Sakuma, Sugu u; Fu u ani, Tomoyuki
A icle
S andalone alua ion me hod o so wa e-as-a-se ice
ope a ional knowledge de i ed om human in ellec ual
capi al quali a i e changes
Adminis a i e Sciences
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MDPI – Mul idisciplina y Digi al Publishing Ins i u e, Basel
Sugges ed Ci a ion: Sakuma, Sugu u; Fu u ani, Tomoyuki (2024) : S andalone alua ion me hod o
so wa e-as-a-se ice ope a ional knowledge de i ed om human in ellec ual capi al quali a i e
changes, Adminis a i e Sciences, ISSN 2076-3387, MDPI, Basel, Vol. 14, Iss. 4, pp. 1-12,
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Ci a ion: Sakuma, Sugu u, and
Tomoyuki Fu u ani. 2024. S andalone
Valua ion Me hod o
So wa e-as-a-Se ice Ope a ional
Knowledge De i ed om Human
In ellec ual Capi al Quali a i e
Changes. Adminis a i e Sciences 14:
71. h ps://doi.o g/10.3390/
admsci14040071
Recei ed: 23 Janua y 2024
Re ised: 29 Ma ch 2024
Accep ed: 1 Ap il 2024
Published: 5 Ap il 2024
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adminis a i e
sciences
A icle
S andalone Valua ion Me hod o So wa e-as-a-Se ice
Ope a ional Knowledge De i ed om Human In ellec ual
Capi al Quali a i e Changes
Sugu u Sakuma * and Tomoyuki Fu u ani
Facul y o Policy Managemen , G adua e School o Media and Go e nance, Keio Uni e si y,
Fujisawa-shi 252-0882, Kanagawa, Japan; [email p o ec ed]
*Co espondence: [email p o ec ed]; Tel.: +81-466-49-3623
Abs ac : This s udy ocuses on digi al ope a ional knowledge belonging o na u al pe sons and
p oposes a g een ield app oach o di e en ia e he alue o in angibles om ha o human in ellec ual
capi al. Ou esea ch app oach in ol es wo assessmen s. Assessmen 1 e alua es in angible asse s
using he in e nally gene a ed goodwill (IGG) measu e. We analyze ime-se ies IGG da a o six
digi al sec o s, using he op 90 so wa e-as-a-se ice (SaaS) companies as a benchma k. The esul s
indica e ha he IGG o he SaaS benchma k is highe han he o al IGG o he six sec o s. Assessmen
2 ocuses on he co ela ion be ween digi al labo in es men and digi al in es men e u ns be o e
and a e 2013 o he six sec o s o iden i y posi i e and nega i e co ela ions om 2013 onwa d. The
esul s indica e ha , since 2013, a quali a i e change has occu ed in digi al labo capi al ha has
no been e lec ed in inancial s a emen s because o accoun ing dis o ions and ha he e u ns on
in es men o digi al labo ha e been unde es ima ed. The s andalone alua ion o digi al know-how
ha belongs o na u al pe sons, p e iously based on ope a ing expense, will be based on capi al
expendi u e. In addi ion, amo iza ion will ha e he same con ibu ion as dep ecia ion o angible
asse s o alue c ea ion.
Keywo ds: SaaS; digi al in angible; alue ans e ; human in ellec ual capi al; accoun ing dis o ion;
digi al labo p oduc i i y
1. In oduc ion
The global digi al e olu ion led o he ise o di e se digi al asse s, no ably ega ding
he assessmen o employee digi al li e acy, also e e ed o as digi al ope a ional p o iciency.
Digi al in angibles p esen challenges o cu en accoun ing egula ions as hey a e o en
di icul o iden i y as sou ces o e enue, leading o signi ican accoun ing disc epancies
(Viscon i 2020). The alua ion o such human capi al in angibles as asse s o income is a
slippe y and complex p ocess ha esul s in hei una ailabili y o eco ding on a balance
shee (BS) (Qin 2017).
In his s udy, we examined he human capi al alue o employees o so wa e-as-a-
se ice (SaaS) companies and de eloped a quan i iable alua ion me hod o hese in angi-
ble asse s. The deba e on digi al angibles is impo an because he ele ance and eliabili y
o in angible asse s in accoun ing and inancial epo s ha e been ques ioned. The li e a u e
on he consequences o a lack o accoun ing ecogni ion o in angibles has been c i ically
e iewed (Zéghal and Maaloul 2011), bu he ela ionship be ween accoun ing choices asso-
cia ed wi h in angibles and hei alue ele ance, and he mi iga ing e ec s o i m li ecycles,
mus be conside ed (Jaa a 2011). Pas ini ia i es, in con as wi h con empo a y p ac ices,
ha e p o ided solu ions o accoun ing o in angible asse s (Lai 2011). Howe e , he e is
con inuing deba e on how o ep esen digi al in angible alua ions in accoun ing. Value
ele ance depends on how well accoun ing cap u es i m digi al in angibles, how hey a e
alued, and he us wo hiness o he assessed alues (Saunde s and B ynjol sson 2016).
Adm. Sci. 2024,14, 71. h ps://doi.o g/10.3390/admsci14040071 h ps://www.mdpi.com/jou nal/admsci
Adm. Sci. 2024,14, 71 2 o 12
The cu en concep o labo p oduc i i y does no accu a ely e lec he alue o
SaaS digi al ope a ional knowledge. The e o e, his s udy p oposes ede ining “digi al
labo p oduc i i y” and eclassi ying “labo in es men ” as “digi al labo in es men ” and
“capi al in es men ” as “digi al capi al in es men ”. We emphasize he impo ance o
disclosing he quan i iable alue o an employee’s knowledge, expe ience, c ea i i y, and
expe ise, which o m he human capi al asse s o a company. Ou goal is o legi imize he
ecogni ion o SaaS ope a ional knowledge as an in angible asse on a BS h ough sound
analy ical a gumen s. Addi ionally, i digi al in angibles a e ecognized and lis ed on a BS,
hey can p o ide a ounda ion o he implemen a ion o he basic income concep s.
Rega ding basic income concep s, he alue o digi al ope a ions lies in human capi al.
De Ops is used as a quan i a i e index o human capi aliza ion o le e age ad anced digi al
ope a ions as a sou ce o e enue. De Ops inco po a es a ious ope a ional me hodologies
wi h he concep o con inuous in eg a ion/con inuous deploymen (CI/CD) and ep esen s
a no el s uc u ing me hod ha ea s digi al ope a ional knowledge as a new e enue
sou ce (Badshah e al. 2020).
NFTs (non- ungible okens) and c yp ocu encies ha e ecen ly ma e ialized, and a
ma ke o ading hem has been o med. Howe e , since i is impossible o ma e ialize
digi al ope a ion capabili ies, companies ha e been o ced o in e nalize he digi al ope a-
ion alue o hei employees, gi ing ise o he idea o CI/CD. The p e ailing mains eam
agile me hod e ol ed in o de elopmen o ms such as De Ops and AIOps, which in eg a e
de elopmen and ope a ions and con ain ci cula i y p ope ies.
The emainde o his pape is o ganized as ollows. Sec ion 2p esen s he li e a u e
e iew; Sec ion 3de ails he esea ch app oach adop ed in his s udy; Sec ion 4p esen s
he esul s o wo di e en ypes o assessmen s; Sec ion 5discusses addi ional conside a-
ions based on he esul s p esen ed in he p e ious sec ions; and Sec ion 6summa izes
ou conclusions.
2. Li e a u e Re iew
2.1. Wha Is Accoun ing Dis o ion?
The ea men o angible and in angible in es men s in cu en accoun ing s anda ds
can lead o inancial me ic dis o ions and mis ep esen a ion o a company’s alue. The
in angible in es men s o digi al companies a e excluded om inancial epo s because
hey canno be capi alized, and esea ch indica es a di e ence in alue ele ance be ween
angible and in angible in es men s. Accoun ing dis o ions esul om acc ual accoun -
ing and may be caused by accoun ing s anda ds, es ima ion e o s, and conse a ism.
Conse a ism ends o p oduce a pessimis ic bias in inancial s a emen s.
2.2. Rela ionship be ween Digi al In angibles and Human Capi al
The digi al e olu ion has in oduced new in angible asse s ha a e di icul o alue
accu a ely, including NFTs and i ual cu ency c edi wo hiness. Digi al in angibles
ela ed o human in ellec ual capi al, such as ope a ional knowledge, a e challenging o
quan i y in inancial e ms. This has led o accoun ing dis o ions, such as base e osion
and p o i shi ing (BEPS), based on he absence o a social consensus ega ding how o
eco d hese in angibles as human in ellec ual capi al. The O ganiza ion o Economic Co-
ope a ion and De elopmen (OECD) BEPS Con e ence o 2018 de ines digi al in angibles as
ha d- o- alue in angibles (HTVI) (OECD 2018). BEPS occu s when companies shi p o i s
o low- ax ju isdic ions, esul ing in e enue losses in high- ax coun ies.
2.3. Va ious App oaches o Digi al In angibles Valua ion
T adi ional alua ion me hods such as he discoun ed cash low me hod a e no longe
adequa e o aluing digi ally ad anced companies, leading o he de elopmen o a ious
new app oaches. One such app oach is he g een ield me hod o in angible asse alua ion
based on he IVS 210 s anda d, which can cap u e he alue o s andalone human capi al
and accommoda e dynamic changes in digi al asse s (Clohessy e al. 2020). Measu emen
Adm. Sci. 2024,14, 71 3 o 12
me hods o SaaS pla o m con ibu ions o GDP c ea ion and p oduc i i y imp o emen
o sel -employed indi iduals ha e been sugges ed (Ahmad and Sch eye 2016). These
include s a is ical measu emen me hods (Ragha an e al. 2020;Labaye and Remes 2015;
Simonsson and Magnusson 2018), empi ical analyses o digi al alue chain inno a ion (Qu
e al. 2017), b and alue pla o m c ea ion (S i am e al. 2006), examina ion o he impac
o he echnology pa adox on p oduc i i y (A bia e al. 2019), and co ela ions be ween
spa ial economic models and economic g ow h. Demand o ecas ing modeling (Kou en zes
and Pe opoulos 2016) and i ual cu ency alua ion modeling a e among he p oposed
p icing me hods (Hun e and Ke 2019).
Na ional empi ical case s udies ha e been conduc ed in Ge many, ocusing on he
p oduc i i y ac o s o in e nal and ex e nal collabo a ion inno a ion (Hensen and Dong
2020), and in Canada, examining he ela ionship be ween digi al aining and economic
g ow h (Walke e al. 2018). Mul i- ac o p oduc i i y s udies using sec o -le el da a
om he Eu opean Union ha e also been conduc ed (Em aloma is 2017), and digi al
inno a ion alua ion in A ica has been analyzed using a sec o -based app oach (Fos e
e al. 2018). P oduc i i y g ow h ac oss many de eloped economies has been analyzed
using a mul i-coun y and mul i-sec o app oach (Remes e al. 2018). In his s udy, we
adop ed he sec o -based me hod p oposed o aluing digi al in angibles in Japan, which
p o ides insigh s in o e alua ing he alue o digi al in angible asse s in he Japanese
ma ke accu a ely.
3. Ma e ials and Me hods
3.1. Resea ch App oach
Ou esea ch app oach in ol ed wo assessmen s. Assessmen 1 examined he alue
ans e phenomenon ac oss digi al sec o s in he Japanese ma ke . The SaaS bubble in
2020 was used as a baseline o he g een ield me hod, which calcula es he s andalone
alue o each sec o . To isola e he alue o in angible asse s, in e nally gene a ed goodwill
(IGG) was used as an e alua ion index o in angible asse s, and ime-se ies da a on IGG
di e ences we e calcula ed o six digi al sec o s con aining 90 SaaS benchma k companies.
β
-co ec ion was pe o med o each sec o o co ec he accoun ing dis o ion caused by
human capi al. Assessmen 2 examined he co ela ion be ween digi al labo in es men
and in es men e u ns in six sec o s be o e and a e 2013 o iden i y sec o s wi h posi i e
and nega i e co ela ions. The goal o his assessmen was o e i y he consis ency o he
in angible asse in low and ou low sec o s wi h espec o he co ela ion be ween digi al
labo in es men and in es men e u ns. Inconsis en sec o s indica e ha he accoun ing
dis o ions migh be caused by quali a i e changes in digi al labo capi al. The p oposed
alua ion me hod conside s he human capi al ac o o digi al ope a ions when alua ing
digi al in angible asse s.
3.2. Assessmen 1: Value T ans e be ween SaaS Sec o s in he Japanese Digi al Ma ke
3.2.1. Pu pose
We de ine he p ice- o-book a io (PBR) mul iples o ep esen IGG. Using he op
90 SaaS lis ed companies in he Japanese ma ke in 2020 as a benchma k, IGG is spli by
digi al sec o o calcula e he IGG di e ences be ween sec o s. The IGG alues and PBR
mul iples ep esen he g oss alua ion o digi al in angibles. The his o ical ansi ion o
IGG is e i ied agains he benchma k.
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip ion-
ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he Japanese
ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
Adm. Sci. 2024,14, 71 4 o 12
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se ices
(N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies), sys-
em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), specialized
in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 companies)
(Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020
p(F
≤
) p(T
≤
)
#Non-SaaS@2020
p(F
≤
) p(T
≤
)
#To al
In as uc u e
Se ices 12 29.3% 0.093 ‡0.483 †1
29 70.7% 0.471 ‡0.483 †7
41
13.3% 4.8%
BPO Se ices 35.2% 0.000 ‡0.000 †2
55 94.8% 0.396 ‡0.436 †8
58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034 ‡0.001 †3
179 91.8% 0.422 ‡0.440 †9
195
17.8% 29.5%
So wa e Se ices 43 22% 0.011 ‡0.000 †4
152 78% 0.221 ‡
0.279
†10
195
47.8% 25.1%
Specialized
In o ma ion Media 95.4% 0.000 ‡0.000 †5
157 94.6% 0.357 ‡
0.305
†11
166
10% 25.9%
Media Ad e ising
Se ices 717% 0.074 ‡0.065 †6
34 83% 0.478 ‡
0.409
†12
41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡
= 0.05,
†1
= 0.041,
†2
= 0.160,
†3
=
−
3.72,
†4
=
−
4.13,
†5
=
−
9.34,
†6
=
−
0.22,
†7
=
−
0.04,
†8
= 0.16,
†9 = 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed and
he s a is ical signi icance o he a iance and mean a e examined. As shown in Table 1,
he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in
a iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since
he popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e
migh be c i ical ac o s ha cause his kind o s ong he e ogenei y.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks di e en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
PBR ep esen s he di e ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
Adm. Sci. 2024, 14, x FOR PEER REVIEW 5 o 13
Figu e 1. PBR ime se ies o he benchma k o 2020′s op 90 SaaS companies.
3.2.5. S andalone Value o In angible Asse s and Value T ans e be ween Digi al Sec o s
Using he g een ield me hod, we calcula e he s andalone alue o he in angibles
gene a ed by each digi al sec o . The six digi al sec o s include he op 90 SaaS ad anced
companies in 2020; he e o e, ⊿PBR ep esen s he s andalone alue o SaaS in angibles.
Addi ionally, SaaS in angible asse alue is alloca ed o each sec o based on he a io o
SaaS companies belonging o each sec o .
We di ide hose da a in o wo pe iods (2006–2012 and 2013–2021) in Assessmen 2.
The PBR eached i s peak in 2020. TOPIXβ is he expec ed e u n o one uni o in es -
men . Sec o idiosync asies should be co ec ed using he β alue, which is he expec ed
e u n on in es men o each sec o . In he Resul s sec ion, we discuss he o al alue o
SaaS in angibles and he seg ega ed alue o human in ellec ual capi al-o ien ed digi al
ope a ional in angibles.
3.3. Assessmen 2: Quali a i e Changes in Capi al In es men and Labo In es men
3.3.1. Pu pose
To de e mine he alue o he SaaS ope a ion me hod alone, we conside he quali a-
i e changes in capi al and labo in es men s s emming om digi al inno a ion and e i y
hei consis ency wi h he alue ans e be ween digi al sec o s, which is de e mined in
Assessmen 1. Any iden i ied disc epancies may indica e he causes o accoun ing dis o -
ions. Speci ically, SaaS adop ion may be ans o ming he quali y o digi al p oduc i i y.
Al hough digi al in angible asse s can enhance alue, accu a ely e lec ing he alue
ans e induced by SaaS in inancial s a emen s is difficul . The e o e, he e is a need o
a be e unde s anding o he ole o human in ellec ual capi al in digi al ope a ional
knowledge as a ac o in he alue ans e p ocesses ac oss he SaaS sec o (Van A k 2016).
3.3.2. Analysis Me hod
We e i y diffe ences in in es men efficiency be ween cases wi h and wi hou he
inclusion o in angible asse s. We compa e he a es o e u n on labo in es men o e
one uni o capi al in es men . By compa ing diffe ences in capi al and labo in es men s
be ween SaaS and non-SaaS companies, we in es iga e he quali a i e changes in capi al
and labo in es men s.
4. Resul s
4.1. Value T ans e Phenomenon be ween Digi al Sec o s
In Assessmen 1, we obse e h ee dis inc ends in he his o ical pe o mance o he
PBR in he digi al sec o , as shown in Figu e 2. The i s end, which spans om 2006 o
2012, e eals ha all six sec o s emain wi hin he ange o −1 < ⊿PBR < 1. The second
end, om 2013 o 2020, highligh s a gene al unde pe o mance in e ms o PBR ac oss
all sec o s wi h ⊿PBR < 0 o all six sec o s in 2019 and 2020. Finally, he hi d end, which
eme ges a e 2021, exhibi s an imp o emen in PBR pe o mance.
Figu e 1. PBR ime se ies o he benchma k o 2020’s op 90 SaaS companies.
Adm. Sci. 2024,14, 71 5 o 12
3.2.5. S andalone Value o In angible Asse s and Value T ans e be ween Digi al Sec o s
Using he g een ield me hod, we calcula e he s andalone alue o he in angibles
gene a ed by each digi al sec o . The six digi al sec o s include he op 90 SaaS ad anced
companies in 2020; he e o e,
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
PBR ep esen s he s andalone alue o SaaS in angibles.
Addi ionally, SaaS in angible asse alue is alloca ed o each sec o based on he a io o
SaaS companies belonging o each sec o .
We di ide hose da a in o wo pe iods (2006–2012 and 2013–2021) in Assessmen
2. The PBR eached i s peak in 2020. TOPIX
β
is he expec ed e u n o one uni o
in es men . Sec o idiosync asies should be co ec ed using he
β
alue, which is he
expec ed e u n on in es men o each sec o . In he Resul s sec ion, we discuss he o al
alue o SaaS in angibles and he seg ega ed alue o human in ellec ual capi al-o ien ed
digi al ope a ional in angibles.
3.3. Assessmen 2: Quali a i e Changes in Capi al In es men and Labo In es men
3.3.1. Pu pose
To de e mine he alue o he SaaS ope a ion me hod alone, we conside he quali a i e
changes in capi al and labo in es men s s emming om digi al inno a ion and e i y hei
consis ency wi h he alue ans e be ween digi al sec o s, which is de e mined in Assess-
men 1. Any iden i ied disc epancies may indica e he causes o accoun ing dis o ions.
Speci ically, SaaS adop ion may be ans o ming he quali y o digi al p oduc i i y.
Al hough digi al in angible asse s can enhance alue, accu a ely e lec ing he alue
ans e induced by SaaS in inancial s a emen s is di icul . The e o e, he e is a need
o a be e unde s anding o he ole o human in ellec ual capi al in digi al ope a ional
knowledge as a ac o in he alue ans e p ocesses ac oss he SaaS sec o (Van A k 2016).
3.3.2. Analysis Me hod
We e i y di e ences in in es men e iciency be ween cases wi h and wi hou he
inclusion o in angible asse s. We compa e he a es o e u n on labo in es men o e
one uni o capi al in es men . By compa ing di e ences in capi al and labo in es men s
be ween SaaS and non-SaaS companies, we in es iga e he quali a i e changes in capi al
and labo in es men s.
4. Resul s
4.1. Value T ans e Phenomenon be ween Digi al Sec o s
In Assessmen 1, we obse e h ee dis inc ends in he his o ical pe o mance o he
PBR in he digi al sec o , as shown in Figu e 2. The i s end, which spans om 2006 o
2012, e eals ha all six sec o s emain wi hin he ange o
−
1 <
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
PBR < 1. The second
end, om 2013 o 2020, highligh s a gene al unde pe o mance in e ms o PBR ac oss all
sec o s wi h
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
PBR < 0 o all six sec o s in 2019 and 2020. Finally, he hi d end, which
eme ges a e 2021, exhibi s an imp o emen in PBR pe o mance.
These esul s demons a e minimal a ia ion in IGG among he six digi al sec o s
un il 2012. Howe e , since he onse o digi iza ion in 2013, i has become e iden ha
he alue shi ed om companies ha had ailed o adap o he SaaS model o hose ha
had success ully ansi ioned. This end can be obse ed in he unde pe o mance o all
sec o s compa ed wi h he op 90 SaaS companies benchma k in 2020. Each digi al sec o
included bo h SaaS and non-SaaS companies. The mo e SaaS he digi al sec o adop s,
he g ea e he alue ans e . Addi ionally, alue ans e occu s h ough he medium o
un ecognized angibles. One o he c i ical ac o s migh be human in ellec ual capi al ha
media es he alue ans e o he digi al sec o , which was p e iously un ecognized as a
SaaS alue. This means ha SaaS has bo h pe cei ed and un ecognized human in ellec ual
capi al. Addi ional esea ch is equi ed o unde s and how SaaS human in ellec ual capi al
d i es his phenomenon.
Adm. Sci. 2024,14, 71 6 o 12
Adm. Sci. 2024, 14, x FOR PEER REVIEW 6 o 13
Figu e 2. His o ical ⊿PBR pe o mance o six digi al sec o s.
These esul s demons a e minimal a ia ion in IGG among he six digi al sec o s
un il 2012. Howe e , since he onse o digi iza ion in 2013, i has become e iden ha he
alue shi ed om companies ha had ailed o adap o he SaaS model o hose ha had
success ully ansi ioned. This end can be obse ed in he unde pe o mance o all sec-
o s compa ed wi h he op 90 SaaS companies benchma k in 2020. Each digi al sec o
included bo h SaaS and non-SaaS companies. The mo e SaaS he digi al sec o adop s, he
g ea e he alue ans e . Addi ionally, alue ans e occu s h ough he medium o un-
ecognized angibles. One o he c i ical ac o s migh be human in ellec ual capi al ha
media es he alue ans e o he digi al sec o , which was p e iously un ecognized as a
SaaS alue. This means ha SaaS has bo h pe cei ed and un ecognized human in ellec ual
capi al. Addi ional esea ch is equi ed o unde s and how SaaS human in ellec ual capi-
al d i es his phenomenon.
4.2. Calcula ion o he Value T ans e o SaaS Companies
Table 2 de ines he me hodology used o calcula e he s andalone alues o in angi-
bles and a es o alue ans e o each sec o . Ini ially, he in angible asse alue is de e -
mined by applying he β co ec ion o each benchma k and sec o . Subsequen ly, he al-
loca ion o in angible asse s wi hin each sec o and SaaS a ios o all companies in he six
sec o s a e de e mined. The g oss amoun o in angibles in each sec o and he alue ans-
e a io ela i e o he benchma k a e hen calcula ed. Based on he benchma k in angible
asse alue o 6.23 (g oss amoun : JPY 3991B) and o al in angible asse alue o he six
sec o s o 3.41 (g oss amoun : JPY 3476B), he diffe ence o 2.82 (g oss amoun : JPY 515B)
ep esen s he alue ans e o SaaS companies. Fu he , by examining he diffe ences in
sec o alloca ion, we de e mine ha he e a e sec o s wi h in lows o in angible asse
alue, namely in as uc u e se ices and media ad e ising se ices, and sec o s wi h
ou lows o in angible asse alue, namely BPO se ices, sys em de elopmen , so wa e
se ices, and specialized in o ma ion media.
Figu e 2. His o ical
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
PBR pe o mance o six digi al sec o s.
4.2. Calcula ion o he Value T ans e o SaaS Companies
Table 2de ines he me hodology used o calcula e he s andalone alues o in angibles
and a es o alue ans e o each sec o . Ini ially, he in angible asse alue is de e mined
by applying he
β
co ec ion o each benchma k and sec o . Subsequen ly, he alloca ion
o in angible asse s wi hin each sec o and SaaS a ios o all companies in he six sec o s
a e de e mined. The g oss amoun o in angibles in each sec o and he alue ans e
a io ela i e o he benchma k a e hen calcula ed. Based on he benchma k in angible
asse alue o 6.23 (g oss amoun : JPY 3991B) and o al in angible asse alue o he six
sec o s o 3.41 (g oss amoun : JPY 3476B), he di e ence o 2.82 (g oss amoun : JPY 515B)
ep esen s he alue ans e o SaaS companies. Fu he , by examining he di e ences in
sec o alloca ion, we de e mine ha he e a e sec o s wi h in lows o in angible asse alue,
namely in as uc u e se ices and media ad e ising se ices, and sec o s wi h ou lows
o in angible asse alue, namely BPO se ices, sys em de elopmen , so wa e se ices,
and specialized in o ma ion media.
Table 2. SaaS in angible alues and alue ans e a es o six digi al sec o s ia SaaS human
in ellec ual capi al.
Popula ion
TOPIX
β@2020
(a)
PBR@2020
(b)
SaaS
In angible
Value a e
Co ec ion
(c = b/a)
In angible
Alloca ion
(d = c ×
In a-sec o
Ra io)
In angible
Alloca ion
(e = c ×Ra io
wi hin
Benchma k)
Value T ans e
(G oss)
( = d −e)
Value T ans e
% ( /F)
Top 90 SaaS
Companies 90 1.16 7.23 6.23
(C) NA 6.23
(100%)
2.82
(F = C−D)
In as uc u e
Se ices 41 1.35 5.70 4.22 1.24
(29.3%)
0.83
(13.3%) 0.41 14.5%
BPO Se ices 58 1.23 3.00 2.44 0.13
(5.2%)
0.20
(3.3%)
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
0.07
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
2.48%
Sys em
De elopmen 195 1.01 2.42 2.40 0.20
(8.2%)
1.11
(17.8%)
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
0.91
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
32.3%
So wa e
Se ices 195 1.04 4.90 4.71 1.04
(22%)
2.98
(47.8%)
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
1.94
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
68.8%
Specialized
In o ma ion
Media
166 1.41 4.15 2.94 0.15
(5.4%)
0.62
(10%)
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
0.47
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
16.7%
Media
Ad e ising
Se ices
41 0.97 3.73 3.85 0.65
(17%)
0.49
(7.8%) 0.16 5.6%
All Six Digi al
Sec o s 696 1.15 3.82 3.32 3.41
(D) NA
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
2.82
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
100%
Adm. Sci. 2024,14, 71 7 o 12
4.3. Digi al Se ice P oduc i i y In es iga ion
Thus a , p oduc i i y discussions ha e been p esen ed om he pe spec i e o angible
asse s. In Assessmen 2, we p esen wo cases. Case I delinea es he p e ailing con ex
whe ein he capi aliza ion o in angible asse s is p osc ibed. Case II delinea es an idealized
amewo k whe ein bo h he capi aliza ion o in angible asse s on he balance shee and
hei subsequen amo iza ion a e sanc ioned.
To ocus on digi al in angible asse s, digi al se ice p oduc i i y is de ined as ollows:
Digi al Se ice P oduc i i y = (Ou pu o Case II-Case I) ÷(Inpu o Case II-Case I)
Case I: Do no ecognize any in angible asse s
Ou pu : Value added by angible asse s = Ope a ing Income + Pe sonnel Expenses + Ren +
Taxes and Le ies in Manu ac u ing, Selling, and Gene al Adminis a ion Expenses + Pa en
Royal y + Dep ecia ion
Inpu : Tangible asse s and numbe o employees Va iable Y_Case I = Value added by
angible asse s
÷
angible asse s Va iable X_Case I = Value added by angible asse s
÷
numbe o employees
Case II: Recognize bo h angible and in angible asse s
Ou pu : Value added by bo h angible and in angible asse s = Ope a ing Income + Pe sonnel
Expenses + Ren + Taxes and Le ies in Manu ac u ing, Selling, and Gene al Adminis a ion
Expenses + (Ne ) In angible Fixed Asse s + (Ne ) Digi al In ellec ual p ope y + Dep ecia ion
+ Amo iza ion
Inpu : Capi al asse s and numbe o employees Va iable Y_Case II = Value added by bo h
angible and in angible asse s
÷
capi al asse s Va iable X_Case II = Value added by bo h
angible and in angible asse s ÷numbe o employees
The wo cases o only angible asse s and bo h angible and in angible asse s we e
examined as de ined below. He e, he a iable Y ep esen s he a e o e u n on capi al in-
es men , and he a iable X ep esen s he a e o e u n on labo in es men . Addi ionally,
Case I does no ecognize in angible asse s, whe eas Case II does ecognize in angible asse s.
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
X = a iable X_Case II- a iable X_Case I
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
Y = a iable Y_Case II- a iable Y_Case I
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
X ep esen s quali a i e human capi al changes in labo in es men , and
Adm. Sci. 2024, 14, x FOR PEER REVIEW 4 o 13
3.2.2. Ex ac ion Rule o SaaS-Ad ancing Companies
We de ine SaaS-ad ancing companies as hose who (1) p o ide hei own subsc ip-
ion- ype se ice, (2) a e included in he op 90 SaaS companies in e ms o sales in he
Japanese ma ke in 2020, and (3) a e lis ed on he Tokyo S ock Exchange.
3.2.3. Digi al Sec o s o Which he 90 SaaS Companies Belong
The six sec o s o which he op 90 SaaS companies belong a e IT in as uc u e se -
ices (N = 41 companies), business p ocess ou sou cing (BPO) se ices (N = 58 companies),
sys em de elopmen (N = 195 companies), so wa e se ices (N = 195 companies), special-
ized in o ma ion media (N = 166 companies), and media ad e ising se ices (N = 41 com-
panies) (Ami i 2022). The de ails o hese sec o s a e lis ed in Table 1.
To check he s a is ical signi icance o each o he six sec o s o he SaaS 90 company
popula ion and he non-SaaS popula ion, F- es and - es o each sec o a e conduc ed
and he s a is ical signi icance o he a iance and mean a e examined. As shown in Table
1, he SaaS popula ion o mos o all sec o s is s a is ically signi ican , while he non-SaaS
popula ion is no signi ican . In o he wo ds, he SaaS popula ion is he e ogeneous in a -
iance and mean, while he non-SaaS popula ion is homogeneous. Fu he mo e, since he
popula ion o 90 SaaS companies is ex emely he e ogeneous, i sugges s ha he e migh
be c i ical ac o s ha cause his kind o s ong he e ogenei y.
Table 1. Ra io o SaaS (Uppe %: in a-sec o a io; lowe %: a io wi hin benchma k).
Digi al Sec o #SaaS@2020 p (F ≤ ) p (T ≤ ) #Non-
SaaS@2020 p (F ≤ ) p (T ≤ ) #To al
In as uc u e Se ices 12 29.3% 0.093
‡ 0.483
†1 29 70.7% 0.471
‡ 0.483
†7 41
13.3% 4.8%
BPO Se ices 3 5.2% 0.000
‡ 0.000
†2 55 94.8% 0.396
‡ 0.436
†8 58
3.3% 9.1%
Sys em De elopmen 16 8.2% 0.034
‡ 0.001
†3 179 91.8% 0.422
‡ 0.440
†9 195
17.8% 29.5%
So wa e Se ices 43 22% 0.011
‡ 0.000
†4 152 78% 0.221
‡ 0.279
†10 195
47.8% 25.1%
Specialized In o ma ion
Media 9 5.4% 0.000
‡ 0.000
†5 157 94.6% 0.357
‡ 0.305
†11 166
10% 25.9%
Media Ad e ising Se ices 7 17% 0.074
‡ 0.065
†6 34 83% 0.478
‡ 0.409
†12 41
7.8% 5.6%
#To al 90 100% 606 100% 696
‡ = 0.05, †1 = 0.041, †2 = 0.160, †3 = −3.72, †4 = −4.13, †5 = −9.34, †6 = −0.22, †7 = −0.04, †8 = 0.16, †9
= 0.15, †10 = 0.58, †11 = 0.51, and †12 = −0.22.
3.2.4. Se ing a Benchma k
The g een ield me hod seeks diffe en ial da a be ween each digi al sec o and he
benchma k, as shown in Figu e 1, and hen analyzes he ime se ies o he SaaS sec o s o
which he companies belong as well as he accompanying alue ans e . PBR ep esen s
he alue o an in angible asse alue, and ⊿PBR ep esen s he diffe ence be ween he
benchma k and s andalone alue o he in angible asse . The geome ic mean is used o
he PBR popula ion.
Y ep e-
sen s quali a i e changes in capi al in es men . Addi ionally, he co ela ion be ween he
di e ences in “G ow h in alue-added a e pe capi al” and “G ow h in alue added pe
employee” is in es iga ed. By compa ing hese di e ences, we de e mine he expec ed a e
o e u n on labo in es men pe uni and he expec ed a e o e u n on capi al in es men
pe uni .
4.4. Capi al and Labo In es men and P oduc i i y In es iga ion
The six digi al sec o s a e classi ied in o h ee pa e ns, as shown in Table 3. As
shown in Figu e 3, ou esul s indica e ha some sec o s a e inconsis en wi h he esul s o
Assessmen 1. When compa ing he alue ans e pe cen ages p esen ed in Table 2wi h
he coe icien s o X om 2013 o 2020 in Table 3, only he BPO se ices and specialized
in o ma ion media sec o s a e ound o be inconsis en . Fo he emaining ou sec o s, he
ends in he alue ans e a e a e consis en wi h he a e o e u n on labo in es men .
Adm. Sci. 2024,14, 71 8 o 12
Adm. Sci. 2024, 14, x FOR PEER REVIEW 9 o 13
Figu e 3. Six digi al sec o s’ linea eg ession equa ions di ided in o h ee ca ego ies.
Figu e 3. Six digi al sec o s’ linea eg ession equa ions di ided in o h ee ca ego ies.
