0.305 0.093 0.000 1.000
y
4
5.303 - 0.053 0.005 0.000 0.000 0.224 0.050 0.000 1.000
y
5
10.335 - 0.103 0.007 0.000 0.000 0.305 0.093 0.000 1.000
y
6
8.820 - 0.088 0.007 0.000 0.000 0.284 0.080 0.000 1.000
y
7
9.416 - 0.094 0.007 0.000 0.000 0.292 0.085 0.000 1.000
y
8
9.037 - 0.090 0.007 0.000 0.000 0.287 0.082 0.000 1.000
y
9
8.063 - 0.081 0.006 0.000 0.000 0.272 0.074 0.000 1.000
x
1
- 0.806 3.538 0.014 3.500 3.500 0.616 0.379 1.400 5.000
x
2
- 0.905 3.750 0.018 4.000 4.556 0.788 0.621 1.222 4.889
x
3
- 0.856 3.555 0.019 3.625 4.250 0.804 0.647 1.250 4.750
x
4
- 0.938 3.293 0.021 3.333 2.667 0.892 0.795 1.333 5.000
x
5
- 0.875 2.682 0.021 2.625 3.000 0.888 0.789 1.000 5.000
x
6
- - 0.969 0.008 1.000 0.602 0.354 0.125 0.000 2.021
x
7
- - 2.692 0.068 2.000 0.000 2.937 8.628 0.000 9.000
M. Zas empowski Jou nal o Inno a ion & Knowledge 9 (2024) 100552
6
s udies (Lesa e & Molenbe ghs, 1991), i has no ye gained wide-
sp ead use in esea ch on inno a ion (Zas empowski, 2023).
MVP examines he e ec o he independen a iables on each
ype o in oduced inno a ion while allowing o he co ela ion o
unobse ed and unmeasu ed ac o s (e o e ms). As Zas empowski
(2023) sugges s, such co ela ions be ween he di e en ypes o
in oduced inno a ions could be he e ec o hei in e connec ed-
ness e.g., business p ocess inno a ion leads o p oduc inno a ions,
and he co ela ions a e he e o e posi i e. On he o he hand, o
example due o mic o-en ep eneu s limi ed esou ces ( he adop ion
o a gi en ype o inno a ion esul s in he abandonmen o o he s),
such co ela ions a e nega i e. Hassen (2015),(Lin e al. (2005), and
(Wainaina e al., (2016) sugges ha in he case o such a co ela ion,
he es ima ion o simple p obi models may be biased and ine ec-
i e.
The sugges ed MVP model con ains nine bina y choice equa ions
ela ing o he in oduc ion o wo ypes o p oduc inno a ion and
se en ypes o business p ocess inno a ions. Consequen ly, he e a e
nine dependen bina y a iables yij o mic o-en ep eneu iand
inno a ion j. This can be w i en as (Wainaina e al., 2016):
y
ijm ¼X0
ijmbmþeijm m¼1;2;... 9ð1Þ
yijm ¼1i y
ijm >0
0o he wise
;ð2Þ
whe e y
ijm is a la en a iable ha cap u es he deg ee o which
mic o-en ep eneu s see inno a ion mas wo h in oducing. This
la en a iable is assumed o be a linea combina ion o he obse ed
Big Fi e pe sonali y ai s X0
ijm, and he unobse ed cha ac e is ics
cap u ed by he s ochas ic e o e m eijm. The ec o o he pa ame-
e s o be es ima ed is deno ed by bm. Conside ing he la en na u e
o y
ijm, he es ima ion is based on he obse able bina y yijm, indica -
ing whe he a mic o-en ep eneu in oduced a pa icula inno a ion
in he p e ious h ee yea s (2019-2021).
The e o e ms eijm (m¼1;2;...;9) ha e no mal mul i a ia e
dis ibu ion, each wi h means o 0 and a a iance-co a iance ma ix
V, whe e V has 1 on he leading diagonal, and co ela ions pjk ¼pkj as
o -diagonal elemen s (Wainaina e al., 2016).
To quan i y he ma ginal e ec s (dF/dx) o he independen a ia-
bles, he p obabili y o each inno a ion implemen a ion:
P yijm ¼1
¼Fy
ijm
;m¼1;2;... 9ð3Þ
can be di e en ia ed, whe e Fð:Þis he uni a ia e s anda d no mal
cumula i e dis ibu ion unc ion (Lin e al., 2005).
Pe Cappella i and Jenkins’(2003) ecommenda ions, simula ed
maximum likelihood es ima ion and STATA.16.1 so wa e we e used
o es ima e all models.
Resul s
The Kendall co ela ion coe ficien s p esen ed in Table 3 allow o
he o mula ion o se e al obse a ions. Fi s ly, he e a e co ela ions
be ween pa icula ypes o inno a ions (y
1
−y
9
) in oduced by
mic o-en ep eneu s. These a e posi i e and ange om 0.239 (y
1
/y
6
)
o 0.751 (y
2
/y
3
). This confi ms he alidi y o using MVP as an es ima-
ion me hod. Secondly, he e a e s a is ically significan coe ficien s
be ween he dependen and independen a iables. Howe e , hei
alues o he Big Fi e pe sonali y ai s (x
1
−x
5
) a e always below
0.27. The e o e, he in e dependence is e y poo . Thi dly, he coe fi-
cien s among he Big Fi e pe sonali y a iables a e consis en ly
below 0.5, and he a iance infla ion ac o s (VIF) a e all below 10
( he highes obse ed VIF is 2.96), indica ing ha mul icollinea i y is
no a conce n.
Tables 4 and 5 show he MVP model es ima ion esul s. I is wo h
no ing ha he conduc ed likelihood a io es s clea ly indica e ha
he null hypo hesis o ze o co ela ion be ween he e o e ms
should be ejec ed (P <0.0000). This demons a es ha MVP is p e-
e ed o e single-equa ion p obi models.
The esul s also indica e ha he e is a s ong co ela ion be ween
e o e ms (g ea e han 0.7) o se e al ypes o inno a ions in o-
duced by mic o-en ep eneu s (Table 5). I is s onges in he case o
ho41 (0.841), i.e. be ween inno a ions in logis ics, deli e y o dis i-
bu ion me hods (y
4
) and inno a ions in goods (y
1
), as well as ho75
(0.820) and ho76 (0.786) - ha is, be ween inno a ion in business
p ac ices o o ganising p ocedu es o ex e nal ela ions (y
7
) and
me hods o in o ma ion p ocessing o communica ion (y
5
) and
me hods o accoun ing o o he adminis a i e ope a ions (y
6
).
O he s ong co ela ions a e ho71, ho92, ho95, ho65, ho86,
ho96 and ho83. This confi ms ha he a ious ypes o in oduced
inno a ions a e no independen o one ano he (Maie a, 2015;Zas-
empowski, 2023) and ha along wi h an inc ease/dec ease in one o
hem, he o he s inc ease o dec ease analogously.
Only wo ou o fi e mic o-en ep eneu s’pe sonali y ai s a e
s a is ically significan de e minan s explaining all ca ego ies o inno-
a ions (y
1
-y
9
)(Table 4). These ai s a e Openness o expe ience (x
1
)
and Ex o e sion (x
3
). Conscien iousness (x
2
) a ec s only some o he
analysed inno a ions, i.e. wo ca ego ies o p oduc inno a ions (y
1
and y
2
) and wo o business p ocess inno a ions (y
5
and y
9
). The
Table 3
Co ela ion ma ix.
Va . y
1
y
2
y
3
y
4
y
5
y
6
y
7
y
8
y
9
x
1
x
2
x
3
x
4
x
5
x
6
x
7
y
1
1
y
2
0.532** 1
y
3
0.431** 0.751** 1
y
4
0.429** 0.448** 0.523** 1
y
5
0.313** 0.438** 0.550** 0.491** 1
y
6
0.239** 0.295** 0.415** 0.412** 0.647** 1
y
7
0.316** 0.354** 0.493** 0.420** 0.548** 0.514** 1
y
8
0.271** 0.344** 0.470** 0.439** 0.556** 0.528** 0.687** 1
y
9
0.454** 0.580** 0.631** 0.506** 0.526** 0.405** 0.463** 0.406** 1
x
1
0.161** 0.178** 0.190** 0.131** 0.228** 0.177** 0.227** 0.228** 0.191** 1
x
2
0.128** 0.136** 0.132** 0.106** 0.156** 0.108** 0.131** 0.113** 0.149** 0.316** 1
x
3
0.153** 0.204** 0.244** 0.181** 0.263** 0.216** 0.252** 0.266** 0.211** 0.316** 0.216** 1
x
4
0.092** 0.127** 0.157** 0.113** 0.180** 0.166** 0.174** 0.207** 0.137** 0.149** 0.003 0.438** 1
x
5
-0.090** -0.116** -0.150** -0.105** -0.172** -0.171** -0.171** -0.206** -0.117** -0.187** -0.034*-0.409** 0.427** 1
x
16
0.032 0.030 0.020 0.021 -0.007 -0.003 0.003 -0.005 0.028 -0.004 0.030 -0.006 -0.027 0.014 1
x
17
0.075** -0.022 -0.024 0.097** -0.008 0.018 0.015 0.015 0.044*-0.036*-0.067** 0.098** 0.157** -0.149** -0.031 1
* p-Value ≤0.05
** p-Value ≤0.01.
M. Zas empowski Jou nal o Inno a ion & Knowledge 9 (2024) 100552
7
emaining wo Big Fi e ai s, i.e. Ag eeableness (x
4
) and Neu o icism
(x
5
), ha e no e ec .
Rega ding he con ol a iables aken in o accoun , he size o he
en e p ise had a significan impac on he in oduc ion o inno a ion,
bu only in he field o goods inno a ions (y
1
).
Discussion
As Ma z e al. (2016) indica e, "decades o psychological esea ch
sugges ha indi iduals’beha iou s and p e e ences a e no andom
bu d i en by unde lying psychological cons uc s: pe sonali y
ai s". This mo i a ed us o pose he ollowing esea ch ques ion -
Does a mic o-en ep eneu ’s pe sonali y a ec hei inno a i eness?
The esul s allow us o conclude ha he answe is pa ly a fi ma i e.
Some ea u es, namely Openness o expe ience (x
1
), Conscien ious-
ness (x
2
) and Ex o e sion (x
3
), ha e a posi i e impac , while some −
Ag eeableness (x
4
) and Neu o icism (x
5
) - do no , and u ned ou o
be s a is ically insignifican . The e o e, i is wo h looking a he
esul s om he pe spec i e o he wo ypes o inno a ions analysed
- p oduc and business p ocesses.
Fi s , le us look a he esul s om he pe spec i e o p oduc
inno a ions implemen ed by mic o-en ep eneu s in wo ca ego-
ies - goods (y
1
) and se ices (y
2
). Ou o he Big Fi e pe sonali y
ai s (Goldbe g, 1990;McC ae & Cos a, 1987), h ee ai s ha e a
posi i e impac on bo h ca ego ies o p oduc inno a ions, namely
openness o expe ience, conscien iousness and ex o e sion. This
means ha he e is no eason o ejec hypo heses H
1
,H
3
and H
5
(p ≤0.01). In o he wo ds, looking om he poin o iew o ma -
ginal e ec s a he mean (dF/dx), mic o-en ep eneu s wi h a
highe le el o openness o expe ience, conscien iousness and
ex o e sion, compa ed o mic o-en ep eneu s wi h a lowe le el
o hese ai s, ha e a highe p obabili y o in oducing a p oduc
inno a ion (Table 4). This is he highes in he case o ex a e sion
and implemen a ion o se ice inno a ions (y
2
) and amoun s o
0.051 (dF/dx; Model 2; x
3
), while he lowes is o conscien ious-
ness and implemen a ion o goods inno a ion (y
1
), whe e i is
0.013 (dF/dx; Model 1; x
2
).
In he case o business p ocess inno a ions implemen ed by
mic o-en ep eneu s in he se en ca ego ies (y
3
-y
9
), only wo o he
Big Fi e pe sonali y ai s, namely openness o expe ience and ex a-
e sion, posi i ely a ec all ca ego ies o hese inno a ions. This indi-
ca es ha he e is also no eason o ejec hypo heses H
2
(p ≤0.05)
and H
6
(p ≤0.01). Analysing he esul s om he pe spec i e o ma -
ginal e ec s a he mean (dF/dx), mic o-en ep eneu s wi h a highe
le el o openness o expe ience and ex a e sion ha e a highe p ob-
abili y o in oducing business p ocess inno a ions han mic o-en e-
p eneu s wi h a lowe le el o hese ai s, (Table 4). This is he
highes in wo cases, namely in ex a e sion and implemen a ion o
Table 4
Mul i a ia e p obi model esul s −Big Fi e pe sonali y and mic oen ep eneu s’inno a i eness.
Model 1 (y
1
) Model 2 (y
2
) Model 3 (y
3
) Model 4 (y
4
) Model 5 (y
5
) Model 6 (y
6
) Model 7 (y
7
) Model 8 (y
8
) Model 9 (y
9
)
bdF/dx bdF/dx bdF/dx bdF/dx bdF/dx bdF/dx bdF/dx bdF/dx bdF/dx
x
1
0.539**
(0.129)
0.027**
(0.006)
0.374**
(0.094)
0.034**
(0.010)
0.323**
(0.086)
0.035**
(0.011)
0.276*
(0.111)
0.014*
(0.006)
0.501**
(0.092)
0.051**
(0.011)
0.302**
(0.090)
0.035**
(0.011)
0.516**
(0.093)
0.051**
(0.010)
0.528**
(0.096)
0.044**
(0.009)
0.526**
(0.095)
0.039**
(0.009)
x
2
0.319**
(0.106)
0.013**
(0.005)
0.239**
(0.078)
0.026**
(0.008)
0.127
(0.069)
0.022
(0.009)
0.146
(0.093)
0.010
(0.005)
0.258**
(0.076)
0.024**
(0.008)
0.154
(0.072)
0.015
(0.008)
0.149
(0.074)
0.013
(0.007)
0.032
(0.075)
0.003
(0.006)
0.267**
(0.081)
0.023**
(0.007)
x
3
0.395**
(0.121)
0.019**
(0.006)
0.499**
(0,093)
0.051**
(0.010)
0.654**
(0.089)
0.076**
(0.010)
0.714**
(0.125)
0.038**
(0.006)
0.596**
(0.092)
0.065**
(0.010)
0.439**
(0.087)
0.050**
(0.010)
0.552**
(0.093)
0.059**
(0.009)
0.624**
(0.103)
0.055**
(0.008)
0.530**
(0.097)
0.049**
(0.008)
x
4
-0.080
(0.123)
-0.005
(0.006)
0.077
(0.094)
0.005
(0.011)
0.044
(0.087)
-0.001
(0.012)
-0.029
(0.112)
-0.003
(0.007)
0.108
(0.095)
0.006
(0.011)
0.001
(0.093)
-0.003
(0.011)
0.021
(0.095)
-0.010
(0.010)
0.080
(0.098)
0.001
(0.009)
0.103
(0.096)
0.005
(0.009)
x
5
-0.072
(0.129)
-0.002
(0.006)
0.017
(0,096)
0.000
(0.011)
-0.031
(0.089)
-0.007
(0.012)
0.006
(0.114)
0.000
(0.007)
0.032
(0.096)
-0.004
(0.011)
-0.163
(0.093)
-0.024
(0.011)
-0.066
(0.096)
-0.011
(0.010)
-0.161
(0.100)
-0.016
(0.009)
0.121
(0.097)
0.007
(0.009)
x
6
0.252
(0.170)
0.015
(0.008)
0.208
(0.133)
0.027
(0.015)
0.058
(0.115)
0.017
(0.016)
0.141
(0.152)
0.013
(0.009)
-0.044
(0.123)
0.002
(0.014)
0.047
(0.123)
0.004
(0.014)
0.045
(0.124)
0.006
(0.013)
0.042
(0.127)
0.003
(0.011)
0.224
(0.132)
0.023
(0.013)
x
7
0.048**
(0.017)
0.003**
(0.001)
-0.022
(0.015)
0.000
(0.002)
-0.025
(0.014)
-0.001
(0.002)
0.025
(0.016)
0.003
(0.001)
-0.024
(0.014)
0.000
(0.002)
-0.027
(0.014)
-0.001
(0.002)
-0.018
(0.014)
0.000
(0.001)
-0.019
(0.014)
0.000
(0.001)
0.012
(0.014)
0.003
(0.001)
_cons -6.499**
(0.948)
-6.093**
(0.701)
-5.543**
(0.638)
-6.148**
(0.862)
-6.816**
(0.704)
-4.304**
(0.639)
-5.861**
(0.683)
-5.765**
(0.702)
-7.393**
(0.735)
Log likelihood -2706.609
Wald chi
2
(63) 549.38
P ob >chi
2
0.0000
No es: * p-Value ≤0.05.
** p-Value ≤0.01.
S anda d e o s in pa en heses; N= 1848; Likelihood a io es o ho21 = ho31 = ho41 = ho51 = ho61 = ho71 = ho81 = ho91 = ho32 = ho42 = ho52 = ho62 = ho72 = ho82 =
ho92 = ho43 = ho53 =
ho63 = ho73 = ho83 = ho93 = ho54 = ho64 = ho74 = ho84 = ho94 = ho65 = ho75 = ho85 = ho95 = ho76 = ho86 = ho96 = ho87 = ho97 = ho98 = 0: chi
2
(36) = 2282.68,
P ob chi
2
= 0.0000.
Table 5
Mul i a ia e p obi - es ima es o he co ela ion be ween he equa ion e o e ms.
ho ho21 ho31 ho41 ho51 ho61 ho71 ho81 ho91 ho32 ho42 ho52 ho62
Coe . 0.579** 0.550** 0.841** 0.546** 0.646** 0.748** 0.404** 0.575** 0.678** 0.667** 0.303** 0.457**
S d. E . 0.044 0.043 0.022 0.049 0.042 0.036 0.051 0.041 0.035 0.038 0.058 0.046
ho ho72 ho82 ho92 ho43 ho53 ho63 ho73 ho83 ho93 ho54 ho64 ho74
Coe . 0.557** 0.585** 0.754** 0.361** 0.485** 0.639** 0.616** 0.700** 0.691** 0.347** 0.479** 0.631**
S d. E . 0.044 0.042 0.027 0.051 0.045 0.037 0.042 0.032 0.034 0.054 0.043 0.035
ho ho84 ho94 ho65 ho75 ho85 ho95 ho76 ho86 ho96 ho87 ho97 ho98
Coe . 0.615** 0.697** 0.714** 0.820** 0.466** 0.718** 0.786** 0.710** 0.701** 0.598** 0.617** 0.609**
S d. E . 0.040 0.033 0.033 0.024 0.049 0.033 0.030 0.039 0.035 0.043 0.041 0.041
No es: * p-Value ≤0.05
** p-Value ≤0.01.
M. Zas empowski Jou nal o Inno a ion & Knowledge 9 (2024) 100552
8
