Ch is mann, Anne-Sophie; G a -D asch, Vale ie; Schä e , Rica da
A icle — Published Ve sion
Sma U ban Ag icul u e
Business & In o ma ion Sys ems Enginee ing
P o ided in Coope a ion wi h:
Sp inge Na u e
Sugges ed Ci a ion: Ch is mann, Anne-Sophie; G a -D asch, Vale ie; Schä e , Rica da (2024) : Sma
U ban Ag icul u e, Business & In o ma ion Sys ems Enginee ing, ISSN 1867-0202, Sp inge
Fachmedien Wiesbaden GmbH, Wiesbaden, Vol. 67, Iss. 2, pp. 247-264,
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RESEARCH PAPER
Sma U ban Ag icul u e
A S udy o Digi al Oppo uni ies o Feed Ci y Dwelle s
Anne-Sophie Ch is mann •Vale ie G a -D asch •Rica da Scha
¨ e
Recei ed: 3 July 2022 / Accep ed: 5 Decembe 2023 / Published online: 30 July 2024
The Au ho (s) 2024
Abs ac Gi en ci ies’ ising en i onmen al p oblems and
inc easing ood insecu i y, inno a i e o ganiza ional
endea o s such as u ban ag icul u e p esen a chance o
addi ional ecosys em se ices and ood p oduc ion. How-
e e , u ban spaces a e hos ile as hey jeopa dize he
a ailabili y o ai , wa e , o soil. While digi al inno a ions
enable he managemen o sca ce esou ces in adi ional
ag icul u al con ex s, li le is known abou hei applica-
bili y in u ban ag icul u e endea o s. This s udy p oposes a
mul i-laye axonomy ocusing on digi al echnologies,
da a, and di e en app oaches in u ban ag icul u e, as well
as 20 o ganiza ional eadiness ac o s de i ed wi h aca-
demics and p ac i ione s om he sma u ban ag icul u e
domain. Combining bo h pe spec i es, he s udy sheds ligh
on he na u e o sma u ban ag icul u e and ways o
le e age i s economic, ecological, and social alue.
Keywo ds Digi al oppo uni ies Sma u ban
ag icul u e Taxonomy Readiness
1 In oduc ion
Ci ies a e he main con ibu o o global ene gy demand
and ca bon emissions (Wo ld Economic Fo um 2020),
making hem a key le e o add essing he clima e c isis
(Co be and Mellouli 2017; Gimpel e al. 2020). The ising
concen a ion o people in ci ies b ings up he ques ion o
how hese popula ions can bes be p o ided wi h ood –
especially in imes o unce ain global e en s such as
pandemics o wa s. Al hough wo isome, hese challenges
cons i u e a ‘‘window o oppo uni y’’ o inno a i e
o ganiza ions. Resea che s and p ac i ione s a e inc eas-
ingly poin ing o ‘‘u ban ag icul u e’’ – a p omising com-
plemen o adi ional u al ag icul u e ha in ol es
g owing c ops and aising li es ock in ci ies (Ca olan
2020; Langemeye e al. 2021). Howe e , signi ican hin-
d ances exace ba e ealizing he po en ial bene i s o u ban
ag icul u e: Fi s , u ban spaces – cha ac e ized by sealed
su aces and hea s ess – a e a hos ile habi a o many
species, c ea ing challenging en i onmen al condi ions
leading o a high ene gy and ma e ial usage (Gimpel e al.
2021;Lu
¨ ge and Bucke idge 2020). Second, u ban ag i-
cul u e o en ails o compe e economically due o high
in es men cos s in p ime ci y land and equi ed wo k o ce
(Chang and Mo el 2018; Azun e e al. 2019) o go e n-
men s es ic ing land use o e y pa icula a eas (Diehl
e al. 2020). When u ning owa d adi ional u al ag i-
cul u e, digi al inno a ion – i.e., ‘‘ he c ea ion o (and
consequen change in) ma ke o e ings, business p o-
cesses, o models ha esul om he use o digi al ech-
nology’’ (Nambisan e al. 2017, p. 224) – has p o en o be
Accep ed a e h ee e isions by Jens Dibbe n.
A.-S. Ch is mann (&)V. G a -D asch R. Scha
¨ e
FIM Resea ch Cen e o In o ma ion Managemen , Augsbu g,
Ge many
e-mail: [email p o ec ed]
V. G a -D asch
e-mail: [email p o ec ed]
R. Scha
¨ e
e-mail: [email p o ec ed]
A.-S. Ch is mann V. G a -D asch
Uni e si y o Hohenheim, S u ga , Ge many
A.-S. Ch is mann V. G a -D asch
B anch Business & In o ma ion Sys ems Enginee ing o he
F aunho e FIT, Augsbu g, Ge many
R. Scha
¨ e
Uni e si y o Augsbu g, Augsbu g, Ge many
123
Bus In Sys Eng 67(2):247–264 (2025)
h ps://doi.o g/10.1007/s12599-024-00863-w
a signi ican alue le e o d i ing economic and esou ce
e iciency (S eininge e al. 2022). In u ban ag icul u e,
howe e , how o le e age he po en ial o digi al ech-
nologies (i.e., sma u ban ag icul u e) is less clea
(O’Sulli an e al. 2019).
So a , esea che s ha e p ima ily ocused on unde -
s anding indi idual business use-cases o sma u ban
ag icul u e (SUA). The eby, hey speci ically examined
SUA’s digi al in as uc u e and i s economic and en i-
onmen al impac (O’Sulli an e al. 2019; Weidne e al.
2022), as well as he social accep ance o he digi al
echnologies used (Spech e al. 2016; B oad e al. 2021).
Howe e , many eme ging SUA endea o s ail economi-
cally because o high in es men cos s, lacking skills, a
w ong selec ion o echnologies du ing implemen a ion, o
he inabili y o ma e ialize he expec ed inancial, ecolog-
ical, and social bene i s in he long un (Langendahl 2021).
Wha o ganiza ions u gen ly need o mas e he challenges
o inno a ion de elopmen and deploymen in SUA and
be e p epa e hemsel es o launching an endea o in his
ield, is a clea unde s anding o he concep o sma u ban
ag icul u e and he associa ed digi al inno a ion p ocess.
Hence ou esea ch ques ion eads: Wha is sma u ban
ag icul u e and how can i s alue be le e aged?
To cap u e he essence o SUA ac oss de elopmen ,
implemen a ion, and scaling, we ake wo di e en pe -
spec i es. Fi s , he e is no sha ed unde s anding o which
digi al echnologies, da a, and unc ionali ies SUA cons i-
u es. In pe spec i e 1, we hus concep ualize he phe-
nomenon o SUA as a axonomy by analyzing he ele an
dimensions and cha ac e is ics o SUA. Second, o le e age
he alue o SUA, a sound unde s anding o he o ganiza-
ional equi emen s and p e equisi es o le e aging SUA –
a concep called o ganiza ional eadiness – is c i ical
(Lokuge e al. 2019). In pe spec i e 2, we hus de elop 20
o ganiza ional eadiness ac o s o SUA by conduc ing
semi-s uc u ed in e iews wi h 9 esea ch schola s and 16
sma u ban a me s. Inspi ed by esea ch combining di -
e en me hods in one wo k (Venka esh e al. 2016a), we
combine pe spec i e 1 and 2 o gain comple eness: Bo h
pe spec i es aim o deli e a mo e comple e pic u e han
one isola ed app oach by complemen ing he insigh s o
each o he . This yields so-called ‘‘me a-in e ences’’ – a key
esul o his esea ch. Me a-in e ences depic in e p e a-
ions o ou indings in an in eg a i e iew. They p o ide
he oppo uni y o look beyond he limi a ions o a single
pe spec i e and ake a s ance in concep ualizing SUA
holis ically.
Ou wo k p esen s h ee o e a ching implica ions: Fi s ,
ou axonomy o e s a common g ound o concep ualizing
he sca e ed na u e o SUA by uni ing e ms om a ious
domains and gene alizing sub ypes. Second, we deli e
insigh s on o ganiza ions’ eadiness o le e age digi al
echnologies o add ess en i onmen al, economic, and
egula o y challenges in u ban ag icul u e. Thi d, he
in eg a i e iew o ou indings om bo h pe spec i es,
ins an ia ed as me a-in e ences, p o ides an oppo uni y o
he IS discipline o cap u e he na u e o SUA along he
digi al inno a ion p ocess.
2 Theo e ical Backg ound
2.1 Sma U ban Ag icul u e
SUA has links o wo esea ch s eams: 1) u ban ag icul u e
and 2) sma a ming. Fi s , u ban ag icul u e e e s o he
ood p oduc ion, p ocessing, and ma ke ing in u ban
ecosys ems (Smi e al. 2001; De Bon e al. 2010). I can
ange om p i a e ga dens o sel -consump ion o
sophis ica ed concep s such as comme cially o ien ed,
high- ech indoo a ms, mos ly p oducing ege ables, ish,
and mea (Wood e al. 2020). As ee space o adi ional
g ound-based ag icul u al p ac ices is sca ce in mos ci ies,
some o hese sys ems a e aligned wi h g owing ood on
housing acades, oo ops, o indoo g eenhouses (Spech
e al. 2016; Do e al. 2021). Second, sma a ming e e s
o using digi al echnologies o op imize ag icul u al p o-
duc ion in e ms o e iciency, quali y, and sus ainabili y
(Ko
¨ksal and Tekine dogan 2019; Bala ou is e al. 2017).
Key echnologies implemen ed include cloud compu ing,
he In e ne o Things, o obo ics (El Bilali and Allahya i
2018). Robo ics, o example, can con ol ac o s, pe o m
plan ing and mechanical weeding, so and ha es ui s,
o eed animals au oma ically (Nai e al. 2021).
In syn hesizing he unde s anding o u ban ag icul u e
and he desc ip ion o sma a ming, we de ine SUA as he
use o mode n digi al echnologies o op imize ood p o-
duc ion in u ban ecosys ems in e ms o e iciency, quali y,
and sus ainabili y. SUA is p omising o ackling he
challenges o u ban ag icul u e, by, o example, p o iding
ools o au oma ically con ol en i onmen al pa ame e s
(e.g., empe a u e, humidi y) o ci ies (Golds ein e al.
2016; O’Sulli an e al. 2019). In addi ion, SUA enables he
c ea ion o en i ely new concep s, such as closed- ielded
e ical a ming (Maye 2019).
2.2 Digi al Inno a ions in Sma U ban Ag icul u e
To le e age SUA’s sus ainabili y po en ial, i is necessa y
o de ec , implemen , and scale associa ed inno a ions.
While SUA esea ch has no ye concep ualized hese
necessa y phases, digi al inno a ion esea ch has al eady
done so. SUA quali ies as digi al inno a ions, as i uses
digi al echnologies o ans o m ma ke o e ings o
business p ocesses in he u ban ag icul u e ealm. To
123
248 A.-S. Ch is mann e al.: Sma U ban Ag icul u e, Bus In Sys Eng 67(2):247–264 (2025)
concep ualize digi al inno a ion, Kohli and Mel ille (2019)
p opose a model wi h ou key inno a ion phases o gani-
za ions unde go when c ea ing new digi al inno a ions:
ini ia ion, de elopmen , implemen a ion, and exploi a ion.
While he ini ia ion phase desc ibes oppo uni y de ec ion,
he de elopmen phase includes c ea ing, cus omizing, and
adop ing espec i e inno a ions. In he subsequen phases,
implemen a ion inco po a es he deploymen and main e-
nance o he inno a ion, and las ly, he exploi a ion phase
ocuses on he ongoing alue c ea ion o exis ing solu ions
(Kohli and Mel ille 2019).
The key phases abo e a e mean o apply o all digi al
inno a ions (Kohli and Mel ille 2019), including SUA.
Howe e , con ex is a c i ical ac o in he exac execu ion
o hese phases, impac ing inno a ion success (Kohli and
Mel ille 2019; Nambisan e al. 2017). The ein, a deep
unde s anding o he espec i e con ex (in ou case: SUA)
and i s in luence on he digi al inno a ion p ocess is
indispensable o le e aging SUA’s alue.
2.3 A Taxonomy o Sma U ban Ag icul u e
Du ing he ou phases, high le els o knowledge ega ding
he speci ic con ex s’ possibili ies o applying digi al
echnologies a e equi ed (Kohli and Mel ille 2019). As
applica ion knowledge is highly con ex -speci ic, a close
analysis o SUA use cases is essen ial o d i e SUA
inno a ion. Unde s anding he di e en dimensions on
which SUA can di e is needed o mas e oppo uni y
de ec ion and de elopmen . To add ess his need, de el-
oping a axonomy pinpoin ing dimensions o SUA deems a
p omising app oach (Nicke son e al. 2013).
In SUA, digi al echnologies, e e ing o he combina-
ion o in o ma ion, compu ing, communica ion, and con-
nec i i y echnologies, including he ela ed ha d- and
so wa e, a e a he co e (Bha adwaj e al. 2013). Unde -
s anding SUA applica ions hus equi es h ee componen s:
1) he digi al echnology (ha d- and so wa e) i sel
(Bha adwaj e al. 2013), 2) he da a hese echnologies
wo k wi h o gene a e meaning ul insigh s (Zhang e al.
2019), and las ly 3), he speci ica ion o he con ex ,
namely he selec ed SUA app oach in which he digi al
solu ion is applied (Hong e al. 2014). In line wi h ou
unde s anding o BISE esea ch co e ing he in e ac ion
be ween in o ma ion echnology, in o ma ion, and people,
he h ee p esen ed componen s a e sui able building
blocks in he axonomy and se e as a s uc u ing ool o
he di e en dimensions (Lee 2010). Table 1summa izes
he building blocks.
2.4 O ganiza ional Readiness o SUA
Many inno a ion endea o s ail du ing he implemen a ion
and exploi a ion phases, o easons such as inancial
challenges o di icul ies wi h scaling (Roundy 2017; Ba -
is ella e al. 2021; Dese i and Rizzo 2020). Add essing
his challenge, he concep o o ganiza ional eadiness
ac o s as necessa y p e equisi es o success ully imple-
men and exploi he po en ial o an oppo uni y is
eme ging (Lokuge e al. 2019; Molla and Licke 2005). In
he con ex o SUA, he unde lying ‘‘o ganiza ion’’ can
ange om la ge-scale p o essional i ms, communi y-
based public endea o s up un il p i a e household p ojec s
(Wood e al. 2020). As he eadiness o inno a e wi h
digi al echnologies is associa ed wi h seizing business
oppo uni ies (Walczuch e al. 2007), Lokuge e al. (2019)
p opose an o ganiza ional eadiness model o digi al
inno a ion in gene al. The model iden i ied se en possible
a eas o o ganiza ional eadiness o digi al inno a ion
(namely esou ce eadiness, IT eadiness, cogni i e eadi-
ness, pa ne ship eadiness, inno a ion alance, cul u al
eadiness, and s a egic eadiness). Howe e , as eadiness
o en includes psychological and s uc u al ac o s, such as
he commi men and capabili y o change, eadiness mod-
els mus be ailo ed o accoun o he a ibu es o he
speci ic echnology o con ex (Molla and Licke 2005).
3 S udy Design
In analyzing he con ex ual implica ions o SUA on he
gene al digi al inno a ion p ocess, we apply axonomy
esea ch (pe spec i e 1) and he de elopmen o o gani-
za ional eadiness (pe spec i e 2). Inspi ed by me a-in e -
ences in mixed-me hods esea ch (Venka esh e al. 2016a),
we de i ed an in eg a i e iew o bo h pe spec i es, which
p o ides a ulle pic u e o he phenomenon unde in es-
iga ion and links bo h pe spec i es. The me a-in e ences
we e de eloped in an induc i e p ocess o combining
insigh s om all combina ions o SUA’s axonomy
dimensions and SUA’s eadiness ca ego ies o o m
b oade gene aliza ions. The eby, we aimed a iden i ying
causal mechanisms be ween bo h pe spec i es (Venka esh
e al. 2016a).
S a ing wi h pe spec i e 1, we buil he axonomy ol-
lowing Kundisch e al. (2022) who align wi h bu ex end
Nicke son e al. (2013) (see Appendix A (online) o an
o e iew o he axonomy design’s phases. The online-
appendices a e a ailable ia h p://link.sp inge .com).
A e speci ying he axonomy’s pu pose in he In oduc-
ion and Theo e ical Backg ound sec ions o his s udy
(Phase 1: Iden i y he p oblem and mo i a e), we p o-
ceeded o de ine he axonomy’s me a-cha ac e is ics,
123
A.-S. Ch is mann e al.: Sma U ban Ag icul u e, Bus In Sys Eng 67(2):247–264 (2025) 249
ending condi ions, and e alua ion goal (Phase 2: De ine
objec i es o a solu ion).
Me a-cha ac e is ic and ending condi ions: Following
Nicke son e al. (2013) ad ice, we de ined he me a-cha -
ac e is ic o ou axonomy as Cha ac e is ics o digi al
echnologies in u ban ag icul u e. To i e a i ely e alua e
whe he ou axonomy had eached quali y sa u a ion, we
chose a se o objec i e ‘ending condi ions’ (Nicke son
e al. 2013): a) no new dimensions o cha ac e is ics we e
added in he las i e a ion, b) no dimensions o cha ac e -
is ics we e me ged o spli in he las i e a ion, and c) e e y
dimension is unique and no epea ed, d) a leas one objec
is classi ied unde e e y cha ac e is ic o e e y dimension.
In addi ion, we applied he subjec i e ending condi ions
p oposed by Nicke son e al. (2013), which equi e a ax-
onomy o be concise, obus ,comp ehensi e,ex endible,
and explana o y. O e all, we conduc ed six i e a ions
(Phase 3: Design and De elopmen ). I e a ions 1, 2, and 3
ollowed a concep ual- o-empi ical app oach (deduc i e
easoning), and i e a ions 4, 5, and 6 ook an empi ical- o-
concep ual app oach (induc i e easoning) (Kundisch e al.
2022). Table 2de ails he six i e a ions.
I e a ions 1, 2, and 3 (concep ual- o-empi ical): Du ing
i e a ion 1, we conduc ed a sys ema ic li e a u e e iew
(Boell and Cecez-Kecmano ic 2015) o English-language
esea ch pape s wi h he da abase Web o Science co e ing
b oad e minology in he SUA ealm. Appendix B (online)
summa izes he sea ch p o ocol. Fo i le, abs ac , and ull-
ex sc eening, we speci ied he ollowing inclusion c i e-
ia: Pape s add essing (1) u ban con ex s, (2) digi al ech-
nologies, and (3) he p oduc ion phase o ag icul u e. Two
au ho s sc eened each o he esul ing pape s and ex en-
si ely discussed hei inclusion. A e bo h sc eening i e -
a ions, 53 we e conside ed. Addi ional s udies we e
iden i ied in he second i e a ion ia a sea ch o he AIS
eLib a y, yielding eigh mo e s udies. The ull lis o 61
s udies can be ound in Appendix C online. We d ew on
Wol swinkel e al. (2013) app oach o quali a i e da a
analysis and coded all s udies (see exempla y coding
scheme in Appendix D online). Speci ically, du ing open
coding, we i s highligh ed in o ma ion on SUA
Table 1 Building blocks o sma u ban ag icul u e
Building Block Desc ip ion Sou ce
Digi al echnology All so - and ha dwa e o he solu ion and i s combina ion o in o ma ion,
compu ing, communica ion, and connec i i y echnologies
(Bha adwaj e al. 2013)
Da a Types, handling, and in e ac ion o and wi h da a (Pu
¨schel e al. 2020)
App oach The ype o u ban ag icul u e applied bo h wi h espec o end p oduc s and
g owing and ha es ing me hods
(Li e al. 2020)
Table 2 I e a ions o axonomy
de elopmen and e alua ion
*c-econcep ual- o-empi ical; e-
cempi ical- o-concep ual
# App oach* Basis # o Changes in las
i e a ion
Taxonomy
1 c-e WoS li e a u e 8 dimensions,
27 cha ac e is ics
8 dimensions,
27 cha ac e is ics
2 c-e AIS eLib a y li e a u e 4 dimensions,
15 cha ac e is ics
11 dimensions,
36 cha ac e is ics
3 c-e In e iews wi h 9 esea che s 2 dimensions,
4 cha ac e is ics
10 dimensions,
33 cha ac e is ics
4 e-c 10 eal-li e examples 1 dimension,
9 cha ac e is ics
9 dimensions,
25 cha ac e is ics
5 e-c 32 eal-li e examples No changes 9 dimensions,
25 cha ac e is ics
6 e-c In e iews wi h 16 p ac i ione s Renaming o 1 dimension 9 dimensions,
25 cha ac e is ics
123
250 A.-S. Ch is mann e al.: Sma U ban Ag icul u e, Bus In Sys Eng 67(2):247–264 (2025)
echnologies’ cha ac e is ics. Fo axial coding, we hen
g ouped he cha ac e is ics in o dimensions. A e wa d, we
assigned he dimensions o he h ee building blocks digi al
echnology,da a, and u ban ag icul u e app oach. Fo
selec i e coding, we educed and e ined he cha ac e is ics
in each dimension. I e a ion 3 was based on in e iews wi h
nine schola s (see Appendix E online), whose eedback we
used o e ine he dimensions and cha ac e is ics. Mo e
in o ma ion on he expe in e iews, sampling s a egies,
and coding p ocedu e is p esen ed a he end o his sec-
ion. To assess he eal-li e i o he axonomy, we c ea ed
a se o en SUA echnologies (Appendix F online). Fi e o
hese digi al echnologies we e iden i ied om he li e a-
u e, and he o he i e om eal-li e indus y p oduc s. We
buil he sample based on h ee c i e ia: 1) echnical aspec s
o digi al echnology desc ibed in de ail, 2) ad anced s age
o de elopmen , 3) di e se ypes o u ban ag icul u e (e.g.,
aquaponics, g eenhouses). In each o he i e a ions, we
classi ied he en echnologies using ou axonomy (Obe -
la
¨nde e al. 2019; Nicke son e al. 2013).
I e a ions 4, 5, and 6 (empi ical- o-concep ual): We
compa ed eal-li e examples o SUA and iden i ied simi-
la i ies and di e ences (Nicke son e al. 2013). I e a ion 4
le e aged he se o en echnologies which we ex ensi ely
discussed and compa ed in ligh o ou axonomy. Fo
i e a ion 5, we composed a mo e de ailed lis o 32 SUA
echnologies as an in o ma ion sou ce h ough a s uc u ed
web sea ch and classi ied hese echnologies using he
axonomy (Appendix G and H online). Following Amalia
e al. (2020), we conduc ed ou web sea ch as a wo-phased
app oach: 1) Websi e iden i ica ion and 2) con en analysis.
Fo websi e iden i ica ion, we sea ched he in e ne wi h
keywo ds ela ed o ‘‘sma u ban ag icul u e,’’ ‘‘sma
u ban a ming,’’ and he indi idual SUA app oaches (e.g.,
‘‘ e ical a ming’’ o ‘‘hyd oponics’’). Fo he con en
analysis, we analyzed espec i e websi es conce ning he
axonomy’s dimensions and cha ac e is ics by sc eening
o any in o ma ion indica ing an assignmen o he cha -
ac e is ics o he axonomy. This inal classi ica ion also
se ed as a ool o objec i e ending condi ions c) and d)
(Phase 4: Demons a ion). In i e a ion 6, we e alua ed he
axonomy (Phase 5: E alua ion) wi h 16 semi-s uc u ed
in e iews wi h p ac i ione s o assess he a ainmen o he
subjec i e ending condi ions (see Appendix I online o he
in e iew guideline p o ocol). We ensu ed he highes
e hical s anda ds by ha ing ou esea ch app o ed by he
Uni e si y o Hohenheim E hics Commi ee. We ec ui ed
ou ini ial pa icipan s ia pe sonal ne wo ks and con inued
ia snowball sampling. We s opped da a collec ion a e a
o al o 25 in e iews as no signi ican new opics we e
b ough up. Indi idual in e iews las ed be ween 11 and
57 min (12.6 h in agg ega e). The in e iewees esided in
se e al coun ies, including Aus ia, Ge many, Is ael, and
he Ne he lands. We eco ded and ansc ibed each
in e iew.
We coded he in e iew da a wi h e e ence o Wol -
swinkel e al. (2013), applying a pa e n-inducing ech-
nique by ga he ing quali a i e da a and clus e ing ex
segmen s in o concep s. We compa ed new ca ego ies as
hey eme ged and discussed hei connec ion. While going
back and o h be ween da a and desc ip i e codes, we
sys ema ically dis illed eadiness ac o s o SUA. The
coding p ocess was di ided in o h ee s ages: open coding,
axial coding, and selec i e coding (Wol swinkel e al.
2013; Co bin and S auss 1990). An exempla y coding
scheme ou lining he coding p ocess is pa o Appendix J
online. Due o he explo a i e na u e o ou esea ch, one
au ho s a ed by ho oughly eading he in e iew an-
sc ip s and highligh ing impo an ex passages. This way,
ex passages on ac o s ha could con ibu e o o p e en
SUA o ganiza ions om using digi al echnologies we e
highligh ed. Du ing his open coding, we elied on in o -
man e ms close o he o iginal in e iew da a. Fo axial
coding, we used a wo kshop o pa aph ase and g oup he
iden i ied ex passages, sea ching o simila i ies and di -
e ences among he codes (Co bin and S auss 1990). I
ag eemen s abou ce ain codes we e low, we e isi ed he
ansc ip s, engaged in discussions, and de eloped mu ual
unde s anding and consensual decision ules. Fo selec i e
coding he g ouping was ede ined, and ca ego ies o SUA
eadiness we e buil . By in eg a ing exis ing li e a u e
(Lokuge e al. (2019) digi al eadiness ca ego ies o
Resou ce Readiness, Cul u al Readiness, S a egic Readi-
ness, Inno a ion Valence, Cogni i e Readiness, and Pa -
ne ship Readiness), we e alua ed ou da a asking whe he
he eme ging ca ego ies help us o desc ibe and explain he
phenomena we we e obse ing. Al hough p esen ed lin-
ea ly abo e, ou analysis was dynamic and i e a i e. We
con inued coding new da a and e ining ou indings un il
we eached heo e ical sa u a ion, whe e addi ional in e -
iews did no yield any change in he eadiness ac o s.
4 Resul s o Pe spec i e 1: Taxonomy De elopmen
and E alua ion
We build on li e a u e, eal-li e examples, and in e iews
h oughou he six i e a ions o de i e ou axonomy, as
shown in Fig. 1. As ou lined in he Theo e ical Back-
g ound, we use he ailo ed building blocks o Digi al
echnology,Da a, and App oach o s uc u e SUA and
clus e dimensions and cha ac e is ics in he h ee blocks
(Pu
¨schel e al. 2020; Bha adwaj e al. 2013).
123
A.-S. Ch is mann e al.: Sma U ban Ag icul u e, Bus In Sys Eng 67(2):247–264 (2025) 251
Digi al Technology: The i s ou dimensions ela e o
aspec s o digi al echnology applied in he con ex o
u ban ag icul u e. Fi s ly, he Role o Technology indica es
whe he digi al echnology is a Suppo e o an Enable o
he u ban ag icul u e solu ion (Benbasa and Zmud 2003;
Hanel e al. 2017). Suppo e s a e digi al echnologies ha
imp o e exis ing solu ions, o example, by educing wa e
esou ces equi ed in a oo op ga den (Ha ada e al. 2018).
By con as , enable s a e cen al componen s o u ban
ag icul u e solu ions and a e equi ed o he solu ion o
wo k a all. Examples include all ypes o highly au omized
obo ic e ical a ms, whe e ope a ions would comple ely
s ill s and i he unde lying digi al echnologies (such as
obo ics algo i hms) s opped wo king.
Secondly, he dimension Func ionali y Le el indica es
he highes unc ionali y le el so ed om leas o mos
ad anced unc ionali y. The ein, he highe unc ionali y
le els such as adap a ion also include he lowe le els such
as moni o ing. The cha ac e is ic Moni o ing desc ibes all
ac i i ies (such as da a collec ion and analyses) ela ed o
measu ing and acking pa ame e s du ing ope a ion wi h-
ou ac i ely changing hem (e.g., nu ien le els, sunligh )
(Fe
´lix e al. 2018). The cha ac e is ic Recommenda ions
goes one s ep u he and in ol e ac i ely ecommending
speci ic al e na i es (e.g., adding mo e wa e ) (Galdon
e al. 2021). Adap a ion ei he e e s o a) en i onmen al
adap a ion by ac i ely con olling and changing en i on-
men al pa ame e s such as ligh , i iga ion, and nu ien s
(e.g., by modi ying he ligh in ensi y) (G a alos e al.
2019), o b) p oduc ion adap ion by pe o ming ac ions
di ec ly on he p oduc , such as sma ha es ing o weed
managemen (Ampa zidis e al. 2017; Fa hangi e al. 2020;
O o i and El-Gaya 2021). Those cha ac e is ics gi e
insigh s in o he sma ness o SUA echnologies. Acco d-
ing o Al e (2020), he sma ness o SUA echnology
depends on he echnology’s abili y o use au oma ed
capabili ies and physical, in o ma ional, echnical, and
in ellec ual esou ces o p ocess, in e p e and/o lea n om
in o ma ion. Thus, he sma ness o echnologies classi ied
in Moni o ing is compa ably lowe han hose in Recom-
menda ions which, in u n, has a lowe le el o sma ness
han Adap ion.
Thi dly, Suppo in U ban Ag icul u e Planning
in ol es using digi al echnology o ind sui able spaces o
u ban ag icul u e o simula e di e en u ban ag icul u e
se -ups (Khan and Ahmed 2017; Ghanda e al. 2021). SUA
echnologies ei he suppo his planning p ocess (i.e., YES
cha ac e is ic) o do no (i.e., No cha ac e is ic). Las ly, he
dimension In e ace desc ibes whe e humans and machines
in e ac . An in e ace can be ei he di ec ly in eg a ed wi h
he digi al echnology i sel (Solu ion-in eg a ed), h ough,
o example, displays on he echnology, o h ough
Ex e nal De ices such as wea ables (Niemo
¨lle e al.
2019).
Da a: The nex wo dimensions e e o he da a collec ed,
analyzed, and ac ed upon. This building block includes
da a collec ed di ec ly by he ope a o o ex e nals (e.g.,
wea he da a). Fi s ly, he Sou ce e e s o he loca ion o
da a collec ion (Pu
¨schel e al. 2020). Ae ial Remo e Sens-
ing desc ibes da a collec ed om d ones and sa elli es in
he ai , o example, ae ial images (Ege e e al. 2020).
Fig. 1 Taxonomy o sma u ban ag icul u e echnologies
123
252 A.-S. Ch is mann e al.: Sma U ban Ag icul u e, Bus In Sys Eng 67(2):247–264 (2025)
G ound-based Sensing e e s o all g ound-based da a
sou ces collec ing and ac ing upon in o ma ion such as
empe a u e, humidi y, o nu ien le els (Su an ha and
Su an ha 2020). Las ly, Agen - ela ed Sensing e e s o
da a collec ed di ec ly a and by human agen s (e.g.,
a me s, echnicians) wi hin he SUA solu ion, such as
ac i i ies and mo emen s acked by sma wa ches, mobile
phones, o sma glasses (Niemo
¨lle e al. 2019).
Secondly, Con en speci ies wha da a a e collec ed and
used by he digi al echnology and di e en ia es be ween
P ocess Da a,En i onmen al Da a, and hei combina ion
(P ocess and En i onmen al Da a). P ocess da a includes
da a on a machine’s ope a ions (e.g., i iga ion pe o med,
obo ic mo emen s), e e s o da a on he ac ual p oduc
(e.g., g ow h le el o heal h s a us in o ma ion O o i and
El-Gaya 2021), o includes in e ac ional da a ha in ol es
communica ion be ween humans and machines, such as
human-gene a ed inpu o he sys em (Nadal e al. 2017).
Con e sely, En i onmen al Da a comp ises in o ma ion on
he su ounding en i onmen (e.g., CO
2
le els, opog aphy)
(Nadal e al. 2017). SUA endea o s ope a ing on com-
p ehensi e da a ypes include P ocess and En i onmen al
Da a.
App oach: The hi d se o dimensions ela es o he
unde lying u ban ag icul u e app oach. Each u ban ag i-
cul u e solu ion can be classi ied acco ding o i s basic
Type. U ban ag icul u e solu ions a e ei he classi ied as
G ound Indoo (e.g., g eenhouses, hyd oponic sys ems),
G ound Open Ai (e.g., communi y ga dens), Ve ical
Indoo (e.g., indoo e ical a ms), Ve ical Open Ai (e.g.,
p oduc i e ac¸ades), o Roo op Open Ai (e.g., oo op
ga dens) (Do e al. 2021). End P oduc comp ises he
ag icul u al p oduc s p oduced in he u ban ag icul u e
solu ion. U ban ag icul u e solu ions can con ibu e o ood
secu i y by p oducing Plan s (e.g., ege ables) o Animal
and Plan p oduc s (e.g., aquaponics combining plan
cul i a ion in a hyd oponic sys em and ish a ming in an
aquacul u e sys em) (Padilla e al. 2018; Wood e al. 2020).
The dimension Nu ien Medium indica es he nu ien
medium used o p oduce he end p oduc (Padilla e al.
2018). G ow h en i onmen s include Soil, as in adi ional
ag icul u e o g eenhouses, and Wa e o Ai ,asin
hyd oponic o ae oponic sys ems (Padilla e al. 2018).
As de ailed be o e, we classi ied 32 SUA echnologies
using he inal axonomy o i s e alua e i all eal-li e
examples a e classi iable h ough ou axonomy, and sec-
ond o es i e e y cha ac e is ic is add essable h ough a
leas one eal-li e example (objec i e ending condi ions).
Appendix H (online) lis s he 32 echnologies selec ed and
hei assignmen o he di e en cha ac e is ics. Figu e 2
shows he classi ica ion o hese 32 echnologies wi hin he
axonomy. The numbe below he cha ac e is ics indica es
how many SUA echnologies we e assigned o he cha -
ac e is ics. The classi ica ion p o es ha a) all objec s
could be classi ied wi hin he dimensions and cha ac e is-
ics, and b) all cha ac e is ics we e ele an o a leas one
SUA echnology.
5 Resul s o Pe spec i e 2: Readiness Fac o s in Sma
U ban Ag icul u e
A e concep ualizing he phenomenon o SUA as a ax-
onomy, in e iews wi h 16 SUA p ac i ione s on equi e-
men s and p e equisi es needed o le e aging SUA laid he
g ound o he de i a ion o 5 SUA eadiness ca ego ies
ha ga he 20 SUA eadiness ac o s. As s a ed in he
sec ion S udy Design, we e e ed o Lokuge e al. (2019)
p oposed ca ego ies o digi al eadiness o ga he eme ging
SUA eadiness in o ca ego ies. Table 3p esen s ou main
indings: As shown in column one, mos o Lokuge e al.
(2019) eadiness ca ego ies, namely, esou ce eadiness,
cul u al eadiness, s a egic eadiness, and pa ne ship
eadiness we e also ele an o SUA eadiness. Fu he ,
ou da a e ealed a new eadiness ca ego y, namely egu-
la o y eadiness. Column h ee ep esen s de ailed
desc ip ions o each o he 20 eadiness ac o s named in
column wo. Fo esou ce eadiness, he eadiness ac o s
IT in as uc u e, IT expe ise, and inances a e in line wi h
(Lokuge e al. 2019), whe eas in eg a ion, and sus ain-
abili y u ned ou o be speci ically ele an o SUA
eadiness. Wi h cul u al eadiness, he exis ing ac o s
knowledge sha ing, ial-and-e o men ali y, app ecia-
ion, and un ac o a e complemen ed by cul u e o change
and ansdisciplina y mindse . Mo ing on o s a egic
eadiness, only s akeholde awa eness is ound o be el-
e an o SUA eadiness, expanded by scale and scale-up
pace.Pa ne ship eadiness complemen s he exis ing
ac o pe sonal ne wo k wi h high- ech supply a ailabili y,
ecosys em in eg a ion, and aining oppo uni ies.Regu-
la o y eadiness ca ego ies he newly ound SUA eadiness
ac o s o adhe ence and laws. Finally, column ou p o-
ides an exempla y quo e om he in e iews o
anspa ency.
6 Discussion
The e is subs an ial e idence ha digi al echnologies a e
p omising o mas e ing u ban ag icul u e, e.g., by
au omizing edious manual labo o educing esou ce
consump ion du ing p oduc ion (Langendahl 2021). How-
e e , in p ac ice, hei po en ial is no ye le e aged. The
undamen al challenge is ha many o he eme ging SUA
endea o s ail economically – may i be due o high
123
A.-S. Ch is mann e al.: Sma U ban Ag icul u e, Bus In Sys Eng 67(2):247–264 (2025) 253
in es men cos s, lacking skills and alen in wo k o ces, a
w ong selec ion o echnologies du ing implemen a ion, o
he inabili y o ma e ialize he expec ed inancial, ecolog-
ical, and social bene i s in he long un (Langendahl 2021;
Yigi canla e al. 2022). In espec o sol ing his chal-
lenge, ou con ibu ion is wo old.
Fi s , we con ibu e a axonomy ha enables bo h
esea ch and p ac ice o be e unde s and wha applica ion
possibili ies ‘‘digi al echnologies in u ban ag icul u e’’
comp ise, and wha di e ences exis be ween a ious ypes
o SUA. The ein, we make use o he alue o axonomies
which esea ch in he BISE communi y desc ibes as 1)
building he basis o concep ualizing a new phenomenon
(i.e., SUA) and 2), a necessa y s ep owa ds educing
esea ch and p ac ice’s o e load caused by he mul i ude o
di e en SUA echnologies a ailable (Kundisch e al.
2022). We build on u gen calls om digi al ans o ma ion
li e a u e ega ding he impo ance o enabling a conscious,
well-in o med adop ion o digi al echnologies in o de o
educe p ojec ailu e (Hess e al. 2016; Rie a and Iijima
2019).
Second, discussing ou esul s agains he b oade con-
ex o sma ci y and IT-enabled g een ci y li e a u e, we
in e p e ou indings as an ex ension o exis ing s udies in
he ield. On a gene al le el, exis ing esea ch ecognizes
he need o digi al echnologies in ci ies o a sus ainable
ans o ma ion (Dewi e al. 2018; Maye 2019). Howe e ,
he main ocus cu en ly mainly lies on use cases ela ed o
democ a izing go e nance, heal hca e, sus ainable housing,
mobili y, o educa ion (Yigi canla e al. 2022; Kinelski
e al. 2022). Acknowledging he impo ance o hese a eas,
we a gue o a s onge in eg a ion o sma u ban a ming
wi hin he sma ci y and IT-enabled g een ci y li e a u e
s eams, gi en he demons a ed po en ial o SUA. Com-
plemen ing p e ious s udies on ela ed ields such as sma
ci y eadiness (Yigi canla e al. 2022; Dewi e al. 2018),
he eadiness ac o s in hese s udies di ec ly add ess he
challenge o inancially ailing SUA ini ia i es by p o id-
ing a lis o ac o s o conside du ing p ojec launch.
Howe e , he con ibu ion o bo h he axonomy and he
eadiness ac o s is no isola ed, which is why we p o ide
an in eg a i e iew o he indings om bo h pe spec i es,
called ‘‘me a-in e ences’’. They a e summa ized in
Table 4. The p esen ed 15 me a-in e ences allow he dis-
closu e o in e ela ions and bounda y condi ions o SUA’s
axonomy dimensions and eadiness ca ego ies.
The complemen a y pe spec i es on SUA e eal ele-
an indings on he in e sec ion o axonomic esea ch and
eadiness esea ch, as p esen ed by he me a-in e ences. In
addi ion, in o de o explain how he wo pe spec i es
Fig. 2 Technology classi ica ion using he axonomy o sma u ban ag icul u e
123
254 A.-S. Ch is mann e al.: Sma U ban Ag icul u e, Bus In Sys Eng 67(2):247–264 (2025)
alida e ou ini ial indings and d aw conclusions on he
dis ibu ion o examples ac oss cha ac e is ics. Secondly,
we in e iewed p ac i ione s om Aus ia, Ge many,
Is ael, and he Ne he lands. While his sample co e s di -
e en egions, i does no ep esen he global SUA
indus y. I would be in e es ing o assess local di e ences
in SUA eadiness, such as policies and egula ions,
esou ces, and skills, ia a la ge sample o in e iews.
Ou s udy yields in e es ing pa hways o u u e esea ch
in he BISE communi y. Cu en ly, he p ocess o a me s’
e olu ion om u ban o sma u ban is unknown. Becom-
ing a SUA champion will no be a bina y p ocess om 0 o
1 bu will in ol e a ious s ages o de elopmen . Fo
esea ch and p ac ice o unde s and his p ocess, he
de elopmen o a ma u i y model o SUA – including he
eadiness ac o s o each s age (e.g., eadiness le els) – is
a esea ch endea o ha p omises o s eng hen concep ual
unde s anding o he opic (Linha e al. 2017).
Fu u e in es iga ions may also quan i y he speci ic gains
igge ed by digi al echnologies in SUA con ex s, o be e
explica e i s alue. Empi ical esea ch, o example, could
conduc ield s udies ha compa e he ou come o di e en
u ban ag icul u e app oaches – wi h and wi hou digi al
echnologies. Resul s may di e in e ms o en i onmen al
sus ainabili y (i.e., ene gy sa ings, e iciency o esou ces),
socie al (i.e., acili a ion o wo k e o ) and echnical gains
(i.e., c ea ion o da a sou ces, da a anspa ency, moni o ing
and p edic ion op ions) o economic p o i abili y. Simila ly,
u u e esea ch can e alua e di e ences in gains based on he
indi idual SUA dimensions and cha ac e is ics.
7 Conclusion
Mind ul o he g and sus ainabili y challenges we g apple
wi h, hey may cons i u e a ‘‘window o oppo uni y’’ o
new o ganiza ional endea o s. We p opose a mul i-laye
axonomy ha cha ac e izes digi al echnologies helping o
le e age oppo uni ies in u ban ag icul u e and sugges 20
o ganiza ional eadiness ac o s o sma u ban ag icul u e
as a s a ing aid. Ou wo k con ibu es o BISE esea ch by
p o iding a s uc u ing ool o guide schola s wo king a he
in e sec ion o BISE, sus ainabili y, and inno a i e digi al
oppo uni ies in he u ban ealm. O e all, ou s udy se s he
scene o a ho ough concep ual unde s anding o he na u e
o SUA echnologies and equi emen s o le e aging hei
alue, and, we hope, p esen s a u he s ep owa d c ea i e
ideas on how o u n c ises in o oppo uni ies.
Supplemen a y In o ma ionThe online e sion con ains
supplemen a y ma e ial a ailable a h ps://doi.o g/10.1007/s12599-
024-00863-w.
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