G ommes, Alexande
A icle
Impac o audi assu ance on he quali y o sus ainabili y
epo ing
Junio Managemen Science (JUMS)
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Sugges ed Ci a ion: G ommes, Alexande (2025) : Impac o audi assu ance on he quali y o
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Edi o :
DOMINIK VAN AAKEN
Ad iso y Edi o ial Boa d:
FREDERIK AHLEMANN
JAN-PHILIPP AHRENS
THOMAS BAHLINGER
MARKUS BECKMANN
CHRISTOPH BODE
SULEIKA BORT
ROLF BRÜHL
KATRIN BURMEISTER-LAMP
CATHERINE CLEOPHAS
NILS CRASSELT
BENEDIKT DOWNAR
RALF ELSAS
KERSTIN FEHRE
MATTHIAS FINK
DAVID FLORYSIAK
GUNTHER FRIEDL
MARTIN FRIESL
FRANZ FUERST
WOLFGANG GÜTTEL
NINA KATRIN HANSEN
ANNE KATARINA HEIDER
CHRISTIAN HOFMANN
SVEN HÖRNER
KATJA HUTTER
LUTZ JOHANNING
STEPHAN KAISER
NADINE KAMMERLANDER
ALFRED KIESER
NATALIA KLIEWER
DODO ZU KNYPHAUSEN-AUFSESS
SABINE T. KÖSZEGI
ARJAN KOZICA
CHRISTIAN KOZIOL
MARTIN KREEB
TOBIAS KRETSCHMER
WERNER KUNZ
HANS-ULRICH KÜPPER
MICHAEL MEYER
JÜRGEN MÜHLBACHER
GORDON MÜLLER-SEITZ
J. PETER MURMANN
ANDREAS OSTERMAIER
BURKHARD PEDELL
ARTHUR POSCH
MARCEL PROKOPCZUK
TANJA RABL
SASCHA RAITHEL
NICOLE RATZINGER-SAKEL
ASTRID REICHEL
KATJA ROST
THOMAS RUSSACK
FLORIAN SAHLING
MARKO SARSTEDT
ANDREAS G. SCHERER
STEFAN SCHMID
UTE SCHMIEL
CHRISTIAN SCHMITZ
MARTIN SCHNEIDER
MARKUS SCHOLZ
LARS SCHWEIZER
DAVID SEIDL
THORSTEN SELLHORN
STEFAN SEURING
VIOLETTA SPLITTER
ANDREAS SUCHANEK
TILL TALAULICAR
ANN TANK
ORESTIS TERZIDIS
ANJA TUSCHKE
MATTHIAS UHL
CHRISTINE VALLASTER
PATRICK VELTE
CHRISTIAN VÖGTLIN
STEPHAN WAGNER
BARBARA E. WEISSENBERGER
ISABELL M. WELPE
HANNES WINNER
THOMAS WRONA
THOMAS ZWICK
Volume 10, Issue 1, Ma ch 2025
JUNIOR
MANAGEMENT
SCIENCE
Julian An on Meye , Success Fac o s and De elopmen A eas
o he Implemen a ion o Gene a i e AI in Companies
Vincen Albe h-Jan C eme , Di e si y Wi hin Top
Managemen Teams: The E ec s o Di e si y Wi hin
Boa ds Towa ds Manage ial A en ion on Digi al
T ans o ma ion
Ve onika Timoschenko, Good as Gold o Me ely Gli e ? Eli e
Boa d Membe s' Impac on Fi m Pe o mance
Co nelia Kees, Looking Behind he Fading Feminis Façade o
#Gi lboss
Eliza Alena Ma ie Wei zel, Cop eneu ial Couples in S a ups: A
Comp ehensi e Analysis o Cop eneu ial Couples in
S a ups Compa ed o Classical Businesses
An onia Cichocki, Employmen wi h Au ism: Wha A e
Educa ional and Adap i e Needs o Employe s in
Aus ia om he Pe spec i e o Women wi h Low-
Symp om Au ism?
Paula Bao Quie o, "Well, Now They Know": How Men al
Illness Iden i y Managemen S a egies In luence
Leade s' Responses
Alexande G ommes, Impac o Audi Assu ance on he
Quali y o Sus ainabili y Repo ing
Philipp Ri gen,Unde s anding he E ec o Hedge Fund
Ac i ism on he Ta ge Fi m -A Quali a i e S udy on
Sha eholde Value
Gab iel Benedik Thomas Adams, Ene gy-Awa e P oduc ion
Planning wi h Renewable Ene gy Gene a ion
Conside ing Combined Ba e y- and Hyd ogen-Based
Ene gy S o age Sys ems
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24
44
70
95
135
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ISSN: 2942-1861
Impac o Audi Assu ance on he Quali y o Sus ainabili y Repo ing
Alexande G ommes
Ca holic Uni e si y o Eichs ä -Ingols ad
Abs ac
The subjec o sus ainabili y epo ing is becoming inc easingly impo an . In consequence o he implemen a ion o he
Co po a e Sus ainabili y Repo ing Di ec i e, a subs an ial numbe o companies will be equi ed o ha e hei sus ainabili y
epo s audi ed beginning om inancial yea 2024. This pape examines he in luence o ex e nal assu ance on he quali y
o hose sus ainabili y epo s. The e o e, he epo s o all DAX and MDAX companies o inancial yea 2022 a e examined
using a no el ex ual analysis app oach, o de e mine he indi idual epo quali y. The esul s demons a e ha he e is
no s a is ically signi ican ela ionship be ween assu ance le el and he quali y o sus ainabili y epo s. Con e sely, i was
ound ha companies ha a e ac ing sus ainable disclose a highe quan i y o in o ma ion and a e mo e likely o demand
olun a y assu ance o hei epo s. These indings o e insigh s in o he implica ions o assu ance on sus ainabili y epo ing.
Fu he mo e, he de ailed o e iew o adi ional and s a e-o - he-a ex ual analysis me hods o e s esea che s a aluable
esou ce o iden i ying he mos app op ia e me hods o add ess hei indi idual esea ch ques ions.
Keywo ds: audi assu ance; CSRD; na u al language p ocessing; sus ainabili y epo ing; ex ual analysis
1. In oduc ion
1.1. Mo i a ion
Sus ainabili y has been a opic o in e es in business and
academic esea ch o some ime, bu now mo e han e e .
The numbe o companies epo ing on sus ainabili y- ela ed
issues is g owing apidly, as is he numbe o scien i ic pub-
lica ions (e.g. Amel-Zadeh and Se a eim, 2018, p. 87, Lu-
ca elli e al., 2020, p. 5, Guid y and Pa en, 2012, p. 81).
This is due o an in insic in e es in sus ainabili y on he pa
o companies and hei s akeholde s (Two zydło e al., 2022,
p. 144), bu also o egula o y equi emen s ha ha e been
newly imposed and inc easingly e ined in ecen yea s (H.
Ch is ensen e al., 2021, pp. 1178–1179).
The Global Repo ing Ini ia i e (GRI) epo ing ame-
wo k has eme ged as he leading s anda d o sus ainabil-
i y epo ing. As an au onomous en i y, he GRI has c ea ed
guidelines wi h inpu om s akeholde s ac oss he boa d, os-
e ing a eliable amewo k o epo ing. Companies a e no
equi ed o adhe e o hese guidelines by na ional lawmak-
e s. Ra he , hey se e as a common g ound o epo ing.
I adop ed, he GRI s anda ds enable s anda dized epo -
ing and acili a e compa ison be ween companies, ega dless
o hei size, sec o , o coun y o ope a ion (Ch is o i e al.,
2012, pp. 163–164).
Howe e , he e is mo e han jus olun a y guidelines.
In ac , he Di ec i e 2014/95/EU c ea ed by he Eu opean
Union (EU) equi es companies o epo non- inancial in o -
ma ion. As a esul , public in e es en i ies wi h o e 500 em-
ployees mus comply wi h he Non-Financial Repo ing Di ec-
i e (NFRD). S a ing in he 2017 iscal yea , hese companies
a e equi ed o disclose in o ma ion ega ding en i onmen-
al, social, and employee- ela ed ma e s in hei manage-
men epo s. The pu pose o his equi emen is o p o ide
s akeholde s wi h a clea unde s anding o he cu en s a e
o de elopmen and posi ion o companies in hese a eas (Eu-
opean Union (EU), 2014, pp. 4–5, 8).
No long a e he NFRD ook e ec , he EU e ised i s
sus ainabili y epo ing guidelines h ough he implemen a-
ion o Di ec i e 2022/2464/EU, also known as he Co po-
a e Sus ainabili y Repo ing Di ec i e (CSRD). This di ec i e
was in oduced o add ess signi ican de iciencies in he p e-
DOI: h ps://doi.o g/10.5282/jums/ 10i1pp201-235
© The Au ho (s) 2025. Published by Junio Managemen Science.
This is an Open Access a icle dis ibu ed unde he e ms o he CC-BY-4.0
(A ibu ion 4.0 In e na ional). Open Access unding p o ided by ZBW.
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235202
ious equi emen s, which lacked su icien dep h and scope,
and o conside issues such as da a compa abili y and eli-
abili y. Howe e , one o he main d i e s o change is he
limi ed numbe o epo ing companies. The CSRD will make
sus ainabili y epo ing manda o y no only o public in e -
es en i ies bu also small and medium-sized companies in
he u u e (Eu opean Union (EU), 2022, pp. 19–20).
In addi ion o he new epo ing equi emen s and in-
c eased co e age, he CSRD manda es an audi p ocess.
Speci ically, companies a e equi ed o unde go a limi ed
assu ance e iew o hei sus ainabili y epo s by an ex e -
nal audi o . Unde he NFRD, audi o s only had o con i m
ha he ela ed in o ma ion was published a all. Fu he -
mo e, Membe S a es had he op ion o impose a subs an i e
audi equi emen a he na ional le el. The EU aims o
es ablish a consis en link be ween inancial epo ing and
sus ainabili y epo ing by equi ing a subs an i e audi o
sus ainabili y epo ing conduc ed by an ex e nal audi o , as
inancial epo ing is al eady subjec o a s a u o y audi . The
Commission also ese es he op ion o ake a decision by
2028 o adjus he assu ance le el om limi ed assu ance o
easonable assu ance (Eu opean Union (EU), 2022, pp. 34–
35; Vel e, 2023, p. 4). The manda o y implemen a ion o
sus ainabili y epo audi ing has he po en ial o aid he EU
Commission’s objec i es and enhance he gene al compliance
o sus ainabili y epo s wi h egula o y equi emen s. The e
may be mo e bene i s o conside , bu he manda o y audi
could also c ea e an addi ional bu den. Simila ly, ele a ing
he le el o assu ance om limi ed o easonable could ei he
posi i ely impac epo ing o cause unnecessa y expenses.
Sus ainabili y epo ing is no limi ed o he Eu opean
a ea. O he wo ld’s 250 la ges companies by e enue
(G250), 96 % disclose sus ainabili y in o ma ion in he o m
o epo s (KPMG, 2022, p. 13). Only 38 o he G250 a e
companies om EU membe s. China ep esen s 30 % o
he G250 companies and is showing a posi i e end o-
wa ds epo ing on sus ainabili y (KPMG, 2022, p. 18). The
majo i y o he emaining non-EU G250 a e loca ed in he
Uni ed S a es (69), Japan (26) and he Uni ed Kingdom (9)
(KPMG, 2022, p. 75). All o hese s a es ha e ex ensi e,
bu a ying, epo ing equi emen s. On a global le el, he
In e na ional Financial Repo ing S anda ds (IFRS) Founda-
ion add esses he issue o sus ainabili y h ough he imple-
men a ion o new s anda ds. In his manne , he s anda ds
a e designed o mee he needs o he s akeholde s o e-
po ing companies, such as cus ome s, employees and in-
es o s o he na u al en i onmen , which can be conside ed
a s akeholde i sel (Technical Readiness Wo king G oup
(IFRS Founda ion), 2021). Two i s wo exposu e d a s
ha e al eady been issued. IFRS S1Gene al Requi emen s o
Disclosu e o Sus ainabili y- ela ed Financial In o ma ion is
in ended o p o ide gene al equi emen s o he disclo-
su e o sus ainabili y- ela ed inancial in o ma ion. IFRS S2
Clima e- ela ed Disclosu es co e s he disclosu e o clima e-
ela ed isks and oppo uni ies. Bo h d a s ela e o in o -
ma ion ha is meaning ul o he cash lows o companies
and hus o he alua ion o hose companies (In e na ional
Sus ainabili y S anda ds Boa d, 2022a,2022b).
Wi h di e en accoun ing s anda ds, some manda o y,
some olun a y, some na ional, some in e na ional, and wi h
di e en scope and ma e iali y le els, sus ainabili y epo -
ing is highly di e se. A emp s o homogeniza ion a e con-
on ed wi h ongoing subs an i e and egula o y dynamics.
The implemen a ion o he CSRD egula ions could be a c u-
cial s ep owa ds imp o ing and ha monizing he epo ing
landscape.
1.2. P oblem de ini ion and objec i e
While he NFRD is cu en ly in e ec , he CSRD will ind
applica ion o he i s companies as ea ly as 2024 which
means i will impac he iscal yea o 2023 (Eu opean Union
(EU), 2022, p. 77). I is likely ha he applica ion o he
Di ec i e will no ully achie e he desi ed esul s a i s .
Simila ly, e en a e he implemen a ion o he NFRD, he e
emained po en ial o u he imp o emen (Busco e al.,
2022, p. 95), which is one o he easons why he CSRD was
c ea ed. In he con ex o iden i ied weaknesses o he NFRD,
he EU Commission di ec ly men ions he ole o he audi o
epo ing and ha his should ensu e he eliabili y o he
epo s (Eu opean Union (EU), 2022, p. 19). Addi ionally,
ex e nal audi s may also inc ease compliance wi h he CSRD
and o he egula ions.
On he o he hand, he e a e expenses associa ed wi h he
engagemen o audi i ms. The inc eased scope o he audi
beyond he inancial epo ing has a di ec impac on he o al
audi cos s (Zaman e al., 2011, p. 190). A he same ime,
a manda o y audi ing equi emen does no gua an ee audi
quali y. P e ious s udies ha e shown a a ie y o weaknesses
ha can occu in he a ea o audi ing (B. Ch is ensen e al.,
2016, p. 1671).
The pu pose o his pape is o in es iga e he ex en o
which he audi o sus ainabili y epo s can imp o e he
quali y o hese epo s, whe e quali y is p ima ily exp essed
in e ms o he epo s’ compliance wi h egula o y equi e-
men s.
Non- inancial epo ing is he e ogeneous and hus p o-
ides nume ous oppo uni ies o academic esea ch. Due o
i s ac uali y, he domain s ill has some esea ch gaps ha can
be closed. Since non- inancial epo ing essen ially consis s
o quali a i e epo ing in ex o m, ex ual analysis me h-
ods a e pa icula ly help ul in illing hese esea ch gaps. This
hesis makes se e al con ibu ions. Fi s , i con ibu es o he
li e a u e in he ield o audi ing, speci ically he audi ing o
non- inancial epo ing. This a ea o audi ing, while no en-
i ely new, is conside ably less in es iga ed han he a ea o
inancial epo ing. Second, he hesis also con ibu es o he
li e a u e on Eu opean inancial epo ing equi emen s. In
pa icula , i connec s hose wo s eams o li e a u e. Thi d,
i o e s a me hodological con ibu ion by p o iding an up-
o-da e e iew o ex ual analysis me hods. Finally, a con i-
bu ion is made by p o iding e idence on whe he audi ing
imp o es he quali y o non- inancial epo ing. S akehold-
e s and o he ecipien s o non- inancial epo s can assess
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 203
he alue ha audi ing p o ides when making in es men de-
cisions. The indings can also suppo he EU Commission’s
decision on u u e assu ance le el inc eases.
1.3. P ocedu e o he wo k
This hesis is o ganized as ollows. Chap e 2 p esen s he
ele an heo e ical backg ound. Fi s , i consis s o he eg-
ula o y amewo k wi h a ocus on Eu opean accoun ing, in
pa icula he EU axonomy. Second, he es ablished heo ies
o sus ainabili y epo ing and audi ing in gene al a e p e-
sen ed. The heo e ical applica ion o ex ual analysis, which
is he cen al ins umen o his hesis, concludes he chap e
on he heo e ical backg ound.
The ollowing chap e s o m he i s o he wo main
pa s o his hesis. Chap e 3 discusses he ele an li e a-
u e in he domains o inance and accoun ing, while Chap-
e 4 p esen s he a ious me hods o ex ual analysis in e ms
o hei unc ionali y and applicabili y. These me hods a e
no only dis inguished acco ding o hei ield o applica-
ion, bu also be ween adi ional me hods and s a e-o - he-
a me hods, which u ilize he ecen echnical de elopmen s
in machine lea ning and a i icial in elligence. On he one
hand, he comp ehensi e p esen a ion o all cu en ly a ail-
able me hods o ms he necessa y g oundwo k o he second
pa o his hesis. On he o he hand, i o e s a con ibu ion
in i sel , since i can assis he audience o his hesis in iden-
i ying app op ia e me hods o own esea ch p ojec s in he
ield o ex ual analysis.
The second pa o he hesis in ol es u ilizing ex ual
analysis echniques o assess co po a e non- inancial epo -
ing. Fo his pu pose, Chap e 5 o mula es h ee ela ed hy-
po heses. Chap e 6 desc ibes he me hodology by discussing
he da a se , he esea ch design, and he p ocessing o el-
e an a iables ough ex ual analysis. The esul s o he
analyses a e p esen ed in Chap e 7. Finally, Chap e 8 con-
cludes he hesis and discusses he limi a ions o he wo k.
2. Theo e ical backg ound
2.1. De elopmen o he egula o y amewo k
The clima e c isis is one o he g ea es challenges o ou
ime. I s nega i e e ec s a e al eady being expe ienced o-
day and will ge wo se as hey become mo e di icul o mi -
iga e in he u u e (Uni ed Na ions, 2022). The majo i y o
Uni ed Na ions membe s a es a e commi ed o add essing
he clima e c isis h ough he Pa is Ag eemen , which aims
o limi he inc ease in global empe a u e o a maximum o
wo deg ees Celsius abo e p e-indus ial le els, aise o e all
adap a ion capabili ies o he impac s o clima e change, and
shi capi al lows o a clima e- iendly de elopmen (Eu o-
pean Union (EU), 2016, p. 5). Eu ope is con ibu ing h ough
he Eu opean G een Deal. This amewo k includes a p o-
g am o measu es o he necessa y ans o ma ion. The Eu-
opean Commission has se he goal o making Eu ope he
i s con inen o become clima e neu al by 2050 by educ-
ing g eenhouse gas emissions o ne ze o. The in e im a ge
is a educ ion o emissions by 55 % un il 2030 compa ed o
1990 le els. The Eu opean G een Deal also co e s issues such
as he sus ainable use o consume goods, wi h speci ica ions
o p oduce s o enable consume s o epai p oduc s mo e
easily and make hem las longe so ha he goods do no
ha e o be eplaced. O he pa s o he p og am co e he
ields o echnology, mobili y, ood, ene gy and biodi e si y,
and a ious o he subjec s (Eu opean Commission, 2019).
Co po a e go e nance is also speci ically add essed in he
Eu opean G een Deal. Companies a e s ill oo ocused on
sho - e m inancial pe o mance a he han sus ainable de-
elopmen . The e o e, companies mus inc easingly disclose
hei in o ma ion on sus ainabili y- ela ed issues alongside
hei annual epo ing in o de o in o m in es o s abou hei
de elopmen in hese a eas (Eu opean Commission, 2019,
p. 17).
The EU axonomy is pa o he Eu opean G een Deal and
is designed o accompany and suppo he ansi ion o he
en i onmen o he a ge s a e. The axonomy in oduces
a ious ins umen s o achie e his goal and is also supposed
o suppo he inancing o he ansi ion by di ec ing cap-
i al lows in a way ha is conduci e o he ansi ion. An-
o he in eg al pa o he EU axonomy is co po a e disclo-
su e (Eu opean Commission, 2020, p. 8). Regula ions e-
qui e wo g oups o companies in pa icula o add ess his
issue: Financial ma ke pa icipan s1o e ing inancial p od-
uc s wi hin he EU and companies mee ing he size c i e ia
o he NFRD. The equi ed con en , especially o he second
g oup o companies, is discussed in mo e de ail in Chap e
6.2 o his hesis. The in o ma ion mus be published ei he
in he non- inancial sec ion o he consolida ed o annual i-
nancial s a emen s o as a s and-alone non- inancial epo -
ing o sus ainabili y epo ing (Eu opean Commission, 2020,
p. 27). The EU axonomy encou ages, bu does no equi e,
companies o ob ain assu ance om ex e nal audi o s (Eu o-
pean Commission, 2020, p. 37).
E en be o e he in oduc ion o he EU axonomy, many
esea che s add essed hese subs an i e issues (Luca elli e
al., 2020, p. 6). Mo e ecen esea ch iden i ies he bene i s
o he axonomy mainly in he a ea o ha moniza ion and
in es men decision suppo (Dum ose e al., 2022, p. 2),
which is consis en wi h he epo ing objec i es o he IFRS.
I is also impo an o conside he ull scope o he axon-
omy. Beyond he en i ies di ec ly impac ed by he EU axon-
omy, o he en i ies a e also indi ec ly a ec ed (Dusík & Bond,
2022, p. 92). Supplie s and cus ome s which do no mee he
h esholds o manda o y NFRD epo ing do no ha e o col-
lec en i onmen al da a o hemsel es, bu may need o be
able o p o ide i o companies co e ed by he NFRD o hei
epo ing.
In p inciple, he equi emen o mo e comp ehensi e e-
po ing also leads o a educ ion in in o ma ion asymme ies.
Al hough his ela ionship exis s in heo y, i should no be
blindly assumed wi hou e idence and needs o be u he
1e.g. Equi y unds, exchange- aded unds, eal es a e unds, pension
schemes, en u e capi al and p i a e equi y unds.
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235204
in es iga ed (B eije and O ij, 2022, p. 350; H. Ch is ensen
e al., 2021, p. 1231).
The EU axonomy has one pa icula s eng h. By p e-
sc ibing he na a i e ha sus ainable ac i i ies can only be
conside ed as sus ainable i hey do no ha m o he sus ain-
able ac i i ies, ade-o s be ween di e en a eas o de elop-
men canno be used as a loophole. The bene i s o he EU
axonomy p esen ed in academic li e a u e may also ex end
beyond he Eu opean a ea. The EU se es as a p ominen
model o implemen ing egula ions, making i p obable ha
legisla o s ou side o Eu ope will adop hese o aise simi-
la equi emen s (Bloombe g, 2021; Dusík and Bond, 2022,
pp. 93, 96).
2.2. Fundamen al heo ies on sus ainabili y epo ing
Be o e dealing wi h he me hodology, he heo e ical
p inciples need o be de ined. Essen ially, he publica ion o
company da a is c ucial o capi al ma ke pa icipan s and
hei in es men decisions, wi h in o ma ion con en and
imeliness being pa icula ly impo an (Ball & B own, 1968,
p. 176). Fo his hesis, heo ies ha conside olun a y
disclosu e a e mos ele an . Fo a long ime, sus ainabil-
i y epo ing wi hin non- inancial epo ing has been on a
la gely olun a y basis, and companies only disclosed da a
when he bene i s exceeded he cos s associa ed wi h he dis-
closu e. The NFRD made sus ainabili y epo ing manda o y
o some companies, bu he egula ion s ill allows a g ea
amoun o lexibili y in he na u e and ex en o disclosu e,
which is why olun a y disclosu e heo ies a e especially
ele an .
Volun a y disclosu e e e s o a company’s decision o
publish supplemen a y in o ma ion beyond wha is equi ed
by law. The e a e a ious de e minan s ha in luence
whe he and how much olun a y disclosu e is made, in-
cluding i m cha ac e is ics, owne ship s uc u e, o coun y-
speci ic ac o s (Zamil e al., 2023, pp. 232–235). Fo he
pu poses o his hesis, howe e , he gene al heo ies, on
which olun a y disclosu e is based, a e c ucial.
Agency heo y, which is mos o en applied in he con ex
o olun a y disclosu e (Zamil e al. 2002: 239), is closely
linked o he well-known p incipal-agen p oblem om eco-
nomics, which is p ima ily ounded on in o ma ion asymme-
ies be ween pa ies (A ow, 1963, p. 967). Acco ding o
agency heo y, i ms olun a ily disclose in o ma ion in o de
o educe in o ma ion asymme ies be ween hemsel es and
hei s akeholde s and hus acili a e business ela ionships
o capi al lows.
In addi ion o he agency heo y, he nex wo heo ies
mos commonly used in his con ex a e legi imacy heo y
and s akeholde heo y. Legi imacy heo y is conce ned wi h
he in e ac ion be ween companies and hei social en i on-
men and pos ula es ha companies s i e o shape hei ac-
ions, decisions, and p ac ices so ha hey a e iewed as legi -
ima e and accep able by he socie y. Th ough olun a y dis-
closu e, companies seek o achie e he necessa y legi imacy
and gain he us o s akeholde s. The heo y is ounded on
he p emise ha he e is a social con ac be ween compa-
nies and socie y. Recen ly, inc eased awa eness o Co po a e
Social Responsibili y (CSR) conce ns has in luenced co po-
a e p ac ices in sus ainabili y epo ing, and companies ha e
used CSR disclosu e o gain legi imacy (Lepo e & Pisano,
2023, pp. 56–57). S akeholde heo y, on he o he hand,
emphasizes ha companies a e no only beholden o he in-
e es s o hei owne s, bu should also ake in o accoun
he in e es s o a b oade g oup o s akeholde s who a e a -
ec ed by he company’s ac i i ies. This heo y emphasizes
ha companies should ecognize he expec a ions, alues,
and needs o hei a ious s akeholde s. The e o e, olun a y
disclosu e can be seen as an e o o inc ease anspa ency
and add ess s akeholde in e es s and conce ns. In addi ion,
he e a e se e al o he heo ies on he basis o which ol-
un a y disclosu es can be use ul o companies (Zamil e al.,
2023, p. 239).
These heo ies explain di e en incen i es o companies
o olun a ily disclose in o ma ion. Fu he mo e, he olun-
a y disclosu e heo y conside s he cos s o disclosu e and
sugges s ha in o ma ion will be olun a ily p o ided only i
he bene i s o he company ou weigh he cos s o disclosu e.
Acco ding o his p inciple, in o ma ion ha is insigni ican
o disad an ageous o a company will no be disclosed (Ve -
ecchia, 1983, pp. 179, 192).
2.3. Fundamen al heo ies on audi
In addi ion o olun a y disclosu e heo ies, he p incipal
heo ies o audi ing a e pa icula ly ele an o his hesis.
Audi ing is one o he cen al a eas o accoun ing. The ex e -
nal e i ica ion o inancial o non- inancial in o ma ion by
an audi o can ensu e he eliabili y o epo ing o ex e -
nal s akeholde s. In e y simpli ied e ms, his is achie ed by
he audi i m de e mining he ac ual inancial posi ion and
pe o mance o a company h ough a ious audi p ocedu es
and compa ing hese wi h he igu es epo ed in he inan-
cial s a emen s (Wagenho e & Ewe , 2015, pp. 410–411).
The exac p ocedu e and s uc u e o he audi is no
he ocus o his hesis. Ins ead, he basic heo ies and e-
la ed conce ns a e add essed in o de o unde s and how
hey ela e o he audi o sus ainabili y epo ing. Again,
he p incipal-agen heo y is a undamen al heo y wi h sig-
ni ican impo ance. This beha io al heo y can be applied o
audi i ms and hei clien companies. The p incipal, ep e-
sen ing he company o be audi ed, hi es an agen , he audi
i m, o pe o m an audi o he company’s disclosu es. Wi h
a p ede e mined audi ee, he audi ing company lacks in mo-
i a ion o unde ake high cos s in he o m o a de ailed au-
di . Ins ead, he audi ing company seeks o maximize i s own
bene i by minimizing he audi e o , since he compensa-
ion emains he same. The clien and o he pa ies seeking
audi assu ance su e as a esul (An le, 1982, pp. 503, 508,
512). Howe e , his oppo unis ic beha io exis s only in he-
o y. In p ac ice, o he ac o s also in luence audi in ensi y.
Fo example, he audi esul i sel is e iewed by o he en i-
ies, and insu icien audi ac ions can be sanc ioned. Ne e -
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 205
heless, i is use ul o keep in mind he undamen al p oblems
ha a ise in audi ing and he applica ion o la ees.
Audi i ms use a ious o ms o audi ing p ocedu es o
de ec accoun ing manipula ion o unin en ional miss a e-
men s. The model s uc u e dis inguishes be ween subs an-
i e and sys ema ic audi p ocedu es. While subs an i e
audi p ocedu es p o ide assu ance on speci ic balance shee
i ems, o ins ance by sampling, sys ema ic audi p ocedu es
p o ide b oade assu ance, o example by es ing he unc-
ionali y o in e nal con ol sys ems. In mos cases, he
desi ed le el o assu ance is achie ed h ough a combina ion
o bo h ypes o p ocedu es (Wagenho e & Ewe , 2015,
pp. 432–435). Audi ing sus ainabili y epo ing is unique
in ha i conce ns non- inancial epo ing. Essen ially, non-
inancial epo ing p o ides mo e quali a i e in o ma ion
a he han ac ual numbe s, as i would be in he case o
inancial epo ing. Almos all audi i ms e e o he In e -
na ional S anda d on Assu ance Engagemen s (ISAE) 3000
(Re ised) when pe o ming sus ainabili y epo ing audi s.
This s anda d speci ically co e s he audi o in o ma ion ha
can be classi ied as non- inancial in o ma ion (In e na ional
Audi ing and Assu ance S anda ds Boa d, 2013, p. 5) and is
he e o e conside ed an umb ella s anda d. As he ISAE 3000
(Re ised) is applied o a wide ange o disclosu es, i does
no con ain explici audi p ocedu es. Ra he , i desc ibes
gene al equi emen s o audi i ms, such as in eg i y, in-
dependence, and p o essionalism, which a e also equi ed
o inancial audi s. I u he p o ides de ailed in o ma ion
on he con en and scope o he audi i m’s epo ing on i s
engagemen . Fo he ac ual audi , he s anda d p ima ily
equi es audi o s o e iew he con en o he quali a i e dis-
closu es o ma e ial inconsis encies (In e na ional Audi ing
and Assu ance S anda ds Boa d, 2013, pp. 20–21). How-
e e , he s anda d does no speci y how ma e iali y has o be
de e mined o how audi p ocedu es should be pe o med.
The e is no dispu e ha he audi in i sel is a aluable
ool. In gene al, audi ing inc eases he c edibili y o he in-
o ma ion disclosed, as shown, o example, by he ac ha
i ms wi h audi ed inancial s a emen s pay lowe in e es
a es han compa able i ms wi h unaudi ed inancial s a e-
men s (Blackwell e al., 1998, pp. 58, 68). I should be no ed,
howe e , ha he magni ude o such an e ec a ies depend-
ing on whe he he in o ma ion disclosed is a o able o un-
a o able o a company. Ano he a iable o pa icula im-
po ance o his hesis is he olun a iness o he audi . The
ollowing 2x2 ma ix illus a es ou possible condi ions ha
inancial o non- inancial epo ing can adop .
Acco ding o he a ibu ion heo y, inancial s a emen
use s challenge posi i e in o ma ion because i is consis en
wi h he company’s in e es s. Nega i e in o ma ion, on he
o he hand, is less challenged since i would no be eason-
able o companies o mis ep esen in o ma ion ha is no
in hei bes in e es . This heo y is con i med by p ac ice.
In expe imen s, Co am e al. show ha olun a y audi as-
su ance o posi i e sus ainabili y disclosu es has a signi ican
posi i e e ec on he sha e p ice. In con as , no signi ican
esul s we e ound o nega i e disclosu es. I can he e o e
be concluded ha inancial s a emen eade s ha e eliabili y
conce ns mainly when he disclosed in o ma ion is posi i e.
These conce ns a e consis en wi h a ibu ion heo y, which
sugges s ha i is bene icial o i ms o olun a ily unde go
an ex e nal audi when published in o ma ion is posi i e, in
o de o inc ease eliabili y o hose esul s, while nega i e in-
o ma ion al eady ca ies a highe le el o eliabili y (Co am
e al., 2009, pp. 145–148).
These esul s a e ele an o he esea ch o his hesis, as
he EU membe s a es’ op ion igh and he implemen a ion
wi hin Ge many allow companies o olun a ily subjec hei
non- inancial epo ing o an audi . Acco dingly, such a ol-
un a y submission can ha e di e en mo i a ions: The coun-
e ac ion o he p incipal-agen ela ionship, he c ea ion o a
highe eliabili y o he in o ma ion, especially i i is posi i e
and he e o e, acco ding o he a ibu ion heo y, mo e likely
o be doub ed by eade s o he epo , o simply he sa is-
ac ion o s akeholde s in o de no o be a a compa a i e
disad an age o o he companies (B adbu y, 1990, p. 33).
The p esen ed heo ies p o ide he basis o mul iple e-
sea ch s eams. They also o m he basis o he de elopmen
o he hypo heses o his hesis p esen ed in Chap e 5.
2.4. Tex ual analysis in esea ch
Resea ch in business economics hea ily elies on quan-
i a i e me hods o gaining new indings. The a ionale is
clea : coun less amoun s o da a exis in nume ical o m. Fi-
nancial s a emen s con aining balance shee s and p o i and
loss s a emen s, s ock p ices and a as ange o ela ed i-
nancial indica o s, as well as s a is ical in o ma ion on com-
panies, indus ies, egions, and coun ies. The amoun o
nume ical da a is eno mous. When his da a is e ec i ely
con ex ualized, new insigh s can be unco e ed. Howe e ,
how do esea che s handle da a ha is no numbe s, bu le -
e s? In addi ion o he balance shee , e e y inancial s a e-
men p o ides no es. The income s a emen enables insigh
in o ea nings, bu he managemen epo co e s e en mo e.
S ock p ices and inancial a ios a e pai ed wi h analys s’ ec-
ommenda ions and company announcemen s, bo h w i en
and e bal. Each s a is ical su ey is accompanied by a co -
esponding ex . All o his in o ma ion is easily o e looked.
Howe e , i would be inco ec o s a e ha ex ual da a
is no a opic o in e es in esea ch a all. In ac , his ield o
esea ch has been g owing in impo ance o some ime. As
a esul , bo h ea lie and mo e ecen pape s co e no only
esul s in his con ex , bu also he me hodology on i s own
(e.g. Bae e al. (2023), Bochkay e al. (2023), Gen zkow e
al. (2019), and Lough an and McDonald (2016,2020)).
The EU axonomy and especially he NFRD equi emen s
ha e g ea ly inc eased he olume o non- inancial epo ing.
This new in o ma ion in o m o ex ual da a p o ides po en-
ial o esea ch using ex ual analysis me hods. Howe e ,
i is impo an o ensu e ha he me hodology does no ake
p ecedence o e he ac ual esea ch ques ion (Bochkay e al.,
2023, p. 792; Bae e al., 2023, p. 3). The e o e, be o e ad-
d essing he hypo heses, he me hodology o ex ual analysis
will be examined in de ail based on he exis ing li e a u e.
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235206
Table 1: E ec s o audi on eliabili y based on he expe imen o Co am e al. (2009, pp. 142–145)
Posi i e in o ma ion con en Nega i e in o ma ion con en
Audi assu ance High eliabili y High eliabili y
No audi assu ance Low eliabili y High eliabili y
This e iew p o ides a summa y as well as an explana ion o
cu en me hods and highligh s hei ad an ages, disad an-
ages, and a eas o applica ion. This will no only iden i y
he app op ia e me hods o apply o he esea ch pu pose o
his hesis. I also p o ides a aluable con ibu ion as i sum-
ma izes he leading esea ch in a ious ields, pa icula ly in
he domain o inance and accoun ing.
Tex ual analysis has al eady been used o ind e idence
in se e al a eas. Chen e al. ound ha bo h s ock e u ns
and ea nings su p ises can be p edic ed by pee -based knowl-
edge on social media. They used one o he simples me hods
imaginable: coun ing nega i e conno ed wo ds in a icles
w i en by indi idual in es o s on he social media pla o m
Seeking Alpha. The a io o nega i e conno ed wo ds2 o he
o e all numbe o wo ds was u ilized o de e mine i s a neg-
a i e sen imen and hen a decline in s ock e u ns and e en
in ea nings su p ises. This e ec inc eased as he numbe o
nega i e wo ds inc eased (H. Chen e al., 2014, pp. 1368–
1369, 1382, 1400).
In a mo e ecen s udy, Sau ne e al. measu ed he ex en
o co po a e exposu e o clima e change by using an algo-
i hm o coun key wo ds3in ea ning call ansc ip s ha a e
di ec ly ela ed o his opic. This me hod cap u ed clima e
change exposu e om he pe spec i e o all key s akehold-
e s, as he ea ning call ansc ip s included bo h sha eholde
and s akeholde ques ions as well as managemen esponses
(Sau ne e al., 2023, pp. 1450–1451, 1492–1493).
Using a compa able me hodology, Chen and S ini asan
analyzed he 10-K epo s o non- ech i ms o in es iga e he
ela ionship be ween digi al ac i i ies, i m alue, and pe o -
mance. Speci ically, hey measu ed he equency o digi al
e ms4in he desc ip ion sec ion o hese epo s. The au-
ho s disco e ed ha non- ech i ms ha e gene ally inc eased
hei digi al ac i i ies o e ime, and ha g ea e in ol emen
in digi al ac i i ies has a posi i e impac on i m alue and
s ock pe o mance. These indings we e made possible by
quan i ying he deg ee o digi aliza ion wi hin i ms h ough
ex ual analysis (W. Chen & S ini asan, 2023, pp. 2, 10, 29,
35).
As a inal example, in a 2014 s udy, Pu da and Skillio n
analyzed qua e ly and annual inancial s a emen s o aud-
ulen ac i i ies. A mul ile el ex ual analysis p ocess i s
2Examples o nega i e conno ed wo ds a e loss, e mina ion, agains o
impai men .
3The key wo ds wi h he highes equency we e enewable ene gy, elec ic
ehicle, clean ene gy, new ene gy, clima e change and wind powe (Sau ne
e al., 2023, p. 1466).
4Examples o digi al e ms a e analy ics, i ual eali y, au oma ion, a i-
icial in elligence, big da a, da a science o digi aliza ion (W. Chen & S ini-
asan, 2023, p. 36).
so ed wo ds wi hin he sample by equency, hen es ed
hei p edic i e powe using a decision ee-based app oach,
and inally concluded om he epo s he p obabili y ha
he s a emen s we e comple ely ue and did no con ain
aud. The algo i hm based on ex ual analysis was able o
con i m he p esence o absence o audulen ac i i y in o e
82 % o he epo s (Pu da & Skillico n, 2015, pp. 1194,
1197–1200, 1218).
The lis ed esea ch is illus a i e o he wide ange o
possible applica ions o ex ual analysis. Ranging om
meaning ul, ma ke - ele an esul s in he a ea o inance, o
isk exposu e in he example o clima e change, o oppo uni-
ies o companies in he example o echnology adap a ion,
o ele an accoun ing issues like aud de ec ion, he appli-
ca ion possibili ies a e unlimi ed. The ex ual da a examined
anges om indi idual social media pos s o ansc ibed
communica ions be ween companies and s akeholde s o o -
icial co po a e disclosu es in annual and qua e ly inancial
s a emen s. This demons a es ha ex ual da a can con ain
ele an in o ma ion in any concei able o m, ega dless o
i s ype and na u e.
In quan i a i e esea ch, he app oach is usually ela i ely
s aigh o wa d. Wi h he e alua ion o a sample using s a-
is ical me hods in di ec ela ion o a hypo hesis, esea che s
in end o ob ain signi ican indings. The p ocedu e in ex-
ual analysis is no as simple, as he da abase is ini ially qual-
i a i e. The in o ma ion equi ed o esea ch can only be
ob ained in an exploi able o m by means o an app op ia e
ans o ma ion (Lough an & McDonald, 2016, p. 1191).
The majo di icul y is no ha ex ual da a is less s uc-
u ed o p esen ed in a di e en way, bu a he ha i has a
high dimensionali y. The base o he dimensions is de ined by
he numbe o di e en wo ds in a language, and he expo-
nen by he numbe o wo ds in he ex , as shown in Equa-
ion 1. When aking a ex ha consis s o only en wo ds,
and i is w i en in a ic ional language ha also only pos-
sesses en di e en wo ds, hen his ex can ha e en billion
di e en dimensions, each o which is di e en om he o h-
e s. In eali y, ex s a e much longe han en wo ds, and
languages consis o mo e han en di e en wo ds, so bo h
he exponen and he base, and hus he o al numbe o di-
mensions, ake on an unimaginably high deg ee (Gen zkow
e al., 2019, pp. 535–536).
=nl(1)
= ex ual da a dimensions
l=language wo d op ions
n=leng h o ex in wo ds
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 207
Humans can handle he high numbe o dimensions because
hey a e no impo an when eading ex . Wo ds a e pe -
cei ed and in e p e ed in he con ex o o he wo ds. Ac-
co dingly, sen ences do no ep esen a sequence o indepen-
den a iables. In ex ual analysis, howe e , hese dimen-
sions a e impo an and mus be add essed. A he beginning
o any ex ual analysis, he numbe o dimensions conside ed
should be d as ically educed in o de o deal wi h he eno -
mous amoun o da a. This educ ion is usually pe o med
wi hin h ee s eps. The i s s ep is o di ide he o al olume
o ex in o sec ions sui able o esea ch. In he la e cou se
o his wo k, non- inancial epo s om di e en companies
a e analyzed. I is no necessa y o examine he epo s o
all companies oge he . Ra he i is su icien o ex ac in-
o ma ion om each epo sepa a ely. This in o ma ion can
hen be used o d aw conclusions by applying o he esea ch
echniques. By analyzing indi idual ex s sepa a ely ins ead
o pe o ming one single o e all analysis, he numbe o di-
mensions can be d as ically educed. In a second s ep, ce -
ain pa s o he ex can be excluded om he analysis. These
a e i s o all equen ly occu ing wo ds ha main ain he
g amma ical s uc u e o a ex . Such wo ds a e impo an
o human eade s o a ex , bu con ain li le o no in o ma-
ion ha will eme ge in he ex ual analysis. In addi ion, o
some esea ch i can also be use ul o exclude wo ds ha oc-
cu e y a ely in a ex . Al hough hese wo ds may con ain
ele an in o ma ion, he bene i o gaining his in o ma ion
could be ou weighed by he addi ional e o in ol ed in an-
alyzing hese wo ds. In a inal s ep, he s emming me hod
can be used o adjus all wo ds ha ha e he same meaning
bu a e spelled di e en ly. This me hod uni ies di e en ly
conjuga ed wo ds by emo ing hei su ixes. Fo example,
he wo ds connec ed, connec ing, connec ion, and connec ions
ha e he same in o ma ional meaning. By eplacing hem
wi h hei s em wo d connec , he dimensional base o he
o e all ex is educed once again (Po e , 1980, p. 130). The
impo an aspec is o dec ease he numbe o di e en wo ds
wi h iden ical in o ma ion con en . Wi h hese h ee s eps,
he dimensions o a ex can be d as ically educed by lowe -
ing he base no he dimension equa ion (Gen zkow e al.,
2019, pp. 537–538).
The e o s o simpli ica ion a e add essing he base n
o Equa ion 1. F om a pu ely ma hema ical poin o iew, a
educ ion o he exponen would be mo e e ec i e. Howe e ,
i is no as easy o educe he exponen . Tex ual da a om
a sample can be dec eased o simpli ied a he expense o
in o ma ion loss. Bu he ocabula y o he language in which
a ex is w i en is exogenous.
In ex ual analysis, ins ead o ying o educe he expo-
nen , o en he en i e o mula ge s modi ied. The so-called
bag o wo ds me hod igno es he posi ion o wo ds in a ex .
Al e na i ely, i only coun s whe he and how o en indi id-
ual wo ds occu . This implies ha he numbe o possible
dimensions only esul om he mul iplica ion o he num-
be o wo ds n in he ex by he numbe o possible wo ds o
alanguage l. Whe eas be o e, a ex o en wo ds leng h in a
language con aining only en di e en wo ds al eady had en
billion dimensions. Using he bag o wo d me hod a ex in
English language5wi h he same amoun o dimensions can
con ain mo e han 100,000 wo ds. Thus, igno ing he o de
o wo ds in ex s leads o a massi e educ ion o he dimen-
sionali y o a ex (Gen zkow e al., 2019, pp. 539–540).
A e p esen ing a gene al o e iew o how ex ual anal-
ysis wo ks, he nex s ep is o speci y i s applica ion a eas.
Tex con ains some in o ma ion, bu wha kind o aluable
in o ma ion is included and how can i be ex ac ed? P io
esea ch has es ablished a ious applica ions o ex ual anal-
ysis o in o ma ion acquisi ion, which will be p esen ed in
he ollowing li e a u e e iew o he inance and accoun -
ing domain, which does no p esen all he li e a u e, bu he
mos impo an in e ms o he objec i e o his hesis.
3. Li e a u e e iew
3.1. Readabili y
Readabili y is one o he main a eas o use in ex ual anal-
ysis. Depending on he con ex , he de ini ion o eadabili y
a ies. Ei he way, i should somehow de e mine i ex is
designed in a way ha eade s can ecognize and comp e-
hend he unde lying message (Lough an & McDonald, 2016,
p. 1188). Mo e speci ically, eadabili y ep esen s he ela-
ionship be ween a ex and he cogni i e load equi ed o
unde s and i (Ma inc e al., 2021, p. 143). E en i a ex is
gene ally comp ehensible o a eade , a high cogni i e load,
o in simple e ms, a ex ha is challenging o ead, may
indica e poo eadabili y. The eadabili y o a ex always
should be conside ed in he con ex o he a ge audience
(Lough an & McDonald, 2020, p. 28). The Uni ed S a es Se-
cu i ies and Exchange Commission (SEC) suppo s his po-
si ion (Uni ed S a es Secu i ies and Exchange Commission
(SEC), 1998, p. 9). The Plain English Handbook published by
he SEC desc ibes he linguis ic o m in which publica ions
should be made. This includes he annual inancial s a e-
men s and non- inancial epo ing componen s. The hand-
book ecommends, among o he hings, he use o e e yday
language wo ds and sho sen ences. I u he ecommends
o pe o m au oma ic eadabili y checks by using o mulas
de eloped o his pu pose, bu also manual es ing by simple
human p oo eading o own publica ions (Uni ed S a es Se-
cu i ies and Exchange Commission (SEC), 1998, pp. 18, 57).
This hesis examines non- inancial epo ing wi h ocus on
sus ainabili y epo ing wi hin he Ge man ma ke , al hough
Eu opean o o he in e na ional egula o s ha e objec i es
o publica ion equi emen s simila o he SEC.
The eadabili y o ex has been subjec o many s udies.
Li’s widely ci ed pape examines he ela ionship be ween he
eadabili y o annual epo s and company ea nings. He e,
eadabili y was measu ed by wo a iables, he so-called Fog
Index and he leng h o he epo s. The Fog Index is closely
5The e a e se e al unde lying bases o de e mining he numbe o English
wo ds. The ollowing example is calcula ed using he numbe o 88,500
English wo ds (Nagy & Ande son, 1984, p. 320).
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235208
ela ed o he opic o eadabili y and will be discussed in
Chap e 4.2.1 in mo e de ail. Li ound ha companies wi h
a low eadabili y sco e on hei epo s had wo se ea nings
pe sis ence. On he o he hand, companies wi h easily ead-
able annual epo s a e mo e pe sis en . This co ela ion
could sugges ha managemen is hiding nega i e in o ma-
ion abou i s company by making he in o ma ion in he e-
po s mo e di icul o access (Li, 2008, pp. 222, 225–226,
244–245).
Repo ing quali y can also in luence in es men alloca-
ion. Biddle e al. ound ha companies wi h highe e-
po ing quali y a e less likely o be o e - o unde in es ed.
O e - o unde in es men occu s when he ma ginal bene i
o a capi al in es men is lowe han he ma ginal cos o ha
in es men . In o ma ion asymme ies be ween managemen
and in es o s in capi al ma ke s a e one main eason o such
ine icien alloca ions. Highe epo ing quali y can educe
he occu ence o ad e se selec ion and i s e ec s. Biddle e
al. use he Fog Index o measu e epo ing quali y and ind
a posi i e ela ionship be ween inc easing epo ing quali y
and dec easing o e - o unde in es men (Biddle e al., 2009,
pp. 113–114).
No only he gene al alloca ion e iciency can be ela ed
o eadabili y, bu also indi idual ading beha io . Mille
inds ha sha es o companies wi h less eadable epo s a e
aded less equen ly. This e ec is mos e iden among
small in es o s and can be explained by he highe cos o
in o ma ion acquisi ion, which is pa icula ly impo an o
such in es o g oups (Mille , 2010, pp. 2108, 2114, 2138).
Law ence ound simila esul s in ha in es o s a e mo e
likely o hold on o sha es o companies wi h mo e eadable
inancial disclosu es. Fo he inc ease o one s anda d de i-
a ion in eadabili y, s ock e u ns inc ease by 91 basis poin s
on a e age (Law ence, 2013, pp. 131, 135, 141–142, 144).
These wo s udies use he Fog Index and ex leng h as ead-
abili y measu es.
Readabili y esea ch has also p o ided insigh s in he do-
main o accoun ing. Chychyla e al. ound a ela ionship be-
ween epo ing complexi y and accoun ing expe ise wi hin
companies. They app oxima e accoun ing complexi y by a -
ious pa ame e s, including i m cha ac e is ics such as i m
size and numbe o segmen s co e ed, as well as inancial e-
po ing a iables such as he numbe o wo ds in 10-Ks and
hei eadabili y. The le el o accoun ing expe ise is app ox-
ima ed h ough boa ds o di ec o s and audi commi ees.
Mo e speci ically, he numbe o accoun ing expe s6in hese
unc ions ep esen he le el o accoun ing expe ise wi hin a
company. Chychyla e al. a gue ha companies wi h a high
le el o epo ing complexi y also ha e a highe le el o ac-
coun ing expe ise. This expe ise should coun e ac he neg-
a i e e ec s o accoun ing complexi y and is expec ed o ac-
i ely manage accoun ing complexi y (Chychyla e al., 2019,
pp. 227–229, 233–236, 247–248).
6An indi idual is conside ed an expe i he o she is a ce i ied public
accoun an (o simila ) o has p o essional expe ience in ele an a eas
such as easu y o audi ing.
Ano he example o how companies can in luence hei
own epo ing is he s udy by Chak aba y e al. on he e-
la ionship be ween execu i e compensa ion and disclosu e
anspa ency. The s udy concludes ha i ms wi h man-
age s ecei ing highe isk incen i es, measu ed by he s ock
op ion compensa ion, p oduce less comp ehensible 10-K e-
po s. This is because hese incen i es encou age manage s
o unde go isky p ojec s wi h highe ewa ds, which may
no be in line wi h he company’s s a egy. Managemen
a emp s o camou lage he unde aking o such p ojec s
by making he epo ing less eadable. He e, eadabili y is
assessed h ough he size o 10-K epo s, as e idenced by
Lough an and McDonald (2014). The esul shows ha com-
panies in he op qua ile o he s ock op ion egas7publish
epo s ha a e 15.4 % la ge . The esul s we e es ed o
obus ness ia a iables such as i m complexi y, while o he
es ing, such as measu ing eadabili y ia he Fog Index also
suppo ed he esul s (Chak aba y e al., 2018, pp. 3, 5–7,
10–11, 13, 25).
A ecen s udy by Do lei ne e al. examines eadabili y,
among o he issues, in a se ing simila o he one in his he-
sis. Do lei ne e al. in es iga e he impac o he Gene al
Da a P o ec ion Regula ion (GDPR) on p i acy s a emen s.
Jus like he NFRD, he GDPR was published as a di ec i e
in he EU and became binding law in he o m o a egula-
ion in 2018. Using me hods simila o he Fog me hod, p i-
acy s a emen s we e es ed o eadabili y. The esul was a
wo sening o eadabili y due o he in oduc ion o he GDPR.
E en when conside ing he numbe o wo ds as a eadabili y
measu e, he e was a dec ease in eadabili y due o an in-
c ease in he leng h o p i acy s a emen s (Do lei ne e al.,
2023, pp. 1–2, 4, 10–12).
The pape s p esen ed ound o he li e a u e e iew in
he ield o inance and accoun ing o he applica ion a ea o
eadabili y, wi h he esea ch a ea being cons an ly expanded
and, abo e all, newe s a e-o - he-a me hods being inc eas-
ingly used.
3.2. Sen imen analysis
In he con ex o ex ual analysis in accoun ing, sen imen
analysis inds e en mo e in e es han he opic o eadabili y
(Bochkay e al., 2023, p. 797). In sen imen analysis, ex s
a e examined o de e mine whe he hey ha e a posi i e o a
nega i e one. This has al eady p oduced indings in a wide
a ie y o esea ch a eas, e en in a un ela ed ields.
Fo example, Che alie and Mayzlin examine he sen i-
men o cus ome e iews o books in he wo la ges on-
line books o es using a di e ences-in-di e ences app oach
and ind ha e iews wi h posi i e (nega i e) sen imen lead
o signi ican ly highe (lowe ) sales on he espec i e si e.
They also ind ha e iews wi h nega i e sen imen ha e a
7Vega measu es how he alue o a s ock op ion changes as he ola ili y o
he unde lying asse changes (Black & Schloes, 1973, pp. 638–639). The
ega pa ame e is used because i is expec ed ha as ola ili y inc eases,
managemen will ake on iskie p ojec s in o de o inc ease he alue o
hei own op ions.
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 215
bu disca d hem al oge he i hey occu in a la ge ac ion
o he sample (Hobe g & Phillips, 2010, p. 3782).
Finally, i is c ucial o conside he ex leng h. The
g ea e he leng h o he ex s o be compa ed, he highe
he likelihood o common wo ds occu ing in bo h ex s, hus
inc easing hei simila i y (S. B own & Tucke , 2011, p. 317).
The e o e, longe ex s will na u ally ha e a highe simila i y
han sho e ones. This can be coun e ac ed by in eg a ing a
co ec ion a iable adap ed o he espec i e s udy (S. B own
& Tucke , 2011, pp. 343–344) o by no malizing he ec o s
so ha all ec o s o a s udy ha e he same leng h (Hobe g
& Phillips, 2010, p. 3809). Tex leng h i sel can also be used
as an indica o o simila i y. B own e al. examine spillo e
e ec s in quali a i e co po a e disclosu es. Speci ically, hey
ind ha companies change hei disclosu es mo e i he in-
dus y leade , di ec compe i o s, o an indus y pee wi h
he same audi o ecei es commen s om egula o s, e en
i hei own disclosu es we e no c i icized (S. B own e al.,
2018, pp. 623–625). To ge o his inding, B own e al.
compa ed he absolu e change in he numbe o wo ds in
isk disclosu es om one yea o he p e ious yea , assuming
ha a change in he numbe o wo ds also indica es a change
in he in o ma ion con ained (S. B own e al., 2018, pp. 631,
636).
Chap e 4.2 ou lined he undamen al p ac ices o adi-
ional ex ual analysis. The ollowing chap e es ablishes he
me hodological p inciples o NLP and machine lea ning o a
deepe unde s anding o he subjec be o e a simila p esen-
a ion o he s a e-o - he-a me hods is p o ided in Chap e
4.4.
4.3. De elopmen o na u al language p ocessing and ma-
chine lea ning
Tex ual analysis has been u ilized in academic esea ch
o a long ime, bu he ad an ages o e ed by NLP ha e
b ough he ield o an ad anced s age. No all s a e-o - he-
a me hods make use o NLP, bu i o ms he basis o many
newe me hods. The e o e, be o e lis ing he s a e-o - he-a
me hods in he a eas o eadabili y, sen imen analysis, and
disclosu e quan i y and simila i y, he amewo k a ound NLP
and ela ed concep s will be u he examined.
NLP o e s he possibili y o wo k wi h ex in a ious
ways. In gene al, NLP models unc ion by aking gi en ex-
ual da a as an inpu , ans o ming i , and p esen ing a new
ou pu as a esul . In his ans o ma ion, NLP models di -
e in he way hey ope a e. A i s ca ego y includes ule-
based models. The ans o ma ion o a ex in hese mod-
els is pe o med by manually de eloped ules. Fo ins ance,
hese ules may coun he numbe o ce ain wo ds o ex
elemen s. Keywo ds can be coun ed o iden i y speci ic con-
en s o a ex . Nex o hem, wo ds de ined as complex
o he numbe o sen ences can be coun ed. I can also be
help ul o coun wo ds ha ha e been p e iously assigned
o ca ego ies, such as wo ds wi h posi i e o nega i e sen-
imen (Bochkay e al., 2023, p. 769). These simple ans-
o ma ions a e hus s uc u ed simila o adi ional models
like he Fog Index o adi ional sen imen analysis me hods.
Mo e complex a e models like he VSM. He e, he inpu is
modi ied by ep esen ing ex ual da a as a ec o , which is
hen ans o med in a ule-based manne in a second s ep,
o example o pe o m a simila i y analysis (Bochkay e al.,
2023, p. 771). Howe e , NLP is no es ic ed o hese speci ic
applica ions, bu also acili a es inno a i e applica ions u i-
lizing machine lea ning models ha exceed he cu en ones
p esen ed.
Machine lea ning is a p ocess whe e an algo i hm gen-
e a es a model om p e-exis ing da a. The inpu da a and
desi ed ou pu da a a e de ined, and he model a emp s o
ma ch he inpu da a o he co esponding ou pu da a us-
ing de ined ules. Once ained on he gi en aining da a,
he model is expec ed o be capable o applying his p ocess
o new da a se s. The aining da a is ypically o med o a
small bu ep esen a i e po ion o he o e all da a se o en-
su e ha he machine lea ning algo i hm wo ks well on all
da a (Zhou, 2021, pp. 3–4). Machine lea ning is hus clas-
si ied as a i icial in elligence, al hough, unlike many o he
a i icial in elligence applica ions, i elies on his o ical da a
o aining da a. F om his da a, i is possible o iden i y pa -
e ns ha can be used o e en p edic ion o classi ica ion
asks (Alloghani e al., 2020, pp. 3–4).
The e a e a ious machine lea ning models ha a e ap-
plied in he domain o ex ual analysis. One o hem is he
Nai e Bayes model. The Nai e Bayes model is a p obabilis-
ic gene a i e model ha calcula es ou pu s based on condi-
ional p obabili ies. I is mainly used o ex classi ica ion
o opic de ec ion, which is an a ea o analysis ha was no
men ioned in he adi ional me hods, lis ed in Chap e 4.2,
bu is s ill used in ex analysis. Fo example, B own e al. use
opic de ec ion o de e mine whe he i is possible o make
assump ions abou he p obabili y o aud cases based on
company disclosu es. They do no e e o measu es such as
eadabili y o sen imen analysis, bu speci ically o he con-
en o he disclosu es and whe he his can be exploi ed wi h
he help o machine lea ning echniques o gene a e obus
p obabili ies o he exis ence o aud (N. B own e al., 2020,
pp. 238–239).
In he Nai e Bayes model, simila o he bag o wo ds
me hod, a ex is conside ed as a ec o . Howe e , in his
model, he ex is ep esen ed as a bina y ec o , so he e-
quency o one and he same wo d is no ele an . The model
now equi es ex s as aining da a. F om his da a, he model
is able o de e mine he p obabili y o ce ain pa ame e s,
in his case wo ds, occu ing in a ca ego y. The p obabili-
ies de e mined in he aining phase can be ans e ed o
he subsequen p edic ion phase in o de o assign unca e-
go ized ex s o he ca ego y ha he model conside s mos
likely. This is he ca ego y o ex s ep esen ing mos simila
ec o s. The Nai e Bayes model ge s i s name because i is
ounded on he nai e assump ion ha each p obabili y o he
occu ence o a wo d is independen om he occu ence o
any o he wo d. Thus, he join p obabili y o se e al speci ic
wo ds occu ing is equal o he p oduc o he p obabili ies o
he indi idual wo ds, which would no be he case in a eal
se ing (Agga wal, 2018, pp. 123–125).
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235216
Ano he example o machine lea ning is he nea es
neighbo classi ie . This model classi ies a iables such as
ex o sepa a e ex componen s in o ca ego ies, simila o
he Nai e Bayes model. The majo di e ence is ha he
nea es neighbo classi ie does no equi e a lea ning phase,
bu pe o ms i s classi ica ion decisions based on he ain-
ing da a only. A a iable ha is no included in he aining
da a se ge s assigned o he ca ego y in which an al eady
ca ego ized a iable is loca ed, which in he case o ex ual
analysis would be he ex om he aining da a se ha
was p e iously ca ego ized and has he smalles de ia ion,
hus ep esen ing he nea es neighbo . The de ia ion is
calcula ed using cosine simila i y (Agga wal, 2018, p. 133).
Tex eg ession is ano he popula machine lea ning ap-
plica ion in ex ual analysis. Due o he high dimensionali y
o ex ual da a, as discussed in Chap e 2.4, common eg es-
sion me hods, such as o dina y leas squa es eg ession, a e
no sui able. The la ge numbe o di e en English wo ds,
which ep esen s he numbe o pa ame e s in a eg ession,
usually e en exceeds he numbe o obse a ions in a sam-
ple. Ins ead, nonlinea eg essions can be pe o med. The so
called classi ica ion and eg ession ees model is cons uc ed
by i e a ing a ex h ough all a ailable b anches o he de-
cision ee. The ex is s ipped down o he mos in o ma-
i e ea u es, which a e he wo ds iden i ied as mos ele an .
This can be done wi h he help o speci ic dic iona ies. The
ea u es a e hen g ouped and i e a ed h ough he decision
ee, whe e he algo i hm iden i ies mo e ea u es ha can
help wi h he ca ego iza ion. A each le el o he ee, he
algo i hm selec s ea u es ha bes con ibu e o he sepa-
a ion o ca ego ies, esul ing in inc easingly speci ic c i e ia
o classi ica ion. When a new da a poin , in his case a ex
ou side o he aining da a, is added o classi ica ion, i a -
els along he b anches based on he p esence o absence o
ce ain c i e ia, in his case speci ic wo ds, and ends up a he
end o a b anch ha de e mines he classi ica ion. In some
models, he da a poin can also land a mul iple ends, which
a e hen weigh ed (Bochkay e al., 2023, pp. 771–772). Such
classi ica ion and eg ession ees can be used in sen imen
analysis, o example. The classi ica ion and eg ession ee
model enables he de ec ion o co ela ions in he o m o
a nonlinea eg ession by di iding a iables in o domains in
which hey a e homogeneous. Rela ionships a e no mea-
su ed along one a iable, bu a he by disc e e ea u es. In
he case o ex ual analysis, hese ea u es a e wo ds and a e
e alua ed wi hin he con ex o an associa ion, such as sen i-
men .
The models p esen ed so a belong o he supe ised ma-
chine lea ning g oup. This means ha he models ope a e in
such a way ha he aining da a is al eady co ec ly labeled
and he models he e o e ha e a bluep in o which hey can
e e . In con as , he e is unsupe ised machine lea ning,
in which case he model has o ecognize pa e ns wi hou
classi ied aining da a (Alloghani e al., 2020, p. 4).
Topic modeling is one o he unsupe ised machine lea n-
ing models used in ex ual analysis. The mos popula subse
is LDA, which was b ie ly in oduced in Chap e 3.3. In his
unsupe ised machine lea ning model, he algo i hm de ec s
opics wi hin a ex by using p obabilis ic me hods o iden-
i y wo ds ha a e ela ed o a opic. The algo i hm is he e-
o e used in opic de ec ion wi hin ex s, bu also o simi-
la i y analysis, since ex s wi h simila iden i ied opics a e
assumed o ha e a highe simila i y (Bochkay e al. 2022:
772).
All o hese supe ised and unsupe ised models a e a-
di ional models o machine lea ning. They o e ad an ages
o e some o he ex ual analysis me hods, bu also show hei
own sho comings. Fo example, adi ional machine lea n-
ing models ha e ouble ecognizing complex con ex s in he
lea ning p ocess. This can esul in an inco ec o insu i-
cien algo i hm ha does no gene a e aluable ou pu s. An-
o he weakness is ha use s manually de ine he in es iga ed
ea u es. Fo example, o eadabili y, he numbe o cha ac-
e s o syllables a e de ined as examina ion a iables. Finally,
i is necessa y o ain he models, which equi es bo h ime
and he a ailabili y o a sui able aining da a se . Deep lea n-
ing me hods can o e come hese weaknesses (Bochkay e al.
2022: 772-773).
A he beginning o his chap e , he unc ionali y o NLP
models was desc ibed, in which inpu da a, such as ex ual
da a, is ans o med and subsequen ly ep esen ed in a mod-
i ied o m as ou pu da a. The models he e o e ha e h ee
dis inc laye s: an inpu laye o he inpu o he da a, a
second laye in which a ans o ma ion p ocess is applied ac-
co ding o he me hods o he models desc ibed, and a hi d
ou pu laye o he display o he da a.
The p ocess o deep lea ning is compa able, bu a ies in
he middle o he model s uc u e, be ween he inpu laye
and he ou pu laye . Ins ead o a s aigh o wa d ans o -
ma ion unc ion, a so-called hidden laye is used. This laye ,
in u n, can consis o se e al laye s ha a e in e connec ed
(Agga wal, 2018, p. 326). The hidden laye ep esen s a
ma hema ical unc ion ha compu es ou pu alues om in-
pu alues. This unc ion i sel is composed o se e al sim-
ple unc ions, he indi idual laye s. I is he dep h o he
laye s wi hin he model ha gi es deep lea ning i s name.
The mo e laye s a model has, he mo e complex asks i can
sol e. Each laye pe o ms a unique unc ion in he o e all
ans o ma ion (Good ellow e al., 2016, pp. 5, 8). Figu e 2
shows a simpli ied deep lea ning model whe e a speci ic ou -
pu pa ame e is de e mined om a ious in ege a iables.
An example use case would be he ca ego iza ion o ex ual
da a. The hidden laye he e has a dep h o wo and can be
ex ended many old in mo e complex models.
Such models a e also called A i icial Neu al Ne wo ks
(ANN) because hey esemble he neu al ne wo k o he hu-
man b ain (Bochkay e al., 2023, p. 773). The in e sec ions
be ween laye s a e connec ed o o m a ne wo k h ough
which in o ma ion passes. Deep lea ning me hods consis ing
o ANNs ind applica ion in he p ocessing o isual ma e-
ial such as images and ideos, audio ma e ial such as audio
acks, and also In he p ocessing o ex and speech. Espe-
cially in ex ual analysis, Deep Lea ning o e s eno mous pos-
sibili ies o he u u e, by conside ing wo ds and sen ences
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 217
Figu e 2: Mul ilaye neu al ne wo k example (Agga wal, 2018, p. 327)
in he con ex o he whole ex (LeCun e al., 2015, pp. 436,
442).
The ANNs can be designed o p ocess ex ual da a as a
simple ec o . A mo e eliable use, aking in o accoun he
con ex o indi idual wo ds, can be achie ed by in eg a ing
loops in o he s uc u e o he ANNs. Inpu s and ou pu s a e
no conside ed as single a iables (wo ds), bu as dependen
sequen ial a iables (sen ences o ex segmen s). Such mod-
els a e use ul, o example, in ansla ion applica ions o he
in design o an a i icial in elligence which is able o answe
ques ions (Agga wal, 2018, pp. 342–343, 350–351). In ad-
di ion, he e a e models ha calcula e ou pu ia so-called
a en ion. He e, he simila i y o he inpu o di e en ec-
o se ies which con ain in o ma ion is calcula ed i s . The
highe he simila i y, he highe he assigned weigh . The
sum o he weigh ed ec o s hen p o ides in o ma ion abou
he measu e o a en ion, allowing he model o ocus on ce -
ain pa s o he ex depending on which in o ma ion is im-
po an o he gi en inpu . Vaswani e al. p opose ha a -
en ion weigh ing p o ides be e ou pu p edic ions han us-
ing loops in ecu en neu al ne wo ks (Vaswani e al., 2017,
pp. 2, 3, 5, 9).
The a en ion mechanism is used in s a e-o - he-a la ge
language models (LLM). LLMs a e NLP models ha ha e
la ge neu al ne wo k a chi ec u es and a e ained on la ge
se s o ex ual da a. Open AI’s gene a i e p e- aining (GPT)
language model Cha GPT is ecei ing emendous a en ion.
Immedia ely a e i s launch in No embe 2022, i was he
as es g owing consume applica ion a he ime and is now
among he op 20 isi ed websi es wo ldwide wi h 1.5 billion
mon hly use s (Reu e s, 2023, p. 1). Such GPT models a e
able o pe o m simila i y assessmen s o o classi y ex s, bu
mos o all hey a e known o hei abili y o answe ques-
ions h ough ex gene a ion. The models a e buil using
a combina ion o unsupe ised p e- aining and supe ised
ine- uning. The unsupe ised p e- aining is pe o med by
aking a la ge amoun o unlabeled ex s as aining da a and
ans o ming hem in o ou pu s using a mul i-laye ans-
o me decode . The mul i-laye ans o me decode wo ks
acco ding o he a en ion mechanisms desc ibed be o e,
whe e he ans o ma ion akes place o e se e al laye s
in o de o pe o m a s ep-by-s ep e inemen o he in e -
p e a ion o he ex , om simple o complex con ex ual
ela ionships. Finally, he ine- uning is ca ied ou wi h la-
beled ex ual da a o he di e en asks o such a model.
The combina ion o unsupe ised p e- aining using he a -
en ion mechanism and supe ised lea ning a he ask le el
helps o b ing he pe o mance o GPT models o a new le el
(Rad o d e al., 2018, pp. 2–4, 8).
Nex o Cha GPT is he Bidi ec ional Encode Rep esen-
a ions om T ans o me s (BERT). BERT is an LLM wi h e -
sa ile applica ion possibili ies. Like Cha GPT, BERT is buil in
wo s eps. The i s s ep o p e- aining is done using a mul i-
laye bidi ec ional ans o me . The main di e ence be ween
he models is he di ec ion in which hey gene a e ex . In
Cha GPT, he ex is gene a ed oken11 by oken o wo d by
wo d om le o igh , as a human would ead i . BERT uses
he Masked Language Model (MLM) ins ead. In he MLM,
andom okens o wo ds a e masked in p e- aining, making
hem un ecognizable o he model, so ha hey can be p e-
dic ed based on he su ounding wo ds. The ad an age o
MLM is ha ex s a e no only gene a ed om le o igh so
ha only he p eceding wo ds in he p e- aining in luence
he p edic ions, bu also he ollowing wo ds. The second
s ep o ine- uning is pe o med as in he GPT models, using
labeled ex ual da a ha is equi ed o he pa icula appli-
ca ion. The p e- ained e sion o BERT is designed so ha
ine- uning can be pe o med by adding a single addi ional
laye o he ANN, allowing i o be buil on op o he base
model wi h minimal e o (De lin e al., 2019, pp. 4171–
4174).
Bo h models a e p e- ained wi h a e y la ge amoun
o da a and hus can be e e ed o as LLMs. The e is no
11 Tokens a e sequences o le e s ha a e somewha simila o wo ds. On
a e age, 100 okens co espond o app oxima ely 75 English-language
wo ds (h ps://pla o m.openai.com/ okenize ).
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235218
clea bounda y a which NLP models a e conside ed as a
LLM. Chelba e al. ha e designed a benchma k o language
modeling ha con ains one billion wo ds (Chelba e al.,
2013, pp. 1–5). When using such a huge benchma k, i is
easonable o desc ibe a model as an LLM. Cha GPT uses
he BooksCo pus12 da abase o p e- aining, which con-
ains abou one billion wo ds, compa able o he Chelba e
al. benchma k. BERT is p e- ained wi h he BooksCo pus
da abase as well as wi h English Wikipedia and hus has ac-
cess o well o e h ee billion wo ds. Bo h models do no
use he Chelba e al. benchma k because i p o ides a shu -
led sen ence-le el co pus. The sen ences and wo ds wi hin
he da abase ha e been andomly dis ibu ed, so ha he
models ha access he benchma k only conside he ac ual
ela ionship be ween he wo ds, and no he na u al o de o
hose wo ds (Chelba e al., 2013, p. 2). Howe e , his o de
is undamen ally ele an in he case o Cha GPT and BERT.
Th oughou he use o BookCo pus and Wikipedia, i is pos-
sible o he models o in es iga e long con iguous sequences
(Rad o d e al., 2018, pp. 4, 5; De lin e al., 2019, p. 4175).
4.4. S a e-o - he-a me hods
4.4.1. Readabili y
The p e ious chap e in oduced he p inciples o NLP
models. Fi s , ule-based models we e b ie ly p esen ed, ol-
lowed by machine lea ning models, including supe ised and
unsupe ised models. Finally, ANNs we e discussed and i
was shown how hey a e applied in popula LLMs. Based
on his ounda ion, he s a e-o - he-a models in he a eas
o eadabili y, sen imen analysis, and disclosu e quan i y
and simila i y a e p esen ed now o comple e he de ailed
o e iew o ex ual analysis me hods. A compa ison wi h
he adi ional models as well as an in-dep h me hodologi-
cal insigh hen allows o iden i y he me hods sui able o
he esea ch ques ions o his hesis.
In he a ea o eadabili y, he Fog Index has been e y pop-
ula . Weaknesses such as insu icien applicabili y o business
ex s o he classi ica ion o wo ds as complex, when hey ac-
ually end o be easily eadable, ha e been iden i ied and
coun e ed by al e na i e measu es such as he Bog index.
Lough an and McDonald ind ano he measu e ha ou -
pe o ms adi ional eadabili y indices: he 10-K documen
ile size. The au ho s look a he ile size o 10-Ks iled wi h
he SEC. These iles a e highly s anda dized and p esen ed
in HTML o ma . Lough an and McDonald ind ha la ge
ile sizes a e associa ed wi h lowe eadabili y. The esul s
a e bo h s ongly co ela ed wi h adi ional eadabili y mea-
su es and consis en wi h Lough an and McDonald’s de ini-
ion o eadabili y ha highe eadabili y leads o less am-
bigui y in alua ion, which he au ho s demons a e in hei
esea ch.
12 BookCo pus is a da abase o no el books w i en by unpublished au ho s
and con ains o e 11,000 books in gen es like omance, his o y, and ad-
en u e (BookCo pus, 2023).
File size as a eadabili y measu e is as simple as one can
imagine and equi es e y li le adjus men be o e use, mak-
ing i less p one o e o s han o he measu es. Howe e , i
is ques ionable whe he his measu e can be applied o o he
ex s such as sus ainabili y epo s. Gi en ha eadabili y
is a measu e o he ease wi h which alue- ele an in o ma-
ion can be ex ac ed, ile size wo ks mainly on he simplis ic
p emise ha a highe quan i y o ex ual da a makes i mo e
di icul o ex ac ele an in o ma ion (Lough an & McDon-
ald, 2014, pp. 1646, 1650, 1658–1658, 1667–1668).
Sus ainabili y epo s a e e y he e ogeneous. The la ge
a ia ion in epo leng h could yield signi ican esul s in he
eadabili y analysis, bu i is deba able whe he a longe sus-
ainabili y epo inc eases he di icul y o ex ac ing alue-
ele an in o ma ion o whe he i jus con ains mo e in o -
ma ion. I is also possible o he same in o ma ion o be
p esen ed di e en ly in wo epo s, one wi h a b ie e sion
and one wi h a mo e de ailed e sion. While he longe e -
sion may be easie o he eade o unde s and, i would
educe eadabili y in his model due o he inc eased ile size
(Bochkay e al., 2023, p. 779).
The app oach o Lough an and McDonald is a me hod
ha does no ake ad an age o he de elopmen s in he ield
o NLP and machine lea ning ha a e desc ibed in Chap e
4.3. Howe e , o he s a e-o - he-a eadabili y me hods in-
c easingly ely on hem. Machine lea ning can be u ilized
o build an accu a e model om a ailable componen s ha
bes i a pa icula esea ch ques ion. Table 4lis s di e en
ea u es ha can be used in eadabili y analysis, di ided in o
ca ego ies.
Shallow ea u es, such as he leng h o wo ds o sen-
ences, he a io o simple wo ds, o he a io o di e en
e ms, a e used in adi ional models and o m he basis
o eadabili y analysis. Mo phological ea u es cap u e how
wo ds a e used in ela ion o hei s em wo ds, and hus
can cap u e complexi y and g amma ical ea u es. These ea-
u es ge los in many adi ional models because s emming
is used o educe he dimensionali y o such wo ds back o
hei oo . Syn ac ic ea u es cap u e he equency o ce -
ain wo d combina ions o sen ence s uc u e, which also o -
en ge los because o he use o he bag o wo ds app oach.
Seman ic ea u es e alua e eadabili y a he con en le el,
going beyond o he me hods. Fo example, he use o many
synonyms dec eases eadabili y, as a low eading le el audi-
ence is mo e likely o equi e a simpli ied ocabula y. An-
o he seman ic ea u e is cohesion, which uses simila i ies o
measu e how well sen ences me ge in o each o he . An easy
ansi ion h ough a highe simila i y o he las wo ds o one
sen ence o he i s wo ds o ano he sen ence inc eases he
eadabili y o a ex (Mad azo & Pe a, 2020, pp. 4–6).
Once he analysis me hods ha e been g ouped, machine
lea ning can be applied o de e mine he mos e ec i e me h-
ods o a pa icula use case. This can be done by aining
he model wi h only one ca ego y o analysis me hods a a
ime and hen compa ing he esul s. Such a compa ison can
show, o example, ha he shallow ea u es a e mo e accu-
a e han he mo phological ea u es, o ha he seman ic
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 219
Table 4: Tex ual ea u es ( o de ini ions o e ms see Mad azo and Pe a (2020, pp. 4–6)
Shallow Fea u es Mo phological Fea u es Syn ac ic Fea u es Seman ic Fea u es
Wo d leng h In lec ion a io Pa o speech a io Synonym usage
Sen ence leng h Mo phological phenomena equencies Dependency ee complexi y Seman ic closeness
Ra io o simple e ms Cohesion
Ra io o di e en e ms
ea u es a e less accu a e han any o he g oup o ea u es.
Fu he mo e, i can also be de e mined which ea u es wi hin
he ca ego ies ha e he g ea es in luence, so ha , o exam-
ple, he ou come o he shallow ea u es p edominan ly de-
pends on he esul o he indi idual ea u es wo d leng h
and sen ence leng h (Mad azo & Pe a, 2020, pp. 4–6). Ac-
co dingly, he machine lea ning p ocess does no consis o
a single p ocess in which he ex ual da a passes h ough a
e y la ge numbe o laye s. Ins ead, i is di ided in o se e al
sub p ocesses, each wi h a ewe amoun o laye s. On he
one hand, ine ec i e c i e ia can be il e ed ou o educe he
compu a ional e o compa ed o an all-encompassing p o-
cess. A he same ime, he accu acy o he analysis can be
imp o ed by elimina ing ea u es ha ake he w ong pa h in
he lea ning p ocess and lead o unin o ma i e esul s.
Ano he way o apply machine lea ning in eadabili y
analysis is o compa e no only c i e ia, bu also en i e mod-
els. Such compa isons would no be easible wi hou he au-
oma ion possibili ies o e ed by machine lea ning due o he
high enginee ing cos s. Compa ing models wo ks simila ly o
compa ing analysis ea u es. All models a e ed he same ex-
ual da a o ensu e compa abili y, he esul s a e compa ed
wi h each o he in e ms o he ou pu o classi ica ion accu-
acy, and only he bes model is used in ac ual esea ch be-
yond he aining da a se . To go one s ep u he , he models
can hen be combined wi h each o he . Fo example, i may
be ound ha one model, e.g. BERT, has a wo d p edic ion ac-
cu acy o 90 %, and ano he model, e.g. GPT, has an accu acy
o 85 %, bu using bo h models in combina ion achie es an
e en highe accu acy han ei he model sepa a ely, because
he s eng hs o one model compensa e o he weaknesses o
he o he . By ha ing mul iple models wo k oge he and com-
bining hei p edic ions, an o e all mo e obus and powe ul
esul can be achie ed. Combining models can be pe o med
h ough di e en app oaches, such as ca ego izing he inpu
ex in o he ca ego y p edic ed by he majo i y o models
(i mo e han wo models a e combined), o weigh ing he
esul s o he models acco ding o hei indi idual accu acy
(Filighe a e al., 2019, pp. 335–336, 338–340, 344–345).
Readabili y models a e being c i icized o no being
ans e able be ween di e en ypes o ex (Bochkay e al.,
2023, p. 780). I is no wo hwhile e alua ing me ics ha
a e o li le impo ance o a ex ’s a ge audience (Lough an
& McDonald, 2020, p. 28). Schoolbook ex s should be ac-
cessible o s uden s and adap ed o hei expe ience and
eading abili y. Howe e , inancial s a emen s o ea ning call
ansc ip s a e no likely o be ead by lowe -le el s uden s,
bu by mo e expe ienced eade s who can be expec ed o
comp ehend a ce ain le el o complexi y.
Ma inc e al. show ha supe ised and unsupe ised
NLP models a e able o assess eadabili y ac oss di e en au-
diences. Using me hods simila o hose o Mad azo e al.
and Filighe a e al., hey p o e his by using di e se aining
da a se s. Ma inc e al. use ex sou ces, such as educa-
ional ma e ials, which a e classi ied by eading abili y, age
g oup, o g ade, bu also la ge da abases, such as Wikipedia.
These da abases a e classi ied in o di e en eadabili y le -
els, such as simple, balanced, o no mal. Readabili y can hen
be measu ed by an adjus able sco e ha akes in o accoun
he eading skills o he espec i e audience (Ma inc e al.,
2021, pp. 241, 152, 166–169, 172–175).
Technological ad ances in NLP and machine lea ning al-
low o mul i ace ed eadabili y esea ch, pa ly h ough a
mo e e icien e alua ion o adi ional me ics and pa ly
h ough new de elopmen s. Measu ing eadabili y can p o-
ide aluable in o ma ion o companies. I allows hem o
assess hei quali a i e disclosu es o de e mine i hose dis-
closu es a e comp ehensible o hei s akeholde s. A he
same ime, legisla o s, in e nal and ex e nal egula o s and
o he eade s o disclosu es can bene i om he esul s o
such measu es.
Howe e , he le el o eadabili y mus be ele an o he
esea ch ques ion. O he wise, i ep esen s no hing mo e
han an indica o wi hou any pa icula meaning, which
a wo s is a e lec ion o he complexi y o he company
(Lough an & McDonald, 2020, p. 28).
4.4.2. Sen imen analysis
Va ious applica ions o sen imen analysis we e in o-
duced in Chap e 4.2.2. T adi ional me hods measu e sen i-
men by coun ing he numbe o wo ds in a ex , which a e
classi ied in o sen imen ca ego ies using dic iona ies. The
g ea es po en ial o imp o emen in hese me hods lies in
he imp o emen o hese dic iona ies. The c ea ion o a dic-
iona y speci ically o he inance and accoun ing domain,
ins ead o he commonly used H4N, has had a majo im-
pac on sen imen analysis esea ch (Lough an & McDonald,
2011, pp. 61–62). The applica ion o machine lea ning o he
ield is likely o be e en mo e signi ican . Machine lea ning
me hods elimina e he need o dic iona ies o assess sen i-
men . Ins ead, he classi ica ion o ex s o ex segmen s is
pe o med by an algo i hm ha is ained on da a samples
o de ec he one o a ex . This app oach p omises a mo e
accu a e classi ica ion han me hods ha ely on dic iona ies
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235220
(Ha mann e al., 2023, pp. 76, 78).
T adi ional machine lea ning wo ks in he same way as
supe ised lea ning desc ibed in Chap e 4.3. The algo i hm
ecei es ex s as aining da a ha a e p e-labeled wi h he
co esponding sen imen . This app oach is used, o exam-
ple, by Azimi and Ag awal o ex ac in o ma ion om he
sen imen o 10-Ks. They ind ha bo h posi i e and nega i e
sen imen can p edic abno mal e u ns. Chen e al. al eady
had simila indings in 2014 when analyzing SeekingAlpha
a icles (see Chap e 2.4), bu Azimi and Ag awal’s esul s
di e in ha hey look a a di e en da ase wi h 10-Ks and
ha hey analyze a much la ge sample wi h o e 200 million
sen ences. A he same ime, hei esul s a e also signi ican
o posi i e sen imen , in con as o hose o Chen e al. who
could no ind signi ican esul s o o he sen imen s besides
nega i e one (Azimi and Ag awal, 2021, pp. 2, 10, 20–21,
32; H. Chen e al., 2014, p. 1337). This migh be because he
machine lea ning app oach is cap u ing ela ionships ha a e
no appa en h ough he wo dbook app oach, bu he e also
may be o he easons o his.
Machine lea ning has a signi ican ad an age o e dic-
iona y app oaches, as i is capable o consis en ly cap u -
ing sen imen o e mul iple pe iods. This is possible due o
he olume and imeliness o he aining da a. Dic iona ies
can only cap u e a s a us quo and may ha e a lack o ac ual-
i y.13 The e o e, s a e-o - he-a me hods a e o en p e e able
o dic iona y me hods. Ne e heless, adi ional dic iona y
me hods can be used i he empo al con ex does no ma -
e and i is only he occu ence o indi idual wo ds ha is
c ucial o he esea ch ques ion, o i he cos o he mo e
complex implemen a ion o machine lea ning exceeds he
bene i o mo e accu a e classi ica ion (F ankel e al., 2022,
pp. 5515, 5522–5524, 5529).
E en mo e accu a e han adi ional machine lea ning
me hods is he applica ion o con ex ual deep lea ning o sen-
imen analysis. Al hough classical machine lea ning ou pe -
o ms he dic iona y app oach, hese me hods, such as VSM
o Nai e Bayes, ep esen ex s as bag o wo ds and a e hus
subjec o he p oblems desc ibed ea lie . Algo i hms ha
cap u e con ex ual in o ma ion om wo d embedding, as in
ANNs, can also cap u e he su ounding con ex and associ-
a ed sen imen (Hei e mann e al. 2023: 79). This is acili-
a ed h ough he a en ion mechanism discussed in Chap e
4.3.
Sen imen analysis is also being used in a eas o he han
inancial and accoun ing, such as economics, poli ical sci-
ence, and medical esea ch. A mul idisciplina y s udy by
Colón-Ruiz and Segu a-Bedma inds ha LLMs ha e he
highes accu acy o sen imen analysis. While adi ional
machine lea ning models, such as VSM, pe o m well espe-
cially wi h a la ge amoun o aining da a, LLMs domina e
he ield, in pa icula he BERT algo i hm. I deli e s sligh ly
be e esul s han compe ing models, bu a he expense o
13 An example o his a e he esul s o Long e al. (2023) p esen ed in chap-
e 3.2, which could only be achie ed by he au ho s c ea ing a new dic-
iona y adap ed o ime and con ex .
highe compu a ional cos s (Colón-Ruiz & Segu a-Bedma ,
2020, pp. 1, 5–6, 9–10).
Since BERT is a pionee in he ield o LLMs, he model
will now be he subjec o a mo e de ailed discussion. BERT is
a p e- ained model, simpli ying i s use o use s by elimina -
ing he need o na iga e h ough he unde lying complexi y.
In o de o BERT o be able o p ecisely adap he analysis
o esea ch ques ions, only he ine- uning o he model has
o be pe o med. He e, BERT can achie e be e esul s han
adi ional app oaches e en wi h only a ew hund ed ain-
ing samples (Siano & Wysocki, 2021, pp. 6, 27–28). BERT is
p e- ained acco ding o MLM, enabling he model o p edic
missing masked wo ds o o p edic subsequen sen ences.
BERT is publicly a ailable a no cos . While he algo i hm
equi es high compu a ional powe , Google allows ee use
o online g aphics p ocessing uni s o ope a e BERT, so he
model has ew ba ie s o usage (Siano & Wysocki, 2021,
pp. 7, 17, 22).
In con as o dic iona y models, he ope a ion o LLMs
is mo e complex and di icul o comp ehend. Howe e , i
is easible o e i y ha such models ac ually cap u e sen i-
men om he con ex o in o ma ion in a ex , as hey a e in-
ended o do, by dele ing o changing wo ds in manual es s.
Siano and Wysocki ha e pe o med such es s and ound ha
BERT s ill pe o ms be e han adi ional models e en when
key wo ds ha would ha e in luenced sen imen in he wo d-
book app oach a e dele ed. Al hough he accu acy dec eases,
his e alua ion indica es ha BERT gene a es i s p edic ions
based mo e on he con ex o a ex han on he wo d coun ,
as in he case o wo dbook app oaches. Fu he mo e, BERT
loses much o i s p edic i e powe when wo ds in a ex ge
andomized, which again sugges s ha he model deli e s on
i s p omise and, unlike bag o wo ds models, ex ac s in o -
ma ion om he s uc u al o ganiza ion o a ex (Siano &
Wysocki, 2021, pp. 20, 25–26).
In addi ion o i s many ad an ages, BERT also has some
limi a ions. The bigges one is p obably he limi a ion o o-
kens. Cu en ly, ex s wi h a maximum o 512 okens can
be analyzed. A oken usually co esponds o a wo d o , de-
pending on he okeniza ion, o only a ac ion o a wo d.
The e o e, leng hy ex s, which would ce ainly include sus-
ainabili y epo s, canno be analyzed as easily wi h BERT.
Resea che s can apply wo ka ounds by selec i ely o an-
domly analyzing indi idual ex componen s, o by analyz-
ing each ex componen one a a ime in a sc olling pa e n.
Howe e , ad ances in machine lea ning and gene al ech-
nological p og ess o e hope ha compu a ional powe will
inc ease and hese limi a ions will ade (Siano & Wysocki,
2021, pp. 9–10, 30).
BERT has been used and de eloped by esea che s in a -
ious ields. One o he mos impo an de elopmen s is Fin-
BERT, a ine- uned e sion o BERT specialized o he inan-
cial domain. Lough an and McDonald ha e al eady iden i-
ied ha he inancial domain language di e s signi ican ly
om gene al language and ha e e olu ionized ex ual anal-
ysis in his ield wi h hei own dic iona y (Lough an & Mc-
Donald, 2011, pp. 49–50). Huang e al. ollow his example
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 221
by adap ing he new s a e-o - he-a o he inancial domain.
They do his by p e- aining BERT wi h a la ge numbe o
ex s wi h inancial con ex like co po a e disclosu es, inan-
cial analys epo s and ea nings con e ence call ansc ip s.
These ex s help FinBERT o be e p ocess asks ela ed o
inancial in o ma ion. In o al, 4.9 billion okens a e used
o ine- uning, which e en exceeds he popula ion o he
p e- aining o he plain BERT e sion. FinBERT has been
compa ed o o he LLMs as well as adi ional ex analysis
me hods and ou pe o ms hem, as well as un ained BERT,
when applied o inance-speci ic ex s, bu also when applied
o ex s ela ed o he En i onmen al, Social and Go e nance
(ESG) domain (A. Huang e al., 2022, pp. 8–9, 19).
Sen imen analysis has bene i ed om machine lea ning
and NLP de elopmen s, which ha e led o new echniques
and models ha can signi ican ly imp o e he accu acy in his
ield. The hi d main a ea o ex ual analysis conside ed in
his hesis, disclosu e quan i y and simila i y, also bene i s
om hese de elopmen s.
4.4.3. Disclosu e quan i y and simila i y
While adi ional me hods de e mine simila i y, using he
bag o wo ds app oach, esea che s a e now inc easingly u i-
lizing machine lea ning echniques o de e mine simila i ies
be ween ex s. In adi ional me hods, simila i y is mainly as-
sessed by o e lap in wo d usage. Ma ching wo ds o okens
in wo ex s inc ease he simila i y sco e. Ins ead o single
wo ds, sequences o wo ds can also be conside ed. Thus, he
simila i y sco e inc eases when wo d sequences, usually con-
sis ing o wo o ou wo ds, appea in he ex s o be com-
pa ed. These adi ional echniques can be u he adap ed,
o example by applying equency weigh ing. He e, less e-
quen wo ds a e gi en a highe weigh unde he assump ion
ha hey con ain mo e in o ma ion. The simila i y be ween
documen s inc eases, especially when a e wo ds o wo d se-
quences o e lap (Gaulin & Peng, 2022, pp. 2–3, 12–13).
Wi h he help o deep lea ning, wo d embedding algo-
i hms a e able o ecognize simila i y in ex s wi hou be-
ing limi ed o he occu ence o indi idual wo ds o wo d
sequences. Fo his pu pose, he algo i hm uses a sophis i-
ca ed me hod in which i scans he ex o p ede ined wo ds
and builds ec o s om he su ounding wo ds ha a e nea
he sea ched wo d. This nea ness can be de ined by a ce -
ain dis ance, e.g. up o en wo ds be o e o a e he a ge
wo d. These wo ds a e con ex wo ds and a e used o cap-
u e he ela ionship o he a ge wo d o i s en i onmen .
The ec o s o hese con ex wo ds a e placed in he same
ec o space as he sea ched wo d, so ha bo h g amma i-
cal and seman ic ela ions be ween wo ds can be cap u ed.
This allows o a deepe and mo e nuanced ep esen a ion o
ex ual con en (Gaulin & Peng, 2022, p. 34).
When used in combina ion wi h cosine simila i y (de-
sc ibed in Chap e 4.1.3), wo d embedding algo i hms a e a
powe ul ool o accu a ely measu ing disclosu e simila i y.
T adi ional me hods based on he bag o wo ds app oach can
s ill p o ide decen esul s i he esea ch ques ion is p ima -
ily based on wo d choice and less on he con ex o he ex s
(Bochkay e al., 2023, p. 781). In summa y, howe e , his
a ea o ex ual analysis also bene i s om he new echnical
possibili ies o e ed by NLP.
The li e a u e e iew o he inance and accoun ing do-
main in Chap e 3 and he comp ehensi e p esen a ion o
adi ional and s a e-o - he-a me hods in Chap e 4 o m
he i s main pa o his hesis. The insigh s ob ained om
his s udy a e signi ican o he ollowing second main pa
o he hesis. This sec ion will p esen a ex ual analysis ap-
plica ion o a case in he accoun ing domain. This case is
co e ed in he ollowing chap e s.
5. Hypo heses de elopmen
5.1. The ela ionship be ween audi ing and compliance wi h
egula o y equi emen s
A he ou se o his hesis, i was no ed ha sus ainabili y
epo ing a ies g ea ly om company o company. Fu he -
mo e, acco ding o he NFRD, EU membe s a es s ill ha e he
oppo uni y o op ou o manda o y ex e nal audi s o sus-
ainabili y epo ing. In addi ion o he e ogenei y in e ms o
con en and s uc u e, he audi o sus ainabili y epo s is an-
o he c i e ion o di e ences in some EU coun ies, as many
companies olun a ily ha e hei epo s ex e nally audi ed
wi h limi ed assu ance, some e en wi h he highe assu ance
le el o easonable assu ance.
To add ess he issue in mo e de ail, he undamen al heo-
ies in he a eas o sus ainabili y epo ing and audi ing we e
examined i s , ollowed by an ex ensi e discussion o he ex-
ual analysis me hodology. This included a summa y o he
p inciples o he me hodology and a li e a u e e iew in he
inancial and accoun ing domain. The undamen al heo ies
conside ed in he a ea o audi ing ha e been na owed o he
essen ial p inciples and ha e u he emphasized he e ec s
o he audi on he eliabili y o disclosu es.
Li e a u e indica es ha audi o s can also se e as in-
e media ies o suppo company compliance wi h egula-
ions. This is because hey possess comp ehensi e knowledge
h ough hei ac i i ies and hei di e se s uc u e (Walke ,
2014, pp. 214–215). The e o e, he ole o he audi o can
no only inc ease egula o y compliance, bu also inc ease i s
e ec i eness in gene al (King, 2007, p. 213).
In quali a i e esea ch, Walke ound ha companies in
he Aus alian ucking sec o ha pa icipa ed in a olun a y
compliance p og am achie ed be e pe o mance and gene -
a ed highe social alue i hey in ol ed audi o s in his p o-
cess (Walke , 2014, pp. 215–216, 221). This example is no
di ec ly ela ed o he hesis con en wise, bu he unde lying
s uc u al ela ion is he same. Simila as in he Aus alian
ucking sec o case, Eu opean companies ha e he op ion o
in ol e ex e nal audi o s in a p ocess olun a ily. Submi ing
epo ing componen s om he non- inancial epo ing o an
audi imposes addi ional audi ing ees o companies, bu can
also p o ide bene i s such as po en ially inc easing he elia-
bili y o he epo ing in acco dance o a ibu ion heo y o
achie ing a highe le el o compliance wi h legal epo ing
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235222
equi emen s, which is desi able o bo h he company and
i s s akeholde s. F om he ela ed example and he heo ies
p esen ed, he ollowing hypo hesis can be s a ed ega ding
he impac o an ex e nal audi on he quali y o sus ainabili y
epo ing:
H1: Audi assu ance o sus ainabili y epo s in-
c eases hei compliance wi h egula o y equi e-
men s.
In Chap e 6.2, his is add essed by a mo e de ailed ex-
amina ion o he equi emen s o he EU axonomy as well as
cu en ly applicable and o hcoming audi ing s anda ds and
he de e mina ion o he ele an dependen a iables.
5.2. The ela ionship be ween he ex end o co po a e sus-
ainabili y and epo ing quan i y and audi demand
In addi ion o he in luence o he audi on he quali y
o sus ainabili y epo ing, he e may be o he ac o s ha
in luence bo h epo ing and he ci cums ances whe he an
ex e nal audi akes place. Chap e 2.2 discussed he majo
heo ies ha de e mine whe he and o wha ex en compa-
nies a e willing o engage in olun a y epo ing. The olun-
a y disclosu e heo y sugges s ha companies will only ol-
un a ily disclose in o ma ion i hei bene i s ou weigh hei
cos s. The e is al eady much e idence wi hin his heo y. Fo
ins ance, esea ch has shown ha i m cha ac e is ics signi i-
can ly d i e co po a e disclosu e. Fi m size, o example, has
a gene ally posi i e impac on disclosu e as epo ing expe -
ise usually inc eases wi h g owing i m size, bu also because
la ge i ms a e subjec o g ea e public exposu e and ha e
o legi ima e hemsel es o a g ea e ex en . O he i m cha -
ac e is ics, such as he deg ee o in e na ionaliza ion, boa d
size, o media exposu e, also a ec olun a y co po a e dis-
closu e (Zamil e al., 2023, pp. 247, 249, 252).
Co po a e sus ainabili y is a compa a i ely less s udied
d i e in his con ex . Howe e , i is possible ha sus ainable
companies epo on hei sus ainable ac i i ies o e p opo -
ionally in o de o bene i om i . To add ess his esea ch
gap, he ollowing hypo hesis is posed:
H2: Companies ha a e ac ing sus ainable disclose
a highe quan i y o in o ma ion in hei sus ain-
abili y epo s.
The deg ee o sus ainabili y o companies’ ac ions he e is
de ined in a simple manne using exis ing sus ainabili y a -
ings. The quan i y is measu ed using he ile size o sus ain-
abili y epo s in an adjus ed o m, based on he me hodology
o Lough an and McDonald (2014). The de ailed esea ch
design and and use o he a iables is p esen ed in sec ion
6.3.2.
Repo ing equi emen s ha e an undeniable in luence on
his as well. When egula o y bodies impose manda o y dis-
closu es, hese disclosu es a e mo e likely o be made (Du an
& Rod igo, 2018, p. 14). The implemen a ion o olun a y
equi emen s, such as he GRI, can be a signi ican ly d i e
in he epo ing landscape as well (Dissanayake e al., 2019,
pp. 102–103).
A less s udied in luence is he impac o audi ing. As ol-
un a y disclosu e is pe o med only i a company’s bene i s
ou weigh he ela ed cos s, his heo y could also be applied
o olun a y audi s o disclosu es. Acco ding o a ibu ion
heo y, posi i e in o ma ion is mo e likely o be doub ed han
nega i e in o ma ion. The e o e, i would be mo e eason-
able o companies o unde go a olun a y audi i he in o -
ma ion con ained in hei disclosu e is p edominan ly posi-
i e, which leads o he ollowing inal hypo hesis:
H3: Companies ha a e ac ing sus ainable a e
mo e likely o demand olun a y assu ance o hei
epo s.
This hypo hesis expands on he olun a y disclosu e heo y
by shi ing he ocus om disclosu e i sel o he olun a y
submission o olun a y disclosu es as well as non- olun a y
disclosu es o an ex e nal audi .
H1 ep esen s he cen al hypo hesis o his hesis. The
seconda y hypo heses H2 and H3 a e indi ec ly ela ed o i
and can p o ide u he insigh s in o he esea ch a ea o sus-
ainabili y epo ing. Howe e , hey will only be add essed
o a mo e limi ed ex en .
The nex chap e i s desc ibes he da a ga he ed o ad-
d ess he hypo heses. This is ollowed by an exposi ion o
he unde lying esea ch design. Nex , he ocus shi s o he
de e mina ion o he dependen a iables in ega d o he
hypo heses unde conside a ion. Finally, he esul s a e dis-
cussed.
6. Da a and esea ch design
6.1. Sample da a
The sus ainabili y epo s o a subse o companies e-
qui ed o epo unde he NFRD, which a e la ge compa-
nies wi hin he EU wi h an a e age numbe o a leas 500
employees (Eu opean Union (EU), 2014, p. 4), a e now ex-
amined in o de o in es iga e he h ee hypo heses o his
hesis. In o al, he equi emen s o he NFRD a ec app oxi-
ma ely 10,000 companies. The CSRD will ex end he scope o
applica ion by including medium-sized companies o app ox-
ima ely 50,000 companies (KPMG, 2022, p. 37), beginning
om inancial yea 2024. Also c ucial o he e i ica ion
o he hypo heses is he dis inc ion ha an audi wi h lim-
i ed assu ance is manda o y unde he CSRD, whe eas unde
he NFRD he e is s ill an op ion a EU membe s a e le el
o exemp companies om his equi emen . Due o ime
cons ain s, his hesis does no examine all companies a -
ec ed by he NFRD, bu only a subsample, which consis s o
all companies lis ed in he Ge man S ock Index (DAX) and
he Midcap DAX (MDAX). This subsample con ains 90 com-
panies, which co esponds o abou one pe cen o he o e all
a ec ed companies, so ha he esul s may no be uncondi-
ional eplicable a he EU le el. Ge many was chosen as
he coun y o analysis, as i is he coun y wi h he mos
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 223
G250 companies wi hin he EU14 (KPMG, 2022, p. 75) and
could he e o e ake on a pionee ing ole in epo ing issues.
Fu he mo e, Ge many has exe cised i s igh o op ou o
he manda o y audi o sus ainabili y epo ing. Only in his
way is i possible o e i y he hypo heses p esen ed. The as-
su ance a e in he sample is 77 %, which is sligh ly highe
han he G250 o e all (63 %) (KMPG 2022: 24). Appendix B
p esen s addi ional desc ip i e s a is ics on he sample da a.
The dependen a iables a e ga he ed wi h he help o
ex ual analysis me hods ou lined ea lie in his hesis. Chap-
e 6.2 discusses he esea ch design and he associa ed align-
men o he dependen a iables, while he ac ual ga he ing
o he a iables is desc ibed in Chap e 6.3.
6.2. Resea ch design
To analyze H1, i is i s necessa y o de ine how compli-
ance wi h egula o y equi emen s can be assessed. I is hen
impo an o de e mine which o he ex ual analysis me h-
ods, p esen ed in Chap e 4, a e app op ia e o he assess-
men .
The e is no clea ly de ined benchma k o epo ing e-
qui emen s compliance. This is because he epo ing land-
scape i sel is complex, ambiguous and some imes e en con-
adic o y. In e egional s anda ds such as he GRI Repo -
ing F amewo k a e opposed o he i s d a s o sus ainabil-
i y s anda ds om he In e na ional Sus ainabili y S anda ds
Boa d, supplemen ed by na ional egula ions wi hin he indi-
idual coun ies. The EU’s a emp a ha moniza ion u he
adds o he complexi y. The equi emen s o he NFRD could
no p o ide he desi ed e ec s. The inadequa e speci ica ion
o he di ec i e has esul ed in a lack o in o ma ion wi hin
he epo ed da a. The op ions o he EU membe s a es, such
as he equi emen o an audi , bu also he disclosu e op-
ions ha allow o disclosu e sus ainabili y epo s wi hin o
ou side he managemen epo , make i di icul o compa e
in o ma ion be ween companies. The ecen ly enac ed EU
axonomy egula ion imposes u he equi emen s on com-
panies (Vel e, 2023, pp. 1–2).
Repo ing quali y canno only be de i ed om he egu-
la o y equi emen s hemsel es. The ele an audi ing s an-
da ds may also be in o ma i e. Audi ing s anda ds ex en-
si ely discuss egula o y equi emen s and p o ide guidance
o audi o s on how hey can pe o m audi p ocedu es.
While he e a e nume ous audi ing s anda ds co e ing a
a ie y o a eas in inancial epo ing, he ISAE 3000 (Re-
ised) in pa icula p o ides comp ehensi e co e age o he
subjec o non- inancial epo ing. The majo i y o compa-
nies in he sample e e o he s anda d in a ious places
wi hin hei sus ainabili y epo s. The ISAE 3000 (Re ised)
explici ly co e s all assu ance engagemen s ha do no in-
clude his o ical inancial in o ma ion, which also goes o
14 In Ge many he e a e 13 G250 companies. The e a e also 13 G250 com-
panies in F ance, al hough F ance has no exe cised he op ion o EU
membe s a es o be exemp om he audi , and he e o e sus ainabili y
epo s o companies ha mee he size c i e ia a e equi ed o unde go
an ex e nal audi (Reu e s, 2021, p. 2).
he non- inancial epo ing, and has he objec i e o p o id-
ing easonable o limi ed assu ance on ha in o ma ion (In-
e na ional Audi ing and Assu ance S anda ds Boa d, 2013,
pp. 5–6). The audi ing s anda d is ex ensi e. I desc ibes
he equi emen s o complying wi h he s anda d, including
a eas such as audi planning, he de e mina ion o ma e i-
ali y and he equi ed con en o he audi o ’s epo . Be-
cause he ISAE 3000 (Re ised) co e s such a wide ange o
opics, i does no p o ide many speci ic audi guidelines o
equi emen s in e ms o he con en o he audi o ’s epo .
Howe e , i does gi e some guidance o de e mining when
epo ing can be conside ed complian . Fi s , in i s objec-
i es, he s anda d s a es ha limi ed o easonable assu ance
can be ob ained when he subjec ma e in o ma ion is ee
o ma e ial miss a emen (In e na ional Audi ing and Assu -
ance S anda ds Boa d, 2013, p. 6), as is he case wi h o he
audi ing s anda ds. I also de ines he manda o y cha ac e -
is ics o ele ance, comple eness, eliabili y, neu ali y and un-
de s andabili y o published in o ma ion (In e na ional Au-
di ing and Assu ance S anda ds Boa d, 2013, p. 12). Using
hese cha ac e is ics as e alua ion c i e ia, compliance can
be mo e speci ically de ined. In addi ion, he ISAE 3000
(Re ised) s a es ha inconsis encies indica e ma e ial mis-
s a emen s (In e na ional Audi ing and Assu ance S anda ds
Boa d, 2013, p. 20), so inconsis en in o ma ion wi hin sus-
ainabili y epo ing o inconsis encies be ween inancial and
non- inancial epo ing may also indica e a lowe le el o
compliance.
Despi e i s naming, he ISAE 3000 (Re ised) is in need o
imp o emen . When i came in o o ce a decade ago, he a ea
o non- inancial epo ing co e ed by i was much smalle
and less complex. Fu he mo e, he impo ance o his in-
o ma ion has inc eased d ama ically o e he yea s. As a
esul , new audi ing s anda ds a e being de eloped, ha will
e en ually eplace he ISAE 3000 (Re ised). Fo he Ge -
man ma ke , he Ins i u e o Public Audi o s (Ins i u de
Wi scha sp ü e : IDW) has published wo d a s o new
audi ing s anda ds. These d a s add ess he subs an i e au-
di o non- inancial epo ing wi h easonable assu ance and
limi ed assu ance, espec i ely.
The d a s a e based on he ISAE 3000 (Re ised), bu a e
subjec o conside able unce ain ies o in e p e a ion and
he e o e may no be used by audi ing i ms o cu en au-
di s, also due o hei s a us as d a s and no as inalized
audi ing s anda ds (IDW Ve lag, 2022b, p. 1; IDW Ve lag,
2022a, p. 1). The d a s do, howe e , e eal a ce ain di ec-
ion in which he audi p ocedu es o ensu ing he quali y
o sus ainabili y epo ing a e being in ensi ied and on wha
hey a e based. Fo example, he d a s IDW EPS 990 and
IDW EPS 991 e e o he equi emen s o he EU axonomy
in many places, s a ing wi h he scope o applica ion o he
u u e s anda ds o companies included wi hin he EU ax-
onomy (IDW Ve lag, 2022a, p. 4) o he pe o mance o au-
di p ocedu es acco ding o he in o ma ion ca ego ies o he
EU axonomy (IDW Ve lag, 2022a, pp. 26, 29). Fu he mo e,
he d a s explici ly s a e ha he absence o in o ma ion e-
qui ed by he EU axonomy is gene ally o be conside ed as a
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235224
ma e ial miss a emen (IDW Ve lag, 2022b, p. 31; IDW Ve -
lag, 2022a, p. 30). O he sec ions ocus on he assessmen
o he p ocess o he iden i ica ion o axonomy-eligible eco-
nomic ac i i ies.
Measu ing egula o y compliance is challenging, as he e
a e many equi emen s om di e en egula o y bodies. The
equi emen s o he NFRD a e cu en ly in o ce bu a e al-
mos obsole e. The CSRD, which is supposed o eplace he
NFRD, has no ye come in o o ce. GRI s anda ds exis in
pa allel and he IFRS Founda ion is wo king on sepa a e new
s anda ds. As a as audi ing s anda ds a e conce ned, com-
panies mos ly e e o ISAE 3000 (Re ised), which is e ec-
i e bu also somewha ou da ed. New audi ing s anda ds
a e s ill being implemen ed. The EU axonomy, on he o he
hand, di e s om o he egula o y equi emen s. I s o -
mally adop ion in 2021 is ela i ely ecen while i is also
al eady e ec i e o epo ing o he ecen inancial yea ,
2022 (Eu opean Union (EU), 2020, p. 18). The equen e -
e ence o he IDW in new audi ing s anda ds unde lines he
ele ance. Due o hese ac o s, his hesis employs he EU
axonomy equi emen s as a benchma k o egula o y com-
pliance in gene al.
The EU axonomy has been applied on a manda o y ba-
sis o he second ime in he las iscal yea o 2022. In his
yea , non- inancial companies we e equi ed o epo on el-
igibili y and alignmen o hei ac i i ies o he i s wo o
he six axonomy objec i es. A he same ime, inancial com-
panies we e only equi ed o epo on he eligibili y o hei
ac i i ies, bu no on hei alignmen . The epo ing equi e-
men s will g adually inc ease un il he inancial yea 2025, a
which poin companies will be expec ed o epo ully on all
six en i onmen al a ge s. This epo ing includes he iden i-
ica ion o eligible ac i i ies, an assessmen o whe he hese
ac i i ies con ibu e o a leas one o he six objec i es while
no ha ming any o he objec i e, and he compliance wi h
he minimum sa egua ds se o he axonomy. This ensu es a
consis en iden i ica ion o ac i i ies o be conside ed sus ain-
able o he pu pose o de e mining he ele an indica o s
(P icewa e houseCoope s, 2023, p. 10).
The EU axonomy demands, on he one hand, in o ma-
ion on he p opo ion o a company’s u no e as well as i s
in es men and ope a ing expendi u e, which can be classi-
ied as sus ainable acco ding o he axonomy (P icewa e -
houseCoope s, 2023, p. 23; Eu opean Union (EU), 2020,
p. 17). Disclosing hese me ics p o ides insigh in o he
cu en con ibu ion o en i onmen al goals as well as p o-
jec ing u u e con ibu ions. On he o he hand, quali a i e
in o ma ion mus also be p o ided explici ly. Bo h he com-
pu a ion logic and he key elemen s o he indica o s need o
be disclosed. This quali a i e in o ma ion highligh s he an-
si ion p ocess om axonomy-eligible ac i i ies o axonomy-
aligned ac i i ies (Eu opean Commission, 2022, pp. 7–8, Eu-
opean Commission, 2021, p. 4).
The de ini ion o egula o y compliance p o es o be di -
icul due o he many di e en egula o y bodies in ol ed,
al hough he equi emen s o he EU axonomy we e iden-
i ied as a sui able quali y cha ac e is ic as hey a e in use
oday and no going o be supe seded by new egula ions
in he nea u u e. The manda o y equi emen s o he i s
wo objec i es o he axonomy, clima e change mi iga ion and
clima e change adap a ion, a e app op ia e o he analysis
o his hesis, since in addi ion o he key igu es, quali a-
i e in o ma ion is explici ly equi ed, which can be e alu-
a ed wi h he help o ex ual analysis me hods. Howe e , i
should be no ed ha hese do no ep esen an exhaus i e
quali y ea u e o sus ainabili y epo ing. O he app oaches
o measu ing he quali y o sus ainabili y epo ing a e also
concei able.
In o de o analyze H1, i is c ucial o iden i y no only he
con en s o be conside ed, bu also he me hod mos sui able.
The de ailed p esen a ion o known me hods in Chap e 4
se es his pu pose. These me hods can be di ided in o h ee
main ca ego ies: eadabili y, sen imen analysis and disclosu e
quan i y and simila i y. Wi hin hese main ca ego ies, he
compa ible indi idual me hods can hen be de e mined on
he basis o he co espondence be ween he objec i es o he
me hod and he esea ch ques ion, as well as on he basis o
es ic ions, e.g. due o lack o ime, compu ing powe o
o he limi ed esou ces.
The measu e used o assess he quali y o non- inancial
epo ing is he ex en o which he ele an sec ions o he
epo ing comply wi h he equi emen s o he EU axonomy.
To assess hose ex ac s, no only hei con en bu also hei
cha ac e is ics a e e alua ed, mo e p ecisely he cha ac e -
is ics ha a e also lis ed in he cu en ly ele an audi ing
s anda d ISAE 3000 (Re ised) and which a e also ele an
o a ious o he con en s in inancial and non- inancial e-
po ing: ele ance, comple eness, eliabili y, neu ali y and un-
de s andabili y (In e na ional Audi ing and Assu ance S an-
da ds Boa d, 2013, p. 12). The ul illmen o hese cha ac e -
is ics indica es epo ing quali y. T ying o link he cha ac e -
is ics wi h ex ual analysis me hods (o e iew in Table 2), un-
de s andabili y can in ui i ely be co e ed by eadabili y mea-
su es. A ex ha is easily eadable may no always be un-
de s andable. S ill, high eadabili y acili a es he eade ’s
comp ehension, while poo eadabili y makes epo ing mo e
di icul o unde s and. Nex , neu ali y can be assessed by
a ious me hods o sen imen analysis by examining whe he
he ex ac s show ce ain sen imen s, such as posi i ely o -
mula ed language, which indica es a lack o neu ali y. Thus,
unde s andabili y can be analyzed qui e well wi h eadabili y
measu es and neu ali y can be analyzed wi h sen imen mea-
su es. Rele ance, comple eness and eliabili y end o be less
in ui i e. I is easonable o a gue o analysis me hods om
he g oup o disclosu e quan i y and simila i y o compa e he
ex ac s wi h he equi emen s o he EU axonomy, bu such
an app oach is likely o be less p ecise han he assessmen o
he cha ac e is ics o unde s andabili y and neu ali y, whe e
he me hods co espond o he esea ch p oblem mo e well.
To o e come his p oblem, he analysis in his hesis is
ca ied ou using an exhaus i e me hod h ough he applica-
ion o an LLM. In Table 2, i can be seen ha LLMs a e among
he s a e-o - he-a me hods co e ing all me hodological a -
eas. The de elopmen s in NLP and machine lea ning make
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 231
Figu e 6: Rela ionship be ween ESG_Sco e and Assu ance_LVL
In his analysis, Ma ke _CAP shows a signi ican co ela ion
( -s a is ic: 2.38) wi h Assu ance_LVL, while ESG_Sco e is jus
below he signi icance h eshold ( -s a is ic: 1.93). Based on
his esul , H3 canno be con i med. The co ela ion be ween
a company’s sus ainabili y and le el o audi assu ance can-
no be s a is ically p o en. Howe e , i appea s ha la ge
companies wi h a highe capi aliza ion a e mo e likely o un-
de go an ex e nal audi . This ela ionship is also shown in
Figu e 7.
19 o he sample companies do no p o ide an ex e nal
audi o hei non- inancial epo ing.21 Only h ee o hose
companies exceed $10 billion in ma ke capi aliza ion, while
he mean o he en i e sample is $21.2 billion and he me-
dian is $8.7 billion (Appendix B). While many small-cap com-
panies also unde go an audi o hei non- inancial epo -
ing, a he same ime a olun a y audi appea s o be in-
e i able once a company eaches a ce ain ma ke capi al-
iza ion. Since he audi o non- inancial epo ing was no
manda o y o he sample o Ge man companies in he mos
ecen iscal yea , implemen a ion a he i m le el is a ade-
o be ween cos s and bene i s (Widmann e al. 2021: 457),
simila o he publica ion o supplemen a y in o ma ion ac-
co ding o he olun a y disclosu e heo y. Audi ees a e p i-
ma ily d i en by he size and complexi y o i ms, bu o he
ac o s such as a so-called BIG-4 p emium also play a ole, as
i ms hope o achie e highe audi quali y by hi ing he la ge,
p es igious audi i ms (Widmann e al., 2021, pp. 473–475,
479).
Based on he p e ious indings, la ge i ms in pa icula
conside he bene i s o an audi o ou weigh he associa ed
cos s. The easoning behind his opens up possibili ies o
u he esea ch. A he same ime, smalle companies may
no be able o a o d he olun a y audi , as hey do no ha e
he same inancial lexibili y as la ge companies. Ne e he-
21 One company (Six SE) excluded due o incomple e da a.
less, e en he smalles companies included in he sample a e
lis ed on he MDAX, meaning ha hey a e s ill ela i ely
la ge compa ed o o he companies in e ms o o al asse s
o ma ke capi aliza ion, so ha his e ec is unlikely o be
obse ed in his case.
The esul s o Equa ion 8and he esul s o he examina-
ion o he con ol a iable Ma ke _CAP should be conside ed
in ela ion o he condi ions o he da a se . Fi s , he sample
size o 85 is ela i ely small. Second, he dependen a iable
Assu ance_LVL is no a con inuous a iable bu a ca ego ical
a iable ha dis inguishes only be ween no assu ance, limi ed
assu ance and easonable assu ance. I is pa icula ly di icul
o cap u e a ca ego ical a iable h ough a linea eg ession
because he ou come o he eg ession equa ion p o ides al-
ues ha need o be assigned o one o he h ee assu ance
le els since he e a e no in e media e le els.
8. Conclusion
Sus ainabili y epo ing has eme ged as a majo elemen
o co po a e epo ing in ecen yea s. The challenges a ising
om clima e change as well as inc easing s akeholde awa e-
ness a e some o he key d i ing ac o s. While a numbe o
companies ha e been epo ing on hese issues olun a ily
o some ime, oday almos all o he majo companies a e
ge ing on boa d, pa ly d i en by egula o y equi emen s.
The EU axonomy ep esen s one o he key egula o y oun-
da ions in his ield. No only does i igh en epo ing e-
qui emen s, i also encou ages eal e ec s by edi ec ing cap-
i al lows and company ac i i ies owa ds mo e sus ainabil-
i y and educing p ac ices such as g eenwashing (Eu opean
Union (EU), 2020, p. 14).
This hesis me ges he con en opic o sus ainabili y e-
po ing wi h he me hodological opic o ex ual analysis. As
sus ainabili y epo ing is mainly p esen ed in quali a i e ex
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235232
Figu e 7: Rela ionship be ween Ma ke _CAP and Assu ance_LVL
o m, his combina ion i s oge he qui e well. The me hod-
ology o ex ual analysis has become e y popula in aca-
demic esea ch and among he gene al public h ough he
in oduc ion o ex -gene a ing models such as Cha GPT.
This hesis makes se e al con ibu ions. I con ibu es o
he li e a u e in he a ea o audi ing, especially he audi ing
o non- inancial epo ing, and o he li e a u e in he a ea
o Eu opean epo ing equi emen s. The wo main a eas o
ocus o his hesis a e di ided, wi h he i s being a li e a-
u e e iew and analysis o a ious ex ual analysis me hods.
The li e a u e e iew ocuses p ima ily on he inance and
accoun ing domain, de ailing he ex ual analysis echniques
u ilized in p io esea ch. The me hodology o e iew p o-
ides a comp ehensi e examina ion o he ad an ages, disad-
an ages, and limi a ions o each me hod. This e iew con-
ibu es o he eade ’s unde s anding o ex ual analysis ca-
pabili ies and po en ial applica ions, which will help eade s
iden i y app op ia e me hods o hei own ex ual analysis
esea ch. A u he con ibu ion lies in he esul s o he in-
es iga ion o he impac o audi assu ance on he quali y o
sus ainabili y epo ing. The esul s we e ob ained h ough a
combina ion o ex ual analysis me hods and mul iple linea
eg essions.
The unique aspec o he me hodology in his hesis is ha
he essen ial da a o he analysis is ob ained wi h he help
o GPT 3.5, a eely accessible LLM wi h ex comp ehension
and gene a ion capabili ies. The model is used o analyze a
de ined p oblem. Ra ings o sco es ha e been used o iden i y
genuine economic connec ions equen ly in p io s udies. In
his case, he LLM is u ilized o gene a e a a ing based solely
on he ex o he non- inancial epo s o he sampled com-
panies. By cons uc ing a speci ic p omp , i is possible o
de e mine exac ly which ac o s should be included o c ea e
such a a ing. Fo he pu pose o his hesis, he compliance
o he epo ing wi h he equi emen s o he EU axonomy
in ela ion o wo speci ic axonomy objec i es has been de-
ined as a quali y ea u e ha de ines he a ing. Typically,
he e a e no es ablished a ings o such speci ic objec i es.
LLMs ha e been u ilized in p io s udies in a simila man-
ne . Fo example, Kim e al. used ano he GPT 3.5 model o
summa ize componen s o co po a e disclosu e, and ound
ha hese summa ies gene a ed each had a s onge posi i e
o nega i e sen imen han he o iginal epo ing. Acco d-
ingly, GPT appea s o be able o il e noise om he ex s,
imp o e he in o ma ion con en and p esen mo e ele an
insigh s han he o iginal epo ing (Kim e al., 2023, pp. 1,
2, 5, 15–16, 19–20, 30).
The in o ma ion con en o sus ainabili y epo s in his
hesis was signi ican ly educed, wi h only he GPT_Ra ing
emaining. While simila , he app oach he e is much mo e
d as ic, as Kim e al. elimina ed abou 70% o he o iginal
epo ing, while he e he en i e ex was elimina ed and e-
placed by he GPT_Ra ing as a single numbe emaining. This
signi ican educ ion may accoun o why he analyses o his
hesis did no yield many signi ican esul s.
The eg ession esul s om he analysis o H1 show a
nega i e bu insigni ican co ela ion be ween he le el o
assu ance and he epo ing compliance, displayed ia he
GPT_Ra ing a iable, con a y o he expec a ions. Addi ion-
ally, i was ound ha companies wi h a highe ESG sco e
also ha e a highe GPT_Ra ing and a e he e o e mo e likely
o mee he equi emen s o he EU axonomy. The analysis
o H2 indica es a posi i e co ela ion be ween he ESG_Sco e
and he File_Size a iable. Thus, sus ainable ac ing com-
panies disclose mo e in o ma ion, which suppo s H2. Fi-
nally, he las eg ession analyses could no con i m he hy-
po hesis o H3, which s a es ha sus ainable companies a e
mo e likely o ha e hei non- inancial epo ing ex e nally
audi ed, as he a ibu ion heo y would sugges .
The esea ch esul s a e subjec o a numbe o limi a-
ions. Fi s , he sample size which anges om 73 o 90 com-
panies, depending on he analysis, is ela i ely small. This is
A. G ommes /Junio Managemen Science 10(1) (2025) 201-235 233
due o he ac ha he sus ainabili y epo s had o be manu-
ally e ie ed om company websi es and being u he p o-
cessed o he analysis. In addi ion, he a iable GPT_Ra ing
is p one o e o . On one hand, he analysis o his a iable is
ounded solely on sec ions ex ac ed om sus ainabili y e-
po s, and no on he en i e epo s hemsel es. The ex ac-
ion p ocedu e was based on an in ui i e ye un es ed ap-
p oach. The second and mo e in luen ial sou ce o e o is
he a ing o he sec ions by Cha GPT i sel . Due o ime con-
s ain s, he a ing was done using a ze o-sho app oach. The
model was no ine- uned and has no been ed wi h aining
da a in ad ance. Addi ionally, he esul s we e no subjec ed
o obus ness es s. Such obus ness es ing could be con-
side ed in u u e esea ch, ei he by compa ing he esul s
wi h exis ing, ex e nally alida ed sco es, o by compa ing
hem wi h a po olio o esul s om o he ex ual analysis
me hods. In his hesis, he esul s p esen da a ha a e con-
cei able bu no ully comp ehensible.
Changes in he p omp can a ec he ou pu , esul ing in
di e en a ing esul s o he same epo . This e en hap-
pens i he p omp is no changed (Appendix E). The cause
may be an o e load o he GPT 3.5 model u ilized, which
is op imized o ex ou pu and has limi s o app oxima ely
8,000 okens. OpenAI has announced new models capable
o cap u ing an inpu con ex o up o 128,000 okens. These
models a e designed o p oduce ep oducible ou pu s, esul -
ing in lowe a iances. Such models can enhance he quali y
o analyses and ensu e comp ehensi e epo s. Howe e , a
he ime o pe o ming he analysis (No embe 2023), he
new models we e no ye a ailable.22
This hesis has in oduced a numbe o ex ual analysis
me hods, anging om he simples models wi h almos no
ma hema ical o echnical dep h, o models using he la es
echnical achie emen s in he ield o machine lea ning. Fo
he analysis o he hypo heses o his hesis, he applica ion o
an LLM has p o en o be an exhaus i e ins umen . Despi e
he eme gence and popula i y o mode n echniques, adi-
ional me hods a e s ill commonly u ilized in esea ch due o
hei ease o unde s anding and in ui i e esul s. Howe e ,
he u u e o ex ual analysis lies in he possibili ies p esen ed
by he ad ancing s a e-o - he-a . Gene a i e LLMs open up
nume ous possibili ies in all ields o esea ch. Tex ual da a
is becoming inc easingly ele an in he ield o accoun ing.
Models like Cha GPT can be pa icula ly help ul in dealing
wi h he g owing impo ance o ex in epo ing and he as-
socia ed in o ma ion o e load (Kim e al., 2023, p. 30). Re-
sea che s should no o e look hese oppo uni ies and u ilize
he possibili ies o e ed by hese and simila models in hei
esea ch.
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