Shared business models enabling small farmers to afford new technologies

Sha ed Business Models
Enabling Small Fa me s o A o d
New Technologies
TESI DI LAUREA MAGISTRALE IN
MANAGEMENT ENGINEERING
INGEGNERIA GESTIONALE
Au ho : Joan Olle Co bella
S uden ID: 10992693
Ad iso : Filippo Ma ia Renga
Academic Yea : 2024-25
i
Abs ac
This hesis in es iga es how small a me s can adop new echnologies mo e
e ec i ely h ough Sha ed Business Models, a speci ic ype o Sus ainable Business
Models (SBMs). By ocusing on he Uni ied Theo y o Accep ance and Use o
Technology (UTAUT) and he Theo y o Planned Beha iou (TPB), he s udy explo es
how co e cons uc s a ec small a me s’ echnology adop ion p ocesses inside and
ou side o Sha ed Model. Addi ionally, he amewo ks a e expanded by in oducing
ac o s like P ice Value, Hedonic Mo i a ion, Habi , En i onmen al Unce ain y and
Sus ained Adop ion o adap hem o he con ex o small a me s.
A quali a i e, mul iple-case s udy app oach was chosen o cap u e he con ex -speci ic
challenges aced by small a me s, h ough in e iews ac o s such—limi ed inancial
esou ces, in as uc u e gaps, egula o y p essu es, and socio-cul u al dynamics—
a e assessed. In e iews and in-dep h case analyses e eal ha Pe o mance
Expec ancy, Social In luence, P ice Value, Facili a ing Condi ions a e consis en ly
impo an in shaping Beha iou al In en ion. E o Expec ancy exe s a mino —bu
di ec —in luence on Use Beha iou oge he wi h Beha iou al In en ion and
Facili a ing Condi ions. Sus ained Adop ion is ound o be in luenced by In en ion and
P ice Value. Technology amilia i y and Geog aphical Region eme ged as key
mode a ing ac o s in echnology adop ion decisions. Rega ding SBM, Subjec i e
No ms s ands ou as a nega i e ac o condi ioning a me s engagemen In en ions.
The s udy hen examines whe he a me s inclined o adop echnology a e necessa ily
mo e willing o engage in SBMs, inding no di ec co ela ion be ween hese wo
p e e ences. Some pa icipan s emb ace echnology bu ejec sha ed models due o
conce ns abou us o b and con iden iali y, whe eas o he s pa icipa e in sha ed
ini ia i es p ima ily due o ma ke p essu es a he han s ong echnological
en husiasm. To comple e his knowledge, a lis o d i e s and ba ie s is p o ided.
O e all, he esul s highligh ha indi idual cha ac e is ics and con ex ual ac o s—
including subsidies, en i onmen al unp edic abili y, gene a ional succession, and
cul u al a i udes—join ly de e mine bo h echnology accep ance and he easibili y o
sha ed models. These indings in o m p ac ical ecommenda ions o policymake s,
ag icul u al coope a i es, and echnology p o ide s seeking o suppo smallholde s
in main aining bo h compe i i eness and sus ainabili y in an e ol ing ag icul u al
landscape.
Key-wo ds: Sha ed Business Models, Small Fa me s, UTAUT, TPB, Sus ainabili y
iii
Abs ac in i aliano
Ques a esi esamina come i piccoli ag icol o i possano ado a e meglio le nuo e
ecnologie ami e Modelli di Business Condi isi, un ipo di Modelli di Business
Sos enibili (SBMs). Basandosi sulla Teo ia Uni ica a di Acce azione e Uso della
Tecnologia (UTAUT) e sulla Teo ia del Compo amen o Piani ica o (TPB), lo s udio
analizza come i cos u i chia e in luenzino l’adozione ecnologica den o e uo i dal
Modello Condi iso. Inol e, amplia ques i modelli in oducendo Valo e del P ezzo,
Mo i azione Edonica, Abi udine, Ince ezza Ambien ale e Adozione Sos enu a pe
ada a li ai piccoli ag icol o i.
Si è scel o un app occio quali a i o con uno s udio mul i-caso pe capi e le s ide
speci iche. A a e so in e is e, sono alu a i a o i come iso se limi a e, ca enze
in as u u ali, p essioni no ma i e e dinamiche socio-cul u ali. Le analisi i elano
che Aspe a i a di P es azione, In luenza Sociale, Valo e del P ezzo e Condizioni
Facili an i in luenzano cos an emen e l’In enzione Compo amen ale. L’Aspe a i a di
S o zo ha un e e o mino e, ma di e o, sul Compo amen o d’Uso, insieme a
In enzione e Condizioni Facili an i. L’Adozione Sos enu a dipende da In enzione e
Valo e del P ezzo. Familia i à con la ecnologia e Regione Geog a ica eme gono come
a o i mode a o i chia e. Pe quan o igua da gli SBM, le No me Sogge i e si
dis inguono come un a o e nega i o che condiziona le in enzioni di coin olgimen o
degli ag icol o i.
Lo s udio analizza poi se chi ado a ecnologia sia più p openso ai SBMs, senza o a e
una co elazione di e a. Alcuni usano ecnologia ma i iu ano i modelli condi isi pe
mancanza di iducia o ise a ezza, men e al i i pa ecipano pe p essioni di
me ca o, non pe en usiasmo ecnologico. Una lis a di a o i ainan i e os acoli
comple a l’analisi.
In sin esi, i isul a i mos ano che ca a e is iche pe sonali e a o i con es uali, come
sussidi, ince ezza ambien ale, successione gene azionale e cul u a, de e minano sia
l’acce azione ecnologica che la a ibili à dei modelli condi isi. Ques i isul a i
o ono sugge imen i p a ici a poli ici, coope a i e ag icole e o ni o i ecnologici pe
aiu a e i piccoli ag icol o i a es a e compe i i i e sos enibili in un se o e in
e oluzione.
Pa ole chia e: Modelli di Business Condi isi, Piccoli ag icol o i, UTAUT, TPB,
Sos enibili à.

Con en s
Abs ac ................................................................................................................................. i
Abs ac in i aliano .......................................................................................................... iii
Con en s ...............................................................................................................................
In oduc ion, Objec i es and S uc u e ......................................................................... 1
1 Resea ch Con ex ...................................................................................................... 3
1.1. Ag icul u al Sec o Con ex ......................................................................... 3
1.2. Sus ainable Business Models ....................................................................... 5
2 Li e a u e Re iew ..................................................................................................... 9
2.1. Sus ainable Business Models in Ag icul u e ............................................. 9
2.2. Theo e ical F amewo ks o Technology Adop ion & Use Beha iou 15
2.3. Desc ip ion o Which Pa s o he Theo ies A e Al eady Resea ched in
he Ag icul u al Sec o ................................................................................................ 23
3 Me hodology ........................................................................................................... 31
3.1. Resea ch App oach ..................................................................................... 31
3.2. F amewo ks U ilized .................................................................................. 32
3.3. Sample Desc ip ion ..................................................................................... 32
3.4. E hical Conside a ions ................................................................................ 33
3.5. Da a Collec ion ............................................................................................ 33
3.6. Da a P esen a ion ........................................................................................ 34
3.7. Da a Analysis ............................................................................................... 36
4 F amewo ks ............................................................................................................. 41
4.1. Reasons o using UTAUT and TPB ......................................................... 41
4.2. Cus omizing he UTAUT Model o Small Fa me s' Technology
Adop ion ....................................................................................................................... 42
4.3. UTAUT and TPB as Analy ical Tools ....................................................... 45
5 Da a P esen a ion ................................................................................................... 49
5.1. Gab i .............................................................................................................. 50
5.2. Jo di ............................................................................................................... 53
5.3. Be a .............................................................................................................. 56
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| Con en s
5.4. Sancho ........................................................................................................... 59
5.5. Joan ................................................................................................................ 62
6 Resul s and Discussion ......................................................................................... 67
6.1. Wi hin-Case Analysis ................................................................................. 67
6.2. C oss-Case Analysis .................................................................................... 90
6.3. Resul s Summa y ....................................................................................... 101
6.4. Discussion ................................................................................................... 103
7 Conclusions and Recommenda ions ................................................................ 109
Bibliog aphy ................................................................................................................... 111
A Appendix: In e iews empla e ......................................................................... 119
Lis o Figu es ................................................................................................................. 127
Lis o Tables .................................................................................................................. 129
Acknowledgmen s ......................................................................................................... 131
1
In oduc ion, Objec i es and S uc u e
Small-scale a ming is inc easingly squeezed by economic p essu es, en i onmen al
unce ain ies, and echnological ad ancemen s ha o en seem mo e sui ed o la ge
o ganiza ions. Despi e hei i al ole in u al de elopmen , biodi e si y, and local
ma ke s, small a me s ace limi ed access o inancial capi al, mode n machine y, and
digi al ools. As a esul , hey equen ly isk losing compe i i eness o e en exi ing
he ag icul u al sec o al oge he .
In esponse o hese challenges, his hesis examines how sha ed business models—a
subse o sus ainable business models (SBMs)—can enable small a me s o
collec i ely a o d new echnologies ha migh o he wise emain ou o each.
Speci ically, he p ojec ocuses on wo amewo ks o analysing echnology adop ion
and collec i e beha iou s: he Uni ied Theo y o Accep ance and Use o Technology
(UTAUT) and he Theo y o Planned Beha iou (TPB). These models o e
complemen a y pe spec i es on indi idual decision-making (UTAUT) and g oup-
ela ed ac o s (TPB), cap u ing how di e en ac o s in luence small a me s adop
inno a ion o engage in collabo a i e a angemen s.
The key objec i es o his hesis a e:
• To iden i y he main d i e s and ba ie s ha in luence small a me s' adop ion
o new echnologies.
• To examine he challenges and enabling ac o s ha a ec small a me s'
engagemen in sha ed business models.
• By in e ac ing he p e ious wo poin s, o iden i y he d i e s and ba ie s ha
eme ge when adop ing an inno a ion h ough a sha ed model.
• To explo e whe he a me s who a e mo e inclined o adop inno a ions a e
also mo e willing o pa icipa e in sha ed business models.
To achie e hese objec i es, he hesis is s uc u ed o i s p o ide con ex ual and
heo e ical ounda ions (Chap e s 1 and 2), ollowed by me hodology and amewo k
de elopmen (Chap e s 3 and 4). I hen p esen s case s udy da a (Chap e s 5) and he
analysis oge he wi h esul s (Chap e s 6), leading o conclusions and p ac ical
ecommenda ions (Chap e 7). This s uc u e ensu es a clea p og ession om
unde s anding he p oblem o p oposing ac ionable solu ions.

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2 Li e a u e Re iew
This chap e explo es he key heo ies and esea ch ha explain how a me s adop
new echnologies and how hese decisions connec o sus ainable business models
in ag icul u e. Unde s anding he ac o s ha in luence echnology adop ion and he
expec ed beha iou in on o a new business model is essen ial. This hesis is ocusing
on sha ed models, which a e in e es ing o imp o e accessibili y and e iciency in
a ming.
The discussion begins wi h an examina ion o sus ainable business models in
ag icul u e and hei po en ial o add ess inancial and ope a ional challenges aced
by small a me s, wi h a special ocus on he sha ed models. The ocus hen shi s o
heo e ical amewo ks ha p o ide insigh in o echnology adop ion and beha iou ,
pa icula ly UTAUT and TPB, which highligh he ole o di e en ac o s in decision-
making.
Building on hese ounda ions, he ela ionship be ween echnology adop ion
heo ies and sus ainable business s a egies is explo ed, emphasizing how sha ed
models i wi hin exis ing esea ch on inno a ion di usion. Finally, p e ious s udies
in he ag icul u al sec o a e e iewed o assess wha has al eady been in es iga ed
and whe e gaps emain. This app oach p o ides a s ong heo e ical basis o
analysing how sha ed business models could suppo small a me s in adop ing new
echnologies.
2.1. Sus ainable Business Models in Ag icul u e
Sus ainable business models (SBMs) in ag icul u e aim o balance economic iabili y
wi h en i onmen al s ewa dship and social esponsibili y. As ag icul u e is bo h a
signi ican economic sec o and a key con ibu o o global sus ainabili y challenges,
adop ing sus ainable business models has become impe a i e. These models seek o
in eg a e esou ce e iciency, esilience o clima e change, and equi able alue
dis ibu ion while ensu ing long- e m p oduc i i y and ood secu i y (Sengup a e al.
2024; Sado ska, Fe nq is , and Ba h 2023).
The ans o ma ion o business models in he ag icul u al sec o is being d i en by
mul iple ac o s, including egula o y p essu es, e ol ing consume p e e ences, and
ad ances in echnology. While la ge ag ibusinesses a e a he o e on o adop ing
sus ainable p ac ices, small and medium-sized a ms ace g ea e cons ain s in
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in eg a ing hese models due o limi ed access o capi al, echnology, and knowledge
ne wo ks (Donne and De V ies 2023). This dispa i y is gene a ing g ea e di e ences
be ween la ge a ms, which can mo e easily in es in sus ainabili y-d i en
inno a ions, and small-scale a me s, who s uggle o access he necessa y esou ces
and in as uc u e. As a esul , unde s anding he ba ie s ha small a me s ace in
adop ing sus ainable business models and accessing echnology is essen ial o
ensu ing a mo e inclusi e and equi able ansi ion owa ds sus ainabili y in
ag icul u e.
Ag icul u e has wi nessed he eme gence o se e al sus ainable business models, each
a ge ing di e en aspec s o sus ainabili y and e iciency. These models include:
• Ci cula Economy Models: These emphasize was e educ ion and esou ce
e iciency by epu posing ag icul u al by-p oduc s, p omo ing soil
egene a ion, and adop ing closed-loop p oduc ion sys ems (Donne and De
V ies 2023). Techniques such as compos ing, bioene gy p oduc ion, and
egene a i e ag icul u e all unde his ca ego y.
• T aceabili y-Based Models: By le e aging digi al ools such as blockchain and
IoT, aceabili y models enhance anspa ency in ood p oduc ion, ensu ing
compliance wi h sus ainabili y s anda ds and consume demand o e hically
p oduced goods (Sengup a e al. 2024).
• F ugal Inno a ion Models: These models ocus on cos -e ec i e, simpli ied
echnologies ailo ed o small-scale a me s who canno a o d expensi e
machine y o digi al solu ions. Low-cos i iga ion sys ems o bio- e ilize s
exempli y ugal inno a ion in ag icul u e (Sado ska, Fe nq is , and Ba h
2023).
• Design o Du abili y, Reusabili y, and Recyclabili y: Some sus ainable
business models ocus on ex ending he li espan o ag icul u al ools,
machine y, and packaging ma e ials. This p inciple ensu es lowe ma e ial
consump ion and minimizes was e gene a ion.
• Closed-Loop Supply Chains: This model ex ends beyond ci cula economy
p inciples by ensu ing ha e e y componen o ag icul u al p oduc ion, om
inpu s o ou pu s, emains wi hin a con olled cycle, educing was e and
maximizing e iciency. (Gholian-Jouyba i e al. 2023)
• Co-C ea ion Models: These models in ol e collabo a ion be ween a me s,
esea che s, and consume s o de elop inno a i e ag icul u al solu ions.
Fa me s pa icipa e in he design and implemen a ion o new p ac ices,
ensu ing ha sus ainabili y inno a ions align wi h eal-wo ld needs.
• Sha ed Business Models: Sha ed economy app oaches in ag icul u e in ol e
collabo a i e owne ship o esou ces such as machine y, s o age acili ies, and
p ocessing uni s, allowing small a me s o access essen ial echnology wi hou
2| Li e a u e Re iew
11
signi ican inancial bu dens. This model will be u he explo ed in he nex
sec ion.
2.1.1. Sha ed models (Sha ing economy)
Ka el Alloh de ines he sha ing economy as a business model ha uses in o ma ion
echnology and ma ke ing o acili a e he exchange o goods and se ices be ween
indi iduals (Wi z e al. 2019; Mody e al. 2023). These models a e ypically media ed
by digi al pla o ms o communi y-based ne wo ks, enabling pa icipan s o
op imize esou ce u iliza ion while educing cos s and en i onmen al impac
(Guyade and Piscicelli 2019). The sha ing economy os e s a cul u e o access o e
owne ship, encou aging mo e e icien and sus ainable use o esou ces (Pou i and
Hil y 2018; Hong and Yoo 2020). I s sus ainabili y includes en i onmen al, social, and
economic aspec s—wi h en i onmen al bene i s linked o highe esou ce e iciency
and ene gy sa ings (Laukkanen and Tu a 2020), social bene i s ocusing on well-being
and inno a ion (Ma in, Upham, and Budd 2015; Lan e al. 2017), and economic
ad an ages ied o cos -e ec i eness and b oade pa icipa ion (Cu is and Mon 2020;
Colapin o e al. 2020; Cu is and Lehne 2019).
While he sha ing economy has p ima ily eme ged in u ban se ings, i s applica ion in
ag icul u e is expanding, o e ing small a me s new oppo uni ies o collabo a e, pool
esou ces, and collec i ely access c i ical asse s such as machine y, land, s o age
acili ies, and knowledge-sha ing pla o ms.
2.1.2. The Role o Sha ed Models in Ag icul u e
Sha ed models in ag icul u e a e pa icula ly aluable o add essing esou ce
ine iciencies and enabling collec i e access o key ag icul u al asse s. Fo example,
in egions whe e land agmen a ion limi s p oduc i i y, a me s can bene i om land
pooling ini ia i es, whe e mul iple small plo s a e consolida ed o join cul i a ion.
Simila ly, coope a i e i iga ion sys ems allow mul iple a me s o sha e wa e
esou ces, educing was e and ensu ing mo e sus ainable usage (Mi alles, Den oni,
and Pascucci 2017). These app oaches lowe inancial ba ie s, imp o e ope a ional
e iciency, and enhance esilience agains economic and en i onmen al
unce ain ies (Mi alles, Den oni, and Pascucci 2017).
Beyond esou ce e iciency, sha ed models also acili a e en y in o high-cos
echnological in es men s. P ecision a ming ools, such as d ones o c op
moni o ing o au oma ed ha es ing sys ems, emain inaccessible o many
smallholde s due o high cos s. Howe e , h ough sha ed owne ship ag eemen s,
a me s can co- inance, join ly use, and main ain such echnologies, making
inno a ion adop ion mo e easible (G. A z and Nae e 2016). Success ul cases include
machine y coope a i es in Eu ope, whe e g oups o a me s collec i ely pu chase and
main ain specialized equipmen , lowe ing cos s and imp o ing machine y u iliza ion
a es.
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Howe e , he success o hese models depends on e ec i e go e nance mechanisms
and social us . While some sha ed models ope a e in o mally among us ed
communi y membe s, o he s equi e s uc u ed ag eemen s, membe ship ees, and
o malized scheduling sys ems o ensu e ai access and main enance esponsibili ies.
The le el o o mali y o en depends on he size o he coope a i e and he complexi y
o he sha ed asse .
2.1.3. Types o Sha ed Models in Ag icul u e
While a ious sha ed models exis , his hesis will ocus equally on machine y sha ing
and ag icul u al coope a i es, as bo h ep esen s uc u ed and impac ul s a egies
o helping small a me s o e come ba ie s. Machine y sha ing is pa icula ly
ele an due o i s ole in educing cos s and inc easing access o echnology, while
coope a i es a e widely used and p o ide o ganiza ional and inancial suppo . O he
o ms o sha ing models will be b ie ly in oduced o con ex .
2.1.3.1. Machine y Sha ing
Machine y sha ing enables a me s o access and use ag icul u al equipmen wi hou
ull owne ship, educing in es men cos s and inc easing e iciency. This is
pa icula ly use ul o high-cos seasonal machine y, such as ha es e s, i iga ion
sys ems and p ecision plan ing ools.
The e a e h ee main ypes o machine y sha ing:
• In o mal Machine y Sha ing: In his model, owne ship emains indi idual,
meaning ha each piece o equipmen is owned by a speci ic a me a he
han a g oup o o ganiza ion. Fa me s p i a ely a ange o lend o sha e
equipmen wi h o he s in hei communi y, ypically h ough mu ual
ag eemen s based on us a he han o mal con ac s. Usage is o en
nego ia ed case by case, wi h a me s ei he o e ing machine y in exchange o
labou , sha ed main enance, o ecip ocal use o o he equipmen . This model
is common in igh -kni a ming communi ies, whe e a me s help each o he
manage seasonal demands wi hou signi ican inancial in es men (G. A z
and Nae e 2016).
• Machine y ings: Machine y ings a e s uc u ed ne wo ks whe e a me s can
en o bo ow ag icul u al equipmen om a sha ed pool ins ead o
pu chasing i indi idually. These sys ems a e managed by an o ganiza ion,
associa ion, o hi d pa y, which o e sees scheduling, main enance, and cos
dis ibu ion. In his model, he machine y is ypically owned by he ing i sel ,
a hi d pa y, o indi idual membe s who en ou hei equipmen o
empo a y use. Fa me s pay a membe ship ee o pe -use en al ee o access
he equipmen as needed, allowing hem o use specialized machine y wi hou
he inancial bu den o owne ship. This model is pa icula ly common in
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13
Ge many and he UK, whe e i allows small a me s o access specialized
machine y as needed (Alpaslan Başa ık 2015).
• Machine y Coope a i es: A e long- e m collabo a i e models whe e a me s
co-own and co-manage machine y, making join inancial con ibu ions
owa d pu chasing, main aining, and using equipmen . Machine y
coope a i es equi e membe s o in es in owne ship, meaning hey
collec i ely pu chase and manage a m machine y unde ag eed-upon e ms.
Fa me s in a coope a i e se usage schedules, cos -sha ing ules, and
main enance esponsibili ies o ensu e ai access and long- e m
sus ainabili y. This model allows o g ea e con ol o e he machine y, as
decisions a e made democ a ically by coope a i e membe s. An example is he
Coopé a i e d’U ilisa ion de Ma é iel Ag icole (CUMA) in F ance, whe e
a me s collec i ely pu chase and main ain equipmen , educing ope a ional
cos s.
2.1.3.2. Ag icul u al Coope a i es
Ag icul u al coope a i es in ol e collec i e owne ship and managemen o a ming
esou ces, allowing a me s o educe cos s, imp o e ma ke access, and sha e
p oduc ion isks. While his hesis will no ocus on coope a i es in gene al,
machine y-sha ing coope a i es ha e al eady been explained in he p e ious sec ion.
He e, i s in oduced o he ypes o ag icul u al coope a i es:
• Inpu and Pu chasing Coope a i es: Fa me s buy seeds, e ilize s, and o he
inpu s in bulk, lowe ing cos s and imp o ing supply chain s abili y. By
pu chasing collec i ely, a me s bene i om economies o scale, making
essen ial a ming inpu s mo e a o dable and accessible.
• S o age and P ocessing Coope a i es: These coope a i es p o ide sha ed pos -
ha es s o age and p ocessing acili ies, allowing a me s o educe pos -
ha es losses, imp o e p oduc quali y, and access be e ma ke
oppo uni ies. By wo king oge he , a me s can nego ia e be e p ices and
expand in o new ma ke s.
Coope a i es play a signi ican ole pa icula ly in inpu managemen and pos -
ha es p ocessing.
2.1.3.3. O he Sha ed Models
• Land Sha ing: Fa me s collabo a e on land use and managemen o op imize
e iciency and p oduc i i y. This can in ol e join cul i a ion ag eemen s,
collec i e land leasing, o coope a i e land owne ship. In some egions, land
leasing coope a i es allow smallholde s o combine agmen ed plo s o
sha ed a ming, enabling mo e e icien esou ce use and imp o ed yields.

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• Knowledge-Sha ing Ne wo ks: Digi al pla o ms and a me o ganiza ions
acili a e aining, echnical knowledge exchange, and bes -p ac ice
dissemina ion, imp o ing inno a ion and sus ainabili y in a ming.
• Communi y-Suppo ed Ag icul u e (CSA): Consume s p e-pu chase a m
p oduce, ensu ing s able income o a me s while p omo ing di ec
ela ionships be ween a me s and consume s (Mi alles, Den oni, and Pascucci
2017).
• Pee - o-Pee Ma ke places: Online pla o ms allow a me s o en equipmen ,
ade ag icul u al inpu s, o sha e labou , inc easing lexibili y and imp o ing
access o necessa y esou ces (Cu is and Mon 2020).
2.1.4. D i e s o Sha ed Models in Ag icul u e
The adop ion o sha ed models in ag icul u e usually is in luenced by:
• Cos Reduc ion and E iciency: Fa me s can educe inancial s ain by sha ing
he cos s o machine y, i iga ion sys ems, and pos -ha es p ocessing uni s.
S udies indica e ha machine y coope a i es can lowe equipmen cos s by up
o 30–35% compa ed o indi idual owne ship (Ha is and Ful on 2000).
• Access o Ad anced Technology: Sha ed in es men in high-cos ag icul u al
echnology, such as d ones, au oma ed ha es ing sys ems, and soil analysis
ools, enables small a me s o compe e wi h la ge ag ibusinesses (G. M. A z,
Colson, and Ginde 2010).
• Risk Mi iga ion: By dis ibu ing inancial and ope a ional isks ac oss mul iple
pa icipan s, sha ed models enhance a m esilience agains ma ke ola ili y
and clima e- ela ed dis up ions (De To o and Hansson 2004).
• Knowledge and Skill Sha ing: Collabo a i e a ming models acili a e pee
lea ning, allowing a me s o exchange expe ise, adop bes p ac ices, and
imp o e e iciency (Schanes and S agl 2019).
• Sus ainabili y and Ci cula Economy: Sha ed models p omo e esou ce
e iciency by educing edundan in es men s, encou aging egene a i e
ag icul u al p ac ices, and minimizing inpu was e (Cu is and Mon 2020).
2.1.5. Ba ie s o Adop ion o Sha ed Models
Despi e he po en ial bene i s, sha ed ag icul u al models ace signi ican ba ie s:
1. Coo dina ion and Go e nance Challenges: Es ablishing e ec i e decision-
making s uc u es wi hin coope a i e models can be di icul . Con lic s o e
esou ce alloca ion, scheduling, and inancial con ibu ions o en a ise(Wol ley
e al. 2011).
2| Li e a u e Re iew
15
2. T us and Social Dynamics: The success o sha ed models depends on us
among pa icipan s. Dispu es o e equipmen main enance esponsibili ies and
usage scheduling a e common in machine y-sha ing ini ia i es (G. A z and
Nae e 2016).
3. Legal and Regula o y Cons ain s: The lack o clea legal amewo ks o
sha ed owne ship ag eemen s and coope a i e managemen complica es
implemen a ion, pa icula ly in egions wi h igid ag icul u al policies (Long
and Kenkel 2007).
4. In as uc u e and Logis ics Issues: Geog aphical dispe sion o a ms can
hinde he e iciency o sha ed machine y and s o age acili ies, inc easing
anspo a ion cos s and logis ical complexi ies (Mi alles, Den oni, and Pascucci
2017).
5. Fa me s social and Cul u al Resis ance: Con incing a me s o adop sha ed
models can be challenging due o deeply ing ained adi ional p ac ices and a
p e e ence o indi idual ope a ions. This esis ance is o en oo ed in a lack o
awa eness o unde s anding o he bene i s associa ed wi h sha ed models, as
well as conce ns abou he complexi ies o collabo a ion (G. A z and Nae e
2016).
Since hese cul u al and beha iou al ac o s, along wi h o he adop ion ba ie s, play
a c ucial ole in he easibili y o sha ed models in small a me s, his hesis aims o
in es iga e hem u he . The e o e, 2.2. Theo e ical F amewo ks o Technology Adop ion
and Use Beha iou in oduces he basic concep s ha he in es iga ion will use o
p o ide a deepe unde s anding o he ac o s in luencing a me s' decision-making
and willingness o adop sha ed business models. A e analysing he d i e s and
ba ie s a me s ace in his con ex , he indings will be compa ed wi h hose om he
li e a u e e iew o complemen , con i m, o challenge hem.
2.2. Theo e ical F amewo ks o Technology Adop ion
and Use Beha iou
In oday's wo ld, whe e echnology is ad ancing apidly and new applica ions eme ge
daily, unde s anding echnology accep ance has become a c ucial a ea o in e es . Fo
decades, social science models and heo ies ha e sough o explain how and why use s
adop new echnologies.
One o he ea lies and mos ecognized heo ies is he Inno a ion Di usion Theo y
(IDT) by Roge s (1962), ollowed by he Technology Accep ance Model (TAM)
p oposed by Da is in 1989 es (Roge s, Singhal, and Quinlan 2014) and (Da is 1989).
TAM emphasizes wo key ac o s in echnology adop ion: pe cei ed use ulness and
pe cei ed ease o use. These wo elemen s emain cen al in mo e ecen heo ies and
16
2| Li e a u e Re iew
a e undamen al o unde s anding echnology accep ance. TAM is widely ega ded as
a concise and s aigh o wa d model o e alua ing use adop ion, see Figu e 2.1.
Figu e 2.1: Illus a ion o he Technology Accep ance Model (TAM). Sou ce: (Mille and
Khe a 2010)
Building upon hese concep s, a mo e comp ehensi e amewo k was in oduced in
2003: he Uni ied Theo y o Accep ance and Use o Technology (UTAUT) by
Venka esh e al. om 2003 (Venka esh e al. 2003). UTAUT ex ends TAM by
inco po a ing addi ional a iables ha in luence adop ion beha iou . Ano he widely
used model is he Theo y o Planned Beha iou (TPB), de eloped by Ajzen in 1991
(Ajzen 1991), which p o ides a b oade amewo k o p edic ing human beha iou
beyond echnology accep ance.
Bo h UTAUT and TPB a e he p ima y models conside ed in his li e a u e e iew, as
hey o e a mo e comp ehensi e pe spec i e on beha iou al dynamics compa ed o
TAM and IDT. These models ha e been selec ed o e hei p edecesso s because hey
be e align wi h he ocus o his hesis: unde s anding he accep ance o sha ed
business models ha enable small a me s o adop new echnologies. Addi ionally,
UTAUT al eady in eg a es elemen s o bo h TAM and IDT, making i a uni ied
amewo k ha encapsula es p e ious esea ch in his ield.
2.2.1. The Uni ied Theo y o Accep ance and Use o Technology
(UTAUT)
2.2.1.1. The o iginal UTAUT Model
By he ea ly 2000s, subs an ial e idence and a wide ange o heo ies had been
de eloped o explain use beha iou in ela ion o echnology adop ion. These heo ies
o igina ed om a ious disciplines, which o en limi ed hei applicabili y in speci ic
con ex s.
As a esul , he e was a g owing need o a uni ied app oach ha could in eg a e he
di e se a iables and pe spec i es om hese disciplines, c ea ing a single,
comp ehensi e heo y applicable ac oss di e en scena ios. To add ess his, a
li e a u e e iew was conduc ed o iden i y simila i ies and di e ences among exis ing
echnology accep ance models wi hin h ee main esea ch s eams: beha iou al
2| Li e a u e Re iew
17
psychology, social psychology, and in o ma ion sys em managemen
(Papagiannidis 2022).
Venka esh, who led his esea ch e o , iden i ied se e al limi a ions in he exis ing
heo ies. One key issue was he lack o empi ical es ing and di ec compa isons
be ween di e en accep ance models, which le oom o specula ion ega ding he
p edic i e powe o each heo y's cons uc s. Addi ionally, me hodological
inconsis encies and a ia ions we e de ec ed ac oss s udies. Ano he majo limi a ion
was ha mos p io esea ch assumed echnology accep ance occu ed in olun a y
con ex s, making i di icul o gene alize indings o se ings whe e echnology
adop ion migh be in luenced by ex e nal ac o s.
The empi ical compa ison o hese heo ies ul ima ely led o he de elopmen o he
Uni ied Theo y o Accep ance and Use o Technology (UTAUT). This model,
in oduced by Venka esh and his collabo a o s in hei wo k Use Accep ance o
In o ma ion Technology: Towa d a Uni ied View, p oposes ha echnology usage is
de e mined by beha iou al in en ions, which, in u n, a e shaped by ou main
cons uc s:
1. Pe o mance expec ancy
2. E o expec ancy
3. Social in luence
4. Facili a ing condi ions
The i s h ee cons uc s di ec ly in luence usage in en ion and beha iou , while he
ou h cons uc di ec ly a ec s use beha iou . Addi ionally, ac o s such as gende ,
age, expe ience, and olun a iness o use se e as mode a o s, a ec ing he s eng h
o hese ela ionships. See Figu e 2.2 and Table 2.1 o a be e unde s anding.
24
2| Li e a u e Re iew
in en ion, echnology use and mode a o s. Addi ionally, i includes ac o s ha will
be in oduced in la e chap e s, bu a e al eady examined he e o ensu e ha , when
compa ing he hesis indings wi h exis ing esea ch, all ele an in o ma ion is
consolida ed in one place. This app oach acili a es he alignmen wi h he
me hodology ou lined in 3. Me hodology. Fo his eason, he e iew also co e s p ice
alue, hedonic mo i a ion, habi , en i onmen al unce ain y, sus ained adop ion,
and o he mode a o s, p o iding a comp ehensi e unde s anding o hei ole in
ag icul u al echnology adop ion. While some o hese cons uc s, such as pe o mance
expec ancy and social in luence, a e well-es ablished p edic o s, o he s, such as
hedonic mo i a ion and habi , emain unde explo ed in he a ming con ex :
1. Pe o mance Expec ancy
Exis ing esea ch on a m echnology adop ion consis en ly highligh s pe o mance
expec ancy as a s ong p edic o o whe he a me s pe cei e a new ool o b ing
angible bene i s. Se e al sou ces, including he (Handoko Pu a e al. 2023) con i m
ha i a me s belie e i will aise e iciency o lowe labou cos s, hei willingness
o adop goes up. When dealing wi h smallholde s speci ically, demons a ing gains
such as yield inc eases, ma ke ing ad an ages, o he capaci y o compe e wi h la ge
ag ibusinesses heigh ens con idence in adop ion (T iandini e al. 2023). Ano he
example is he discussion by (G. M. A z, Colson, and Ginde 2010), whe e small
ope a o s saw new echnology mo e a ou ably i i di ec ly enabled hem o emain
compe i i e. Con e sely, some au ho s no e ha i he expec ed pe o mance
imp o emen s a e unce ain, cau ion a ises. S udies speci ically e e encing
ad anced machine y-sha ing ind ha , al hough sha ing can educe pu chase cos s,
some a me s doub whe he he esul ing pe o mance imp o emen s (like
imeliness o be e yields) will be la ge enough o o se complexi ies (Wol ley e
al. 2011). O e all, pe o mance expec ancy emains one o he mos consis en ly
documen ed mo i a o s o a m echnology accep ance. I is hus ai o conclude ha
pe o mance expec ancy is consis en ly one o he mos i al mo i a o s o
accep ance decisions, as a me s highly alue whe he a new echnology can
conc e ely boos hei p oduc ion o educe cos s.
2. E o Expec ancy
Many in es iga ions also add ess he pe cei ed ease o lea ning o implemen ing a
gi en ag icul u al echnology, commonly epo ed as a d i e o accep ance (Handoko
Pu a e al. 2023). The ypical inding is ha i a me s iew a digi al pla o m o
ad anced machine y as di icul o ope a e o main ain, hei accep ance in en ion
dec eases. Some case s udies on a me s sha ing specialized equipmen (Alpaslan
Başa ık 2015; De To o and Hansson 2004) poin ou ha compa ibili y wi h exis ing
a m ou ines signi ican ly in luences pe cei ed e o . I a me s equi e ex ensi e
aining, hey may ea dis up ions o ime in es men , becoming eluc an o adop .

2| Li e a u e Re iew
25
So a , li e a u e con i ms ha simple , mo e use - iendly echnologies acili a e
accep ance, especially whe e ex ension se ices p o ide di ec coaching.
3. Social In luence
The li e a u e on ad anced ag icul u al echnology o en highligh s he impac o
social in luence, which unc ions simila ly o local no ms o communi y app o al. On
(G. A z and Nae e 2016) no e ha pee endo semen o neighbou success s o ies
encou age adop ion. When communi y ne wo ks o coope a i e leade s ad oca e he
new echnology, a me s pe cei e i as mo e legi ima e. In Wes A ican con ex s, o
ins ance, smallholde adop ion soa ed when local champion a me s publicly
endo sed echnology o me hod changes (Pie e e, Coulibaly e al. 2021). The
consensus is ha social in luence s ongly d i es accep ance, hough i can also
hampe i i nega i e local gossip o a p e e ence o indi idualis ic a ming is
widesp ead. The e a e no con adic ion abou he e ec o social, bu some do no e ha
social in luence alone may no be su icien i he pe cei ed pe o mance ad an age
is missing.
4. Facili a ing Condi ions
A majo heme in accep ance s udies is whe he a me s ha e he esou ces (bo h
inancial and in as uc u al) and ins i u ional suppo hey need o adop . Se e al
documen s (Wol ley e al. 2011; Long and Kenkel 2007) depic ha well-de ined
con ac ual amewo ks and ex ension suppo can ease a me s’ eadiness o sha e o
adop new machine y. O he s, like (De To o and Hansson 2004), highligh ha a lack
o cla i y in scheduling o cos -spli ing dampens accep ance. Among smallholde
communi ies, i acili a ing condi ions (like aining p og ams o local main enance
se ices) a e absen , pe cei ed di icul y emains high. The li e a u es e iewed almos
unanimously con i m he ole o acili a ing condi ions as a s ong accep ance ac o
in ag icul u al echnology.
5. P ice Value
P ice alue e e s o whe he a me s pe cei e ha a echnology’s inancial e u ns
jus i y i s o e all expense. While many s udies discuss cos s angen ially, only a ew
explici ly ea “p ice alue” as a s andalone ac o . One ha does is (An wi-Boampong
e al. 2024), which analyses IoT adop ion among smallholde a me s, concluding ha
hei willingness o adop depends hea ily on whe he nea - e m economic bene i s
ou weigh he ini ial capi al ou lay. This esona es wi h b oade indings on cos -
sha ing, (Ha is and Ful on 2000) sugges ha i a me s see a a ou able a io o
bene i o o al cos , accep ance is likely o ise. Smallholde s unde igh capi al
cons ain s, as u he no ed in (Handoko Pu a e al. 2023), pay e en close a en ion
o p ice. Con e sely, i a ool is oo expensi e o i cos -spli ing becomes complica ed
(Mi alles, Den oni, and Pascucci 2017), accep ance d ops.
26
2| Li e a u e Re iew
Simila ly, (Fa idi, Ka oosi-Kalashami, and Bilali 2020) a gue ha inancial cons ain s
a e a majo ba ie o adop ion, making pe cei ed alue a c i ical de e minan .
Fa me s assess no jus di ec cos s bu whe he expec ed bene i s jus i y he
in es men , shaping hei adop ion decisions.
Though he p inciple is widely acknowledged, mos esea ch e e ences p ice
conside a ions implici ly, wi hou labelling “p ice alue” as a dis inc dimension.
Thus, while he economic a io o gains o cos s is undeniably impo an o a me s,
ew in es iga ions excep o hose like (An wi-Boampong e al. 2024) o e a di ec , in-
dep h ea men o his cons uc in he con ex o echnology up ake among small-
scale p oduce s.
6. Hedonic Mo i a ion
Re e ences o hedonic mo i a ion, meaning he enjoymen o pleasu e de i ed om
using new ag icul u al echnology, a e ela i ely spa se. Resea che s s udying
machine y adop ion o cos -sha ing models ypically s ess p agma ic (Pongsuwan
2019) ac o s a he han he “ un” o “en e ainmen ” alue o he inno a ion. In
(An wi-Boampong e al. 2024), he discussion on smallholde a me s’ echnology
up ake does ouch upon use - iendly ea u es bu ames hem as p ac ical a he han
hedonic. Some men ion o “con enience” appea s in (Handoko Pu a e al. 2023);
howe e , no indings ele a e he enjoymen dimension o a p ima y d i e o
accep ance. Consequen ly, i is assumable ha hedonic mo i a ion is no a widely
s udied o highly in luen ial ac o in a me s’ decision-making on new echnology,
especially among small ope a o s who p io i ize economic and labou -sa ing bene i s.
7. Habi
Habi usually is no ea ed as a disc e e ac o , bu he idea o whe he a new
echnology “ i s” one’s exis ing a m ou ine, a he han causing majo dis up ion,
does su ace in some s udies. Fo example, in (De To o and Hansson 2004), se e al
a me s exp essed eluc ance i ad anced machine y equi ed e- iming ce ain
ope a ions o o ced hem o adjus ield schedules d as ically. They ques ioned how
easily he inno a ion would “slo in” o daily asks wi hou in oducing con lic .
Ano he example is (T iandini e al. 2023), which implies ha when digi al ools
ha monize na u ally wi h a a me ’s es ablished p ac ices (e.g., eco d-keeping o
ield moni o ing), accep ance is smoo he because i eels like an inc emen al
imp o emen a he han a sha p change. The consis en h ead is ha i adop ing a
echnology seems o blend in o exis ing ou ines wi h minimal uphea al, a me s
pe cei e ewe isks and a e likelie o emb ace i . Con e sely, a sense ha one’s
no mal ou ine can change d as ically can block hei eadiness o adop .
Addi ionally, (Handoko Pu a e al. 2023) highligh ha long- e m adop ion is no
solely based on ini ial in en ion bu also on he o ma ion o habi ual use. This sugges s
ha echnologies seamlessly in eg a ed in o exis ing ou ines a e mo e likely o be
e ained o e ime, as a me s de elop au oma ic beha iou s in using hem. The
2| Li e a u e Re iew
27
abili y o a echnology o become an ing ained pa o daily a ming p ac ices hus
plays a c ucial ole in ensu ing i s sus ained adop ion.
So, habi is no ypically conside ed as a main cons uc in s udies bu is ex endedly
accep ed ha ha monizing a new solu ion wi h a me s’ es ablished ou ines
eme ges as a complemen a y ac o o echnology accep ance in ag icul u e.
8. En i onmen al Unce ain y
En i onmen al unce ain y has been ecognized as a key ac o in luencing small
a me s' adop ion o digi al pla o ms (Cimino e al. 2024). Rapid echnological
ad ancemen s and ma ke luc ua ions inc ease unce ain y, p omp ing a me s o
seek solu ions ha enhance adap abili y and esilience. The pe cei ed abili y o a
echnology o help na iga e such unce ain ies can signi ican ly impac adop ion
decisions. A ew e e ences (De To o and Hansson 2004) also indica e ha
unp edic able wea he o shi ing ma ke condi ions complica e echnology
scheduling and coope a i e machine y usage. These au ho s highligh how unce ain
ha es windows can de e a me s om us ing ad anced equipmen -sha ing
a angemen s i he isk o delayed access o machine y is pe cei ed as oo g ea .
Howe e , since "en i onmen al unce ain y" is no sys ema ically used as a UTAUT
cons uc , he e is limi ed eliable in o ma ion o con i m ha he way a echnology
in e ac s wi h en i onmen al unce ain y is a key ac o .
9. Beha iou al In en ion, Technology Use and Sus ained Adop ion
In UTAUT s udies, beha iou al in en ion ypically se es as he main dependen
a iable, e lec ing a me s’ eadiness o adop . Mos e e ences con i m ha a solid
in en ion (d i en by pe cei ed pe o mance ad an age, e o expec ancy, social
in luence, and acili a ing condi ions) usually culmina es in ac ual usage. In oducing
he idea o his hesis o combine echnology adop ion wi h a sha ed model, no only
he use should be conside ed. G. A z and Nae e (2016) wa n ha disag eemen s o e
scheduling o con ac e ms can dis up ongoing adop ion, implying ha sus ained
usage may equi e s able collabo a i e go e nance. Ve y ew s udies del e deeply
in o “long- e m usage” me ics; hey mainly ack ini ial accep ance. In sho , he basic
accep ance pa e n, in en ion p edic ing usage, emains alida ed, bu de ailed
explo a ion o how ha usage emains s able o e ime is less common in he small-
a m con ex .
10. Mode a o s: Technology Familia i y, Gende , Age, Expe ience,
Volun a iness o Use
Se e al demog aphic and con ex ual ac o s mode a e he adop ion o new
ag icul u al echnologies among smallholde a me s. Age and expe ience ha e been
widely s udied, wi h indings sugges ing ha while age i sel does no signi ican ly
al e adop ion beha iou s, p io expe ience wi h ag icul u al echnology does. Fo
ins ance, (Zhang e al. 2024) ound ha a me s wi h mo e yea s o echnology use
28
2| Li e a u e Re iew
we e mo e likely o pe cei e highe pe o mance bene i s and cos -e ec i eness,
leading o s onge adop ion in en ions. Gende di e ences seem o play a c ucial ole,
s udies show ha women a me s in many egions ha e lowe adop ion a es due o
socio-economic cons ain s, lack o echnical aining, and adi ional labou di isions
(Rado ić-Ma ko ić, Kabi , and Jo ičić 2020). Technology amilia i y is ano he c i ical
mode a o ; (Gab iel and Gando e 2023) obse ed ha small-scale Eu opean a me s
end o adop sequen ially, wi h ea ly exposu e o digi al ools inc easing he
likelihood o adop ing addi ional echnologies o e ime. Simila ly, (Handoko Pu a
e al. 2023) highligh ha a me s who ha e p e iously in e ac ed wi h ela ed
echnologies may pe cei e adop ion as easie and be less esis an o change, as
amilia i y educes pe cei ed e o and unce ain y. Volun a iness o use, al hough
less equen ly examined, emains ele an , as esea ch indica es ha a me s who
pe cei e echnological adop ion as a necessi y a he han a choice, due o policy
incen i es o ma ke p essu es, may exhibi di e en beha iou al pa e ns (Fadeyi,
A iyawa dana, and Aziz 2022). Despi e ecogni ion o hese mode a o s, exis ing
s udies o en ail o comp ehensi ely analyse hei combined e ec s, lea ing a gap in
he li e a u e ega ding how demog aphic, social and con ex ual ac o s collec i ely
shape smallholde a me s' echnology adop ion.
2.3.2. TPB b oken down
Focusing on he o he heo y discussed, his sec ion e iews exis ing esea ch on TPB’s
main dimensions: a i ude owa d beha iou , subjec i e no ms, and pe cei ed
beha iou al con ol, along wi h hei subcomponen s, o unde s and hei ole in he
adop ion o sha ed business models in ag icul u e. The a ionale o inco po a ing TPB
alongside UTAUT is u he explained in 4.1.Reasons o using UTAUT and TPB , wi h
some ini ial insigh s al eady p o ided in hei espec i e in oduc ions. Exis ing
esea ch sugges s ha economic bene i s, pee in luence and a sense o con ol o e
sha ed esou ces s ongly impac a me s’ pa icipa ion in such models. Howe e ,
challenges such as coo dina ion di icul ies, powe imbalances and scep icism abou
ai ness can ac as ba ie s o adop ion. By b eaking down hese elemen s, his sec ion
highligh s how TPB can help explain why some a me s emb ace collabo a i e
app oaches while o he s hesi a e.
1. A i ude owa d Beha iou (including E alua ions o Beha iou al Ou comes)
A i ude owa d he beha iou e lec s whe he a me s ha e a a ou able o
un a ou able app aisal o adop ing machine y-sha ing o a coope a i e business
model. In ag icul u e, hese a i udes a e o en shaped by how well a me s hink
such a model can help hem inc ease yields, lowe cos s, o o he wise bene i (De
To o and Hansson 2004; Wol ley e al. 2011). When a me s see angible po en ial
ou comes, hey a e mo e likely o de elop a posi i e a i ude owa d joining o
o ganizing a machine y-sha ing a angemen . Con e sely, conce ns abou scheduling
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29
con lic s, coo dina ion hassles, o une en bene i dis ibu ion can yield un a ou able
a i udes (Mi alles, Den oni, and Pascucci 2017).
Mo eo e , he e alua ion o beha iou al ou comes speci ically add esses how a me s
weigh he p os and cons o sha ing. Fo ins ance, s udies (Alpaslan Başa ık 2015; G.
A z and Nae e 2016) indica e ha i he coope a i e amewo k p o ides genuine
economic gains (e.g., be e pu chase powe o mechaniza ion) while also a oiding
ic ion wi h a m ou ines, a me s’ a i ude is mo e suppo i e. On he o he hand,
i hey an icipa e complica ed con lic - esolu ion o minimal ne gains, hey will
e alua e he beha iou as less wo hwhile. So, a i ude is usually shaped by pe cei ed
bene i s, adap abili y, and o ganiza ion challenges.
2. Subjec i e No m (No ma i e Belie s and Mo i a ion o Comply)
Subjec i e no m in he TPB co e s he pe cei ed social p essu es o ei he adop o
ejec he new beha iou . No ma i e belie s in ol e he a me ’s pe cep ion o how
ce ain impo an e e ence g oups iew he idea o machine y-sha ing o
coope a i es. Empi ical indings (Ha is and Ful on 2000; T iandini e al. 2023) show
ha isible endo semen s om in luen ial communi y membe s o ecognized
champions s ongly boos a me s’ pe cei ed legi imacy o coope a i es.
Con e sely, i local gossip discou ages collabo a i e app oaches o i olde a me s
p e e indi idual owne ship, no ma i e belie s can il nega i ely.
Mo i a ion o comply cap u es he deg ee o which a me s a e willing o abide by
hese social expec a ions (Zhang e al. 2024). When a me s us a espec ed local
agg ega o o ely on ad ice om a knowledgeable ag icul u al expe , hey a e mo e
likely o ollow ecommenda ions and pa icipa e. Meanwhile, mo e independen -
minded a me s o hose who dis us he endo se s migh dis ega d social cues. In
sho , no ma i e belie s se he pe cei ed social p essu e, and a me s’ mo i a ion o
comply de e mines whe he ha p essu e e ec i ely d i es hem o adop o no .
3. Pe cei ed Beha iou al Con ol (Con ol Belie s and Pe cei ed Powe )
Pe cei ed beha iou al con ol pe ains o a me s’ sense o whe he hey ha e he
means o implemen he beha iou . Con ol belie s e e o pe cei ed acili a o s o
ba ie s, om logis ic de ails like scheduling, a el dis ance inancial cons ain s o
in angible ac o s like pe sonal nego ia ion skills (De To o and Hansson 2004; Fadeyi,
A iyawa dana, and Aziz 2022). I a me s belie e he a angemen ’s complexi ies,
such as scheduling he equipmen use, can be managed o ha hey ha e local
ex ension suppo o dispu e esolu ion, con ol belie s inc ease.
Pe cei ed powe is he a me s’ sense o hei own abili y o au ho i y o manage hose
cons ain s (Gab iel and Gando e 2023). Fo ins ance, i a single la ge-scale a me in
he g oup dic a es usage ime while smalle -scale a me s eel o e shadowed, hose
smalle a me s pe cei e hey ha e li le powe , dampening hei willingness o
pa icipa e. On he con a y, when g oup s uc u es o o mal ag eemen s ensu e

30
2| Li e a u e Re iew
equi able esou ce access, a me s gain con idence in hei powe o shape ou comes.
This obus sense o con ol is c i ical o o ging s ong in en ion and e en inal
beha iou owa d coope a i e membe ship o machine y-sha ing.
4. Beha iou al In en ion and Beha iou
Taken oge he , a i ude (and ou come e alua ions), subjec i e no m ( ia no ma i e
belie s and mo i a ion o comply), and pe cei ed beha iou al con ol (based on
con ol belie s and pe cei ed powe ) shape a me s’ o e all in en ion o adop a sha ed
machine y solu ion o a coope a i e. Once ha in en ion is su icien ly s ong he
ac ual beha iou o engaging on a sha ed model o en ollows. Howe e , no always
is like his. Ce ain s udies no e ha unexpec ed changes in social no ms o logis ical
b eakdowns can al e a me s’ in en ions mids eam (G. A z and Nae e 2016).
Al hough TPB helps explain a i udinal and social ac o s, esea ch is s ill limi ed on
how hese concep s apply o small a ming communi ies on engaging in sha ed
models. I s uggles o explain why, e en wi h posi i e in en ions, inal engagemen
does no always happen. Ne e heless, he consis en inding is ha each TPB
componen in luences a a me ’s eadiness o adop o pa icipa e in sha ed o
coope a i e-based business models. Howe e , i emains la gely unexplo ed why,
despi e ha ing he in en ion, a me s do no always ollow h ough wi h
engagemen .
To add ess his gap, his hesis will use TPB while inco po a ing addi ional ques ions
o be e iden i y he eal d i e s and ba ie s ha in luence small a me s' engagemen
in a sha ed model.
31
3 Me hodology
Once he con ex and li e a u e ha e been p esen ed, which is essen ial o c i ically
e alua ing he indings a e he hesis in es iga ion, he me hodology ha i ollows
is now in oduced. I explains he esea ch app oach used, he amewo ks i is based
on, he selec ion sample p inciples, and e hical conside a ions. Then, i con inues
explaining he da a collec ion p ocess, how i is p esen ed o u he analysis, and
how hese p ocesses a e ca ied ou . This e y s uc u ed app oach ( ollowing
Eisenha d me hod and insigh s o Miles & Hube man) ensu es a igo ous and
scien i ic analysis. In he di e en sec ions, i will be highligh ed how he s uc u e
and i e a ions a e conduc ed o ensu e he igo ousness o he indings.
3.1. Resea ch App oach
This esea ch ollows a quali a i e case s udy app oach (Eisenha d , 1989) o explo e
how small a me s adop new echnologies and how sha ed business models in luence
his p ocess. Gi en he complexi y o he subjec , whe e echnology adop ion in e sec s
wi h sus ainable business models, his app oach allows o an in-dep h in es iga ion
o eal-wo ld examples, complemen ing and expanding exis ing heo e ical
amewo ks. Case s udies a e pa icula ly aluable in eme ging o unde explo ed
esea ch a eas as hey enable an induc i e, heo y-building p ocess (Eisenha d , 1989).
Fu he mo e, Eisenha d 's amewo k highligh s he necessi y o s uc u ing da a
collec ion and analysis sys ema ically o acili a e pa e n ecogni ion and heo e ical
de elopmen .
This app oach has been selec ed o his hesis, meaning a quali a i e s udy based on
case s udies h ough in e iews will be conduc ed. This me hod was chosen o e
o he s because i p o ides mo e de ailed esponses, helps unco e oo p oblems, and
allows o he be e iden i ica ion o key d i e s and ba ie s. In con as , a
quan i a i e app oach would equi e a la ge sample o a me s and would no yield
insigh s as pe sonal and in-dep h as hose ob ained h ough his quali a i e me hod.
This p ocess ollows a logical p og ession, explained in he nex sec ions, which is
s uc u ed a ound he UTAUT and TPB amewo ks.
32
3| Me hodology
3.2. F amewo ks U ilized
To enhance he explana o y powe o he Uni ied Theo y o Accep ance and Use o
Technology (UTAUT) in he con ex o small a me s' echnology adop ion, his
s udy inco po a es se e al modi ica ions. While UTAUT p o ides a s ong ounda ion
o analysing beha iou al in en ion and use beha iou , i does no ully cap u e
ex e nal en i onmen al in luences, inancial conside a ions, habi ual usage, o he
long- e m sus ainabili y o adop ion in ag icul u e. The e o e, his esea ch akes
ad an age o he possible adap a ions o he amewo k p esen ed in 2.2.1.2.
Imp o emen s o he UTAUT Model, while u he de ails on i s applica ion and
modi ica ions will be p o ided in chap e 4.F amewo ks. These modi ica ions ensu e
ha he amewo k be e e lec s he economic, social, and psychological dimensions
in luencing small a me s’ adop ion decisions.
In he case o TPB, i will be used wi hou modi ica ion as i is al eady a amewo k
ha cap u es he key psychological ac o s in luencing a me s' decision-making,
making i well-sui ed o analysing hei beha iou owa d sha ed business model
accep ance, also de eloped in chap e 4.F amewo ks. Howe e , as men ioned,
addi ional in insic ques ions will be posed o unde s and why he heo y ail no
p edic ing p ope ly he connec ion be ween in en ion and beha iou .
In bo h cases, he li e a u e e iew has al eady highligh ed he ad an ages o hese
amewo ks o e olde ones. UTAUT is used o echnology adop ion because i
ocuses on he ac o s in luencing adop ion decisions and is mo e powe ul, while TPB
is applied o sus ainable business models, as i be e accoun s o collec i e se ings
and p o ides a mo e gene ic app oach o e alua ing he in angible concep o a
sha ed model and in en ion. Howe e , his TPB b oade app oach, while making i
easie o a me s o exp ess hei iews, can lead o he loss o impo an de ailed
in o ma ion. To add ess his, speci ic ques ions abou sha ed business models a e
included o ill he gap al eady discussed in he li e a u e e iew.
3.3. Sample Desc ip ion
The s udy sample consis s o small a me s ope a ing in di e se ag icul u al sec o s,
including c op cul i a ion and li es ock a ming. Fa me s we e selec ed based on
speci ic c i e ia o ensu e a ep esen a i e ange o pe spec i es and expe iences:
• Fa m Size: Pa icipan s a e small/medium scale a me s managing holdings
ypically below 30 hec a es.
• Technology Adop ion S a us: The sample includes a me s who ha e adop ed,
conside ed, o ejec ed new ag icul u al echnologies.
• Geog aphical Dis ibu ion: Fa me s a e d awn om 2 di e en loca ions o
cap u e a ia ions in economic, social and en i onmen al condi ions.
3| Me hodology
33
• Business Model In ol emen : Some a me s cu en ly pa icipa e in
coope a i e o sha ed business models, while o he s ope a e independen ly.
The selec ion o only small and medium-scale a me s is due o hei being he ocus
o his s udy, as hey ace unique challenges compa ed o la ge ag ibusinesses. The
di e si y in echnology adop ion p o ides di e en pe spec i es, which a e use ul o
unde s anding how adop ion pa e ns may a y among a me s. Same o geog aphic
di e ences, is also c ucial o assessing he in luence o ex e nal ac o s, which is why
wo loca ions wi h dis inc cha ac e is ics we e chosen. Las ly, compa ing he
pe spec i es o a me s in ol ed in sus ainable business models wi h hose who ha e
no pa icipa ed in hem can p o ide aluable insigh s on he di e en expe iences.
Se e al addi ional ac o s could ha e been conside ed in he selec ion p ocess bu we e
no included due o easibili y cons ain s like quan i ying he income le el, inancial
s a us o gene a ional di e ences. This las one because no a me s amilies ha e been
ound o in e iew. Also, he need o main ain a manageable scope made he p ojec
s ick wi h hese 4 c i e ia and a o al quan i y o 5 a me s selec ed.
3.4. E hical Conside a ions
All pa icipan s p o ide in o med consen be o e in e iews, ensu ing olun a y
pa icipa ion and con iden iali y.
3.5. Da a Collec ion
Eisenha d (1989) ad oca es o he use o mul iple da a collec ion me hods, including
in e iews, obse a ions, and a chi al sou ces, o ensu e iangula ion. While his
s udy p ima ily elies on semi-s uc u ed in e iews, his app oach is u he
ein o ced by an ex ensi e li e a u e e iew and he open-ended o ma in e iews o
p o ide deepe insigh s in o a me s’ decision-making p ocesses (Miles & Hube man,
1994). This ensu es a comp ehensi e empi ical g ounding o heo y-building.
Da a collec ion wi h his o ma is conduc ed o achie e lexibili y in explo ing a me s'
expe iences, conce ns, mo i a ions, ba ie s, and d i e s. Ga he ing in o ma ion while
gi ing a me s he eedom o alk abou wha e e hey eel is necessa y and ensu es
ha we do no limi ou sel es o ou own assump ions o ely solely on p e ious
s udies. This app oach is ollowed o ob ain mo e au hen ic and unbiased
in o ma ion, which is impo an o his speci ic esea ch ha aims o iden i y he inal
d i e s and ba ie s in he adop ion echnology and engage in SBM p ocess. The
in e iews ollow a s uc u ed app oach consis ing o h ee main sec ions:
1. Gene al In o ma ion: Unde s anding he a me 's backg ound, ope a ional
scale, and challenges. This ques ions di ec ly ela es o he UTAUT mode a o s.

41
4 F amewo ks
Al eady in oduced he me hodology, we a e ba ely p epa ed o s a applying i . Jus
be o e i , he e’s he need o his chap e jus i ying he use o his speci ic amewo k
and explaining he adap a ions and speci ic use ha hey will ha e.
4.1. Reasons o using UTAUT and TPB
In s udying echnology adop ion among small a me s, exis ing amewo ks such as
UTAUT and TPB p o ide aluable bu incomple e pe spec i es. UTAUT is well-
sui ed o analyse indi idual decisions o adop new echnologies, ocusing on ac o s
like pe o mance expec ancy, e o expec ancy, social in luence o acili a ing
condi ions. Howe e , i does no accoun o collec i e decision-making, he ole o
us and sha ed beha iou al no ms o any ac o s ela ed o g oup pa icipa ion
which a e essen ial in sha ed business models. By con as , TPB p o ides a s onge
ounda ion o unde s anding hese dynamics, as i explici ly inco po a es subjec i e
no ms, a i udes, and pe cei ed beha iou al con ol— ac o s ha in luence adop ion
in g oup se ings. Addi ionally, i is amed in a mo e gene ic way, making i easie
o a me s o espond, especially when discussing less amilia and mo e in angible
concep s. Howe e , i does no e alua e indi idual beha iou as e ec i ely as
UTAUT.
Gi en hese complemen a y s eng hs and limi a ions, his esea ch applies bo h
models: UTAUT is used o assess echnology accep ance, while TPB is applied o
explo e a me s' willingness o adop a sha ed business model. By in eg a ing hese
pe spec i es, he s udy le e ages he s eng hs o each amewo k while mi iga ing
hei limi a ions. This hyb id use o he amewo ks enables a mo e comp ehensi e
unde s anding o adop ion p ocesses.
As he s udy adop s an open-ended app oach, UTAUT and TPB se e as analy ical
ools o guide da a collec ion and analysis a he han as igid models wi h p ede ined
ela ionships. This app oach allows indings o eme ge na u ally, ensu ing ha he
s udy iden i ies he key d i e s and ba ie s wi hou p e-imposing assump ions
abou how he ac o s in e ac .
The in es iga ion hen culmina es in a join analysis o he insigh s gained om
applying hese wo amewo ks, assessing whe he indings align wi h exis ing
li e a u e o in oduce new pe spec i es. By compa ing in e iew esponses o
42
4| F amewo ks
heo e ical expec a ions, he s udy seeks o alida e, e ine, o expand he
unde s anding o echnology adop ion h ough sha ed business models among
small a me s.
4.2. Cus omizing he UTAUT Model o Small Fa me s'
Technology Adop ion
While he s udy aims o a oid in luencing a me s' esponses, adap ing he UTAUT
model emains necessa y. This ensu es he inclusion o key adop ion ac o s ele an
o small a me s' ag icul u al con ex and he speci ic ocus o his hesis, which he
o iginal model does no ully add ess (Ronaghi and Fo ouha a 2020; Michels, Bonke,
and Mussho 2020).
To add ess hese limi a ions, his s udy in eg a es addi ional mechanisms ha
enhance UTAUT’s abili y o analyse echnology adop ion in hese se ings. These
modi ica ions include:
1. The addi ion o an exogenous mechanism (En i onmen al Unce ain y) o
accoun o ex e nal condi ions a ec ing adop ion.
2. The expansion o in e nal beha iou al p edic o s (Habi , P ice Value, and
Hedonic Mo i a ion) o cap u e long- e m adop ion pa e ns.
3. The in oduc ion o a mode a ing ac o (Technology Familia i y) o e lec
p io exposu e o simila echnologies.
4. The inco po a ion o a long- e m adop ion ou come (Sus ained Adop ion) o
assess e en ion beyond ini ial adop ion.
All hese modi ica ion me hods a e suppo ed by he scien i ic communi y and ha e
al eady been p esen ed in 2.2.1.2. Imp o emen s o he UTAUT Model. These
modi ica ions a e based on exis ing li e a u e and heo e ical conside a ions, ensu ing
ha key adop ion de e minan s a e inco po a ed in o he s udy wi hou assuming
p ede ined in e ac ions. Thei ole and signi icance will be de e mined h ough
empi ical analysis. Complemen ing he 2.3. Desc ip ion o Which Pa s o he Theo ies A e
Al eady Resea ched in he Ag icul u al Sec o in ollowing sec ions some mo e de ails a e
p o ided on how his ac o migh in luence.
4.2.1. Inco po a ing an Ex e nal P edic o : En i onmen al Unce ain y
(EU)
Ex e nal en i onmen al ac o s a e pa icula ly ele an in he ag icul u al sec o .
Fa me s ope a e in highly unce ain condi ions, whe e clima ic a iabili y, ma ke
luc ua ions, and policy changes can signi ican ly impac hei decision-making
p ocess. To add ess his, he model in oduces En i onmen al Unce ain y (EU) as a
new exogenous a iable.
4| F amewo ks
43
En i onmen al Unce ain y is de ined as he ex en o which ex e nal changes c ea e
ins abili y in decision-making (Cimino e al. 2024). Any o he en i onmen al
unce ain ies men ioned can shape hei willingness o adop echnology by
in luencing how hey assess he isks, bene i s, and usabili y o echnological
solu ions (Shi e al. 2022). EU migh no di ec ly in luence Beha iou al In en ion (BI)
bu a he shape he key de e minan s ha d i e BI.
Exis ing esea ch sugges s ha EU can in luence key adop ion de e minan s such as
Pe o mance Expec ancy (PE), E o Expec ancy (EE), Social In luence (SI), and
Facili a ing Condi ions (FC). In uns able condi ions, a me s may iew echnology as
a means o mi iga e isks, which can inc ease he impo ance o Pe o mance
Expec ancy (PE) i he echnology is pe cei ed as a eliable solu ion o ex e nal
challenges. Howe e , heigh ened unce ain y may nega i ely impac E o
Expec ancy (EE) i a me s pe cei e adop ion as an addi ional bu den in an al eady
ola ile en i onmen . Simila ly, Social In luence (SI) may become mo e in luen ial,
as a me s acing unce ain y a e mo e likely o ely on pee ecommenda ions and
communi y no ms when making adop ion decisions. Addi ionally, Facili a ing
Condi ions (FC) may gain impo ance, as a me s expe iencing unce ain y migh
depend mo e on ex e nal suppo s uc u es, such as inancial aid o coope a i e
ne wo ks, o adop and sus ain he echnology.
Ano he cons uc ha will be p esen ed in he nex sec ion 4.2.2.Expanding In e nal
P edic o s: Habi , P ice Value, and Hedonic Mo i a ion, is P ice Value (PV), which may
play a c ucial ole in a me s' decisions, as inancial ins abili y can heigh en
sensi i i y o up on cos s and long- e m economic bene i s, making a o dabili y a
key de e minan .
This a iable is classi ied as an exogenous mechanism because i o igina es ou side he
a me ’s decision-making p ocess. Unlike in e nal cogni i e ac o s, En i onmen al
Unce ain y is no shaped by indi idual expe iences o psychological p edisposi ions
bu a he by objec i e ex e nal condi ions ha in luence how echnology is pe cei ed
as a ool o isk managemen .
4.2.2. Expanding In e nal P edic o s: Habi , P ice Value, and Hedonic
Mo i a ion
While En i onmen al Unce ain y ep esen s an ex e nal o ce shaping adop ion
decisions, he model also is expanded wi h in e nal mechanisms by inco po a ing
h ee addi ional cons uc s om UTAUT 2 (an ex ension o he amewo k app o ed
by i s c ea o , Venka esh): Habi (HT), P ice Value (PV), and Hedonic Mo i a ion
(HM). These a iables in luence he in e nal decision-making p ocess and di ec ly
shape Beha iou al In en ion (BI) and Use Beha iou (UB).
In he li e a u e e iew sec ion, he s udies ha highligh he ele ance o including
hese cons uc s in he esea ch a e ci ed, and hey se e as he jus i ica ion o hei
44
4| F amewo ks
inclusion in he s udy. Howe e , he s udy does no assume p ede ined in e ac ions
be ween hese a iables and adop ion ou comes; hei ole will be explo ed h ough
empi ical indings.
• Habi (HT) cap u es he ex en o which a me s de elop au oma ic beha iou s
in using a echnology o e ime (Venka esh, Thong, and Xu 2012). P io s udies
sugges ha long- e m adop ion is no solely based on ini ial in en ion bu also
on he de elopmen o habi ual use (Handoko Pu a e al. 2023). Since habi
o ms h ough epea ed exposu e and usage, i is classi ied as an endogenous
mechanism—i does no exis independen ly o he a me ’s own beha iou bu
ins ead eme ges h ough expe ience. Thus, while pas esea ch sugges s a link
be ween habi o ma ion and long- e m use, his s udy will examine whe he
and how his p ocess occu s among small a me s.
• P ice Value (PV) ep esen s he a me ’s pe cep ion o he bene i s o a
echnology ela i e o i s cos (Venka esh, Thong, and Xu 2012). Financial
cons ain s a e a majo ba ie o smallholde a me s, making pe cei ed alue
a c ucial de e minan o adop ion (Fa idi, Ka oosi-Kalashami, and Bilali 2020).
P ice Value is an in e nal cogni i e e alua ion, meaning i is shaped by an
indi idual's subjec i e assessmen o cos s and bene i s. When he pe cei ed
bene i s ou weigh he cos s, P ice Value migh ha e a posi i e impac on
Beha iou al In en ion.
• Simila ly, Hedonic Mo i a ion (HM) e e s o he enjoymen o in insic
sa is ac ion de i ed om using he echnology (Venka esh, Thong, and Xu
2012). Al hough adop ion in ag icul u al con ex s has adi ionally been s udied
h ough unc ional o economic lenses, ecen indings indica e ha posi i e
use expe ience can enhance adop ion, pa icula ly o mobile-based
ag icul u al echnologies (An wi-Boampong e al. 2024). Since Hedonic
Mo i a ion is an in e nal psychological ac o ha a ises om indi idual
pe cep ions a he han ex e nal condi ions, i is also classi ied as an
endogenous mechanism. Wha li e a u e says is ha highe Hedonic
Mo i a ion will posi i ely impac Beha iou al In en ion (BI), pa icula ly
among younge o mo e ech-sa y a me s. Howe e , his s udy will conside
all o he possible impac s, no jus his aspec .
4.2.3. In oducing a Mode a ing Va iable: Technology Familia i y
The o iginal UTAUT model includes gende , age, expe ience, and olun a iness as
mode a ing ac o s. Adding Technology Familia i y will cap u e p io exposu e o
simila echnologies. S udies like (Handoko Pu a e al. 2023) ound ha a me s who
ha e p e iously in e ac ed wi h ela ed echnologies may pe cei e adop ion as easie
and be less esis an o change so i is a good a gumen o conside his mode a o in
he s udy and analyse i s impac .
4| F amewo ks
45
Unlike exogenous p edic o s, which shape he decision-making en i onmen , and
endogenous p edic o s, which in luence beha iou al ou comes, Technology
Familia i y unc ions as a mode a o because i does no di ec ly de e mine adop ion
bu modi ies he s eng h o ela ionships be ween key a iables.
4.2.4. Expanding he Model wi h new Ou comes: Sus ained Adop ion
(SA)
Mos echnology adop ion models, including UTAUT, ocus p ima ily on ini ial
adop ion (Use Beha iou , UB). Howe e , adop ion does no always gua an ee long-
e m use, and his migh be a p oblem when ea ing collabo a i e models. Many small
a me s adop new echnologies only o abandon hem la e due o inancial
di icul ies, usabili y issues, o shi ing p io i ies (Ronaghi and Fo ouha a 2020).
To add ess his limi a ion, his s udy in oduces Sus ained Adop ion (SA) as an
addi ional ou come a iable. Sus ained Adop ion e e s o he con inued use o
echnology o e ime, beyond he ini ial adop ion phase. S udying his ac o is
c ucial, as i di ec ly in luences he long- e m iabili y and e ec i eness o a sha ed
business model.
4.3. UTAUT and TPB as Analy ical Tools
Building upon he adap a ions ou lined in he p e ious sec ion, his s udy applies he
inal s uc u es o UTAUT and TPB as analy ical ools. This means ha hese
amewo ks se e as guiding s uc u es o da a collec ion and analysis a he han
ixed models wi h p ede e mined ela ionships o assumed signi icance o cons uc s.
The UTAUT amewo k, in pa icula , p o ides a lexible base s uc u e whe e he
selec ed cons uc s a e d awn om exis ing li e a u e and ela ed o he hesis ocus,
bu hei in e ac ions and ele ance emain subjec o eassessmen based on
empi ical indings. The connec ions be ween UTAUT cons uc s a e no p ede ined,
allowing he da a o e eal which ela ionships a e mos meaning ul o whe he
addi ional cons uc s should be conside ed based on eal-wo ld insigh s om small
a me s.
By con as , TPB is used in i s o iginal o m wi hou modi ica ion as al e na i e
e sions ha e no been as widely alida ed. I s co e componen s—a i udes,
subjec i e no ms, and pe cei ed beha iou al con ol—a e conside ed b oad enough
o cap u e a me s’ decision-making ega ding sha ed business models. Howe e ,
while i s s uc u e emains unchanged, his s udy does no impose assump ions on he
weigh o in luence o each cons uc be o e da a collec ion. To e ine he indings, as
al eady men ioned, he e a e addi ional ques ions ha may help in e p e
inconsis encies, al eady commen ed in he li e a u e, when applying his model o he
complex in insic aspec s o SBMs.

46
4| F amewo ks
The ollowing Table 4.1 expands on he de ini ions p o ided in Table 2.1 o he UTAUT
concep s used in his s udy. Figu e 4.1 p esen s he empla e o he UTAUT model o
be applied, aiming o de e mine which ac o s in luence adop ion and sus ained used,
whe he any addi ional ac o s should be included and how hey ela e o each o he .
Las ly, Figu e 4.2 depic s he TPB in i s o iginal o m; al hough i emains unchanged,
he impo ance o i s links s ill needs o be con i med. Bo h amewo ks se e as he
co e s uc u e o he in e iews and will be u ilized in da a collec ion, p esen a ion,
and analysis.
Concep
De ini ion
Bibliog aphy
En i onmen al
Unce ain y
“Re e s o he speed and in ensi y o
echnological change and ma ke changes in he
indus y”
(Lissillou e al.
2024)
Habi
"The ex en o which people end o pe o m
beha iou s au oma ically"
(Venka esh,
Thong, and Xu
2012)
P ice Value
“Consume s’ cogni i e ade-o be ween he
pe cei ed bene i s o he applica ions and he
mone a y cos o using hem”
(Venka esh,
Thong, and Xu
2012)
Hedonic
Mo i a ion
"The un o pleasu e de i ed om using a
echnology, which has been shown o play an
impo an ole in de e mining echnology
accep ance and use"
(Venka esh,
Thong, and Xu
2012)
Technology
Familia i y
“The le el o p io exposu e o amilia i y an
indi idual has wi h simila echnologies”
(Handoko Pu a
e al. 2023)
Sus ained
Adop ion
Re e s o he con inued and long- e m use o a
echnology beyond ini ial adop ion
Own
elabo a ion
Table 4.1: Addi ional UTAUT concep s and hei de ini ions. Sou ce: Own elabo a ion
4| F amewo ks
47
Figu e 4.1: UTAUT Templa e o Analysis. Sou ce: Own elabo a ion
Figu e 4.2: TPB empla e o Analysis. Sou ce: (Knaude and Koschmiede 2018)
49
5 Da a P esen a ion
As ou lined in he 3. Me hodology, he collec ed in e iew da a ollows a s uc u ed
o ma aligned wi h he UTAUT and TPB amewo ks o ensu e consis ency and
acili a e analysis. While mos esponses i wi hin hese models, addi ional sec ions
cap u e insigh s beyond hei scope.
Each a me 's da a is p esen ed indi idually, co e ing:
1. In oduc ion: Gene al p o ile (demog aphics, expe ience, olun a iness o use,
echnology amilia i y, and challenges).
2. Technologies Used and Case S udy Selec ion: O e iew o adop ed o ejec ed
echnologies o UTAUT analysis, and, i applicable, he echnology conside ed
in he TPB-based hypo he ical scena io.
3. UTAUT-Based Analysis: Ca ego iza ion o esponses unde key cons uc s
in luencing echnology adop ion and sus ained use.
4. TPB-Based Analysis: E alua ion o a i udes, subjec i e no ms, and pe cei ed
beha iou al con ol ega ding sha ed business models.
5. In insic Sha ed Model P e e ences: Addi ional insigh s in o p e e ences and
conce ns beyond TPB.
6. Addi ional Insigh s: Eme ging hemes o unexpec ed indings.
This s uc u ed p esen a ion ensu es a clea ansi ion in o he wi hin-case and c oss-
case analysis in 6. Resul s and Discussion.
56
5| Da a P esen a ion
5.3. Be a
1. In oduc ion
Be a (27 yea s old) has been in ol ed wi h li es ock om an ea ly age; hough he
o mal engagemen s a ed oughly en yea s ago; she g ew up a ound a ming
ac i i ies nea The Mon seny Massi . She is close o comple ing he e e ina y s udies
and is cu en ly wo king in a mid-sized dai y a m wi h abou 260 milking cows,
alongside eigh o he wo ke s. She also has a pa ne who aises bee ca le in he same
acili y. Despi e common pe cep ions o economic unce ain y in he li es ock sec o ,
Be a belie es ha , a p esen , one can make a decen li ing. Howe e , she no es ha
his can shi ab up ly depending on luc ua ing ma ke p ices and policies: “I can
change omo ow, and we can be uined because you ne e know how he ma ke will
go”.
She is also qui e used o echnological solu ions, desc ibing he sel as mo e open-
minded han olde gene a ions migh be. As she s a ed, “I’m no oo quali ied, bu I
y o emb ace he ech. The only p oblem is maybe we don’ use i s ull po en ial”.
2. Technologies Used and Case S udy Selec ion
He a m uses se e al echnological ools, bu he p ima y ocus o his case s udy is
he elec onic colla sys em o dai y cows, which moni o s ac i i y le els, umina ion,
and po en ial heal h/hea signs. This echnology was adop ed o imp o e b eeding
e iciency and o e all he d managemen . Aside om ha , he a m has a s anda d
milking pa lou and ce ain ag icul u al machine y equipped wi h inno a ions (e.g.,
compu e ized eeding).
• Adop ed Technology o UTAUT: The colla -based senso sys em
• Po en ially Conside ed bu No Adop ed: Full-au oma ion solu ions (e.g.,
ad anced obo ic milking sys ems o u he analy ics) we e no pu sued due
o cos and he complexi y o aining employees.
3. UTAUT-Based Analysis
1. Pe o mance Expec ancy
• Expec a ion: Au oma e and imp o e hea de ec ion, educe manual
obse a ion ime, and enhance b eeding ou comes.
• Ou come: The sys em has “exponen ially inc eased e ili y” because hey
can insemina e he cows in he op imal ime ame and de ec heal h conce ns
ea ly.
• She epo s ha “i sa es us om cons an ly wa ching all he cows and
guessing i hey’ e in hea ”
2. E o Expec ancy

5| Da a P esen a ion
57
• Expec a ion: S aigh o wa d app o compu e in e ace, minimal lea ning
cu e.
• Ou come: Be a conside s he undamen als easy bu suspec s he e a e
ad anced ea u es hey a e no ully exploi ing. “Su ely he e a e unc ions
we s ill don’ know, bu we lea ned enough o day- o-day wo k”.
3. Social In luence
• The decision was pa ly in luenced by neighbou s who p aised he colla s’
bene i s. “Those who al eady had i old us ha hey we e so pleased, so we
hough , ‘Le ’s do i oo!’”.
4. Facili a ing Condi ions
• Be a el p epa ed ega ding budge , labou , and basic ech know-how.
The e we e no majo connec i i y issues in hei ba n, and he endo
p o ided aining sessions.
• She ema ks ha “one unquali ied pe son can s ill lea n i , bu i helps o
ha e some backg ound”.
5. Hedonic Mo i a ion
• She admi s i was exci ing o b ing mode n echnology o a adi ionally
udimen a y sec o . “When some hing new and echy comes o he a m, i ’s
un”.
6. P ice Value
• She ound i jus i iable because be e ep oduc i e pe o mance can quickly
ecoup cos s in a mid-sized dai y. “I’m su e i we coun ed he ex a li e s
hanks o be e e ili y, i ’s wo h i ”.
7. Habi
• The colla sys em in eg a ed well in o daily ou ines: “I hough i would be
a dis up ion, bu i jus simpli ied insemina ion planning”.
8. En i onmen al Unce ain y
• She ecognizes dai y a ming is subjec o ma ke swings, bu he colla
sys em emains aluable ega dless o sho - e m p ice changes. “As long as
i helps keep he he d heal hy and p egnan on schedule, I’ll keep using i ”.
4. TPB-Based Analysis
In a hypo he ical scena io o sha ing machine y (like a ac o o specialized ha es
equipmen ), Be a ga e he pe spec i e:
1. A i ude Towa d Beha iou
58
5| Da a P esen a ion
• She inds i p ac ical and “ideal” in heo y, especially o less equen ly used
o expensi e machines. “Many machines a e only used a ew imes a yea ,
so i makes sense o sha e”.
2. Subjec i e No m
• Family and o he a me s migh be somewha scep ical due o adi ion o
ea o con lic s, bu she pe sonally sees no majo ba ie . “I he sec o we e
mo e coope a i e, we’d all bene i ”.
3. Pe cei ed Beha iou al Con ol
• She eels ully capable o implemen ing a sha ed sys em. He only conce n
is whe he enough neighbou s would commi and main ain he equipmen
p ope ly. “I’d do i i e e yone played ai . The p oblem is always us ”.
4. Beha iou al In en ion
• Be a would be likely o expe imen wi h a sha ed model i o he s showed
genuine in e es . She emains lexible wi h scheduling and sees syne gy:
“We ha e 24 hou s in a day; we can o a e usage. The ques ion is i people
wan o sha e”.
5. In insic Sha ed Model P e e ences
Ou side he o mal TPB cons uc s, Be a p o ided insigh s on:
• Financial Feasibili y s. Collec i e Mindse : She is open o spli ing cos s on
expensi e, seasonally used equipmen . “The only p oblem is he ypical ‘I wan
he bigges ac o ’ mindse ”.
• Main enance Conce ns: She expec s a s aigh o wa d sys em whe e each use
pe o ms ou ine checks, and any majo b eakdown is paid collec i ely.
• P e e ence o Familia Pa ne s: While she would sha e wi h s ange s i well
o ganized, she p e e s o do so wi h a me s she us s o a oid con lic s.
6. Addi ional Insigh s
• She sees bu eauc acy as a bigge h ea o a m iabili y han echnology cos s,
poin ing ou ha “Some days I eel he adminis a ion is he eal daily ba le”.
• Posi i e Ou look on Technology: Be a does no shy away om in es men s i
a clea e u n o wo kload educ ion is e iden .
• Fu u e Goals: She in ends o emain ac i e bo h as a e e ina ian and a li es ock
a me , using echnology “ o balance e e y hing mo e e icien ly”.
5| Da a P esen a ion
59
5.4. Sancho
1. In oduc ion
Sancho (55 yea s old) spen wo decades supe ising a u ni u e ac o y be o e
ansi ioning in o a ming a ound 2005. He cu en ly manages oughly 30 hec a es o
ci us (p ima ily o anges and manda ins), plus smalle plo s o oli es and ca ob ees,
nea To osa. He ope a es wi hin a coope a i e s uc u e—pa ly o ensu e s able sales
channels, pa ly o gain access o echnical guidance and be e inpu p ices. Sancho
highligh s wo consis en challenges in his ci us ope a ion: he i s is pes
managemen : An in lux o new pes s om ou side he EU, coupled wi h s ic e local
egula ions on pes icides. The second he p o i abili y p essu e: G ea e o eign
compe i ion, supe ma ke demands, and luc ua ing p ices.
Despi e hese hu dles, he is open o g adual echnological imp o emen s ha p omise
angible bene i s, such as imp o ed wa e managemen . He cha ac e izes himsel as
“no a he o e on o inno a ion, bu I keep an eye on use ul solu ions”.
2. Technologies Used and Case S udy Selec ion
Sancho’s a m uses mode n a ia o -equipped i iga ion pumps, which adjus mo o
speed based on ac ual p essu e equi emen s, he eby sa ing ene gy. He also u ilizes
mo e con en ional ag icul u al ools ( ac o s, a omize s), some imes making
inc emen al upg ades. While he is awa e o ad anced inno a ions (such as d ones o
sophis ica ed senso s), he cos s and unce ain e u n ha e so a discou aged him
om adop ing hem.
• Chosen Adop ed Technology (UTAUT Case): Va iable-speed i iga ion
sys em o his ci us ields.
• Po en ial Technology Conside ed bu No Adop ed: High-end o cha d d ones
o sa elli e-based p ecision ools—deemed oo expensi e ela i e o he
unce ain yield imp o emen s o a medium-scale o cha d.
3. UTAUT-Based Analysis
1. Pe o mance Expec ancy
• Expec a ion: Reduce elec ici y cos s and op imize wa e low.
• Ou come: The adjus able pump sys em has lowe ed ene gy consump ion
by ma ching mo o speed wi h eal- ime p essu e. He is pleased wi h
hese gains: “You no longe un he mo o a ull e s when you don’
need ha much p essu e”.
2. E o Expec ancy
• Expec a ion: Mode a e complexi y o p og am he a ia o , bu wo h
lea ning o e iciency.
60
5| Da a P esen a ion
• Ou come: He ini ially equi ed echnical assis ance, bu once se up, he
usage is “almos plug-and-play.” He ound he lea ning cu e
“accep able”.
3. Social In luence
• Sancho men ions a local a me s’ Wha sApp g oup in which pee s
discuss new p oduc s and expe iences. These con e sa ions in luenced
him o in es in he a ia o pump. “The g oup was key—someone in
[ illage] ecommended i , saying he sa ings we e eal”.
4. Facili a ing Condi ions
• The local powe in as uc u e is gene ally eliable, making i easie o
him o see eal bene i s. The coope a i e also p o ided pa ial guidance
and help in sou cing he a ia o .
• He had o handle he cos himsel , bu sho - e m inancing was
a ailable.
5. Hedonic Mo i a ion
• He does no conside he new sys em “ un” bu app ecia es he sense o
imp o emen and “peace o mind” om mo e s able i iga ion.
6. P ice Value
• The in es men , hough no negligible, was jus i ied by con inuous
sa ings on ene gy bills. “I looked a he numbe s, and in a ew yea s, I
ecoup he cos om he powe sa ings alone”.
7. Habi
• In eg a ing he pump con ols in o his daily ou ine was easy. Once he
sys em pa ame e s a e se , i equi es minimal weaks.
8. En i onmen al Unce ain y
• The majo unce ain ies o him a e ex e nal compe i ion, pes s’
managemen and unp edic able ma ke p ices. He sees no isk o
con inuing use o he a ia o .
4. TPB-Based Analysis
Asked hypo he ically abou joining a sha ed business model (e.g., co-owning la ge
o cha d machine y), Sancho o e ed he ollowing insigh s:
1. A i ude Towa d Beha iou
• He is concep ually posi i e: “We should ha e done his yea s ago.
Ins ead o each ha ing hal -used machines, we could sha e hem”.
2. Subjec i e No m
5| Da a P esen a ion
61
• Family o neighbou s migh ha e mixed eac ions. Some a e open-
minded, while o he s “like o own he bigges ac o o new gea ”
leading o po en ial con lic s o p ide issues.
3. Pe cei ed Beha iou al Con ol
• Technically, he is con iden hey could manage schedules o inances i
e e y hing is well-s uc u ed. Howe e , iming is a s icking poin : “We
all need he machine y a he same pe iod o ci us. Tha can be
complica ed”.
4. Beha iou al In en ion
• He sees a high po en ial o sha ed implemen s, especially hose no used
in ensi ely all yea (e.g., a woodchippe o ce ain ha es aids). Ye he
wo ies ha o asks like pes sp aying, e e yone wan s he a omize in
he same week.
5. In insic Sha ed Model P e e ences
Sancho elabo a ed on addi ional pe sonal p e e ences:
• Flexibili y in Scheduling: He sugges s an in o mal app oach, us ing ha “we
can alk and see who needs i his week, who needs i nex ” as long as e e yone
is coope a i e.
• Main enance Responsibili y: Ideally, each use checks he equipmen a e use
and epo s any issues. La ge epai s could be spli among all.
• P e e ed G oup Size: He is open o wo king e en wi h unknown a me s i
con ac s a e clea . Bu “wi h oo many people, i ’s chaos. Maybe ou o i e is
wo kable”.
6. Addi ional Insigh s
• Coope a i e Membe ship: Sancho is a s ong p oponen o coope a i es o
ma ke ing p oduce and ob aining inpu s a lowe cos .
• Need o a Mo e O ganized Sec o : He lamen s ha “e e yone has bi s o land
sca e ed a ound. I we consolida ed o sha ed mo e, we’d sa e ime and
money”.
• Ma ke Challenges: Ex e nal compe i ion om coun ies using banned
pes icides is a ecu ing us a ion—he belie es ad anced echnology alone
canno sol e such s uc u al issues.

62
5| Da a P esen a ion
5.5. Joan
1. In oduc ion
Joan (52 yea s old) comes om a long lineage o wineg owe s— “ he i h gene a ion”
as he says i and has been o mally ope a ing in he sec o since 2001. His en e p ise
can be ca ego ized as a mic o-wine y, exploi ing nea ly 59 hec a es o ineya ds (and
smalle plo s wi h oli e ees) in a p o ec ed designa ion o o igin (DO) egion nea
To osa. Though he inhe i ed i icul u e adi ions om his amily, he has aken a
mo e mode n, business-o ien ed app oach, in eg a ing e ical p ocesses—g owing
g apes, elabo a ing wine, and comme cializing i . He emphasizes h ee gene al
challenges in his domain: (1) inding and e aining quali ied pe sonnel; (2)
geog aphical isola ion limi ing logis ics; and (3) adap ing o changing ma ke demands
and s ic e legisla ions.
Despi e hese obs acles, he desc ibes himsel as ela i ely com o able wi h
echnology, ha ing in oduced mul iple inno a ions in he winemaking p ocess: “We
handle e e y hing om he ineya d o he bo le, so we absolu ely need he igh
ools, om aceabili y o empe a u e con ol”.
2. Technologies Used and Case S udy Selec ion
Joan’s wine y in es s in a ious echnological sys ems o o e see e men a ion, s o e
his o ical da a, manage logis ics, and ensu e egula o y compliance. These include:
• Re ige a ed s ainless-s eel anks wi h digi al empe a u e con ol
• An ad anced aceabili y so wa e ha moni o s each ba ch om ha es o
bo ling, cap u ing all ele an da a such as e men a ion cu es, in e en ions,
and cos acking.
Fo he UTAUT case, we ocus on his aceabili y sys em, which he adop ed ea ly on
o be e compliance and p oduc ion managemen . Meanwhile, he echnology he
conside ed bu did no adop was a high-end da a analy ics module o au oma ed
cos o ecas ing and ad anced eal- ime ineya d senso s, disca ded o being oo
expensi e ela i e o pe cei ed bene i s in his mid-scale ope a ion.
3. UTAUT-Based Analysis
1. Pe o mance Expec ancy
• Expec a ion: The aceabili y so wa e would simpli y ul illing legal DO
equi emen s, uni y cos accoun ing, and imp o e p oduc quali y
consis ency.
• Ou come: He ega ds i as indispensable: “I ’s no jus o legal
compliance—knowing exac ly wha happens in each ank helps me
make be e wine.”
2. E o Expec ancy
5| Da a P esen a ion
63
• Expec a ion: Some complexi y was expec ed since i in ol es digi izing
eco ds ha we e once in Excel o on pape .
• Ou come: He ound he lea ning cu e s eepe ini ially han an icipa ed—
“ he i s ime you upload all you da a is pain ul”—bu once
es ablished, day- o-day asks a e “quicke and mo e e icien ”.
3. Social In luence
• As owne -manage , he el p essu e om bo h ex e nal egula ions (DO
and ood sa e y) and a desi e o appea p o essional o dis ibu o s. “Ou
cus ome s wan aceable p oo we mee s anda ds, so i ’s no an op ion
bu a necessi y”.
4. Facili a ing Condi ions
• He assembled a eam ha includes a cella mas e and an ex e nal IT
consul an o p ope implemen a ion. In e ne connec i i y is eliable
enough o so wa e upda es. He in es s signi ican ly in hese
suppo i e esou ces, calling hem “essen ial o a mode n wine y”.
5. Hedonic Mo i a ion
• No speci ically ele an . He doesn’ ind i “ un” bu he acknowledges
he sense o p ide in p esen ing a obus quali y-con ol sys em.
6. P ice Value
• The so wa e is a con inuous expense because o mon hly suppo
con ac s, ye he iews i as necessa y: “I ’s no cheap, bu i sa es us
om chaos and po en ial legal ines—plus i helps wi h cos con ol.
7. Habi
• Upda ing he aceabili y eco ds has become ou ine. He in eg a ed i
ully, s a ing “I ’s pa o he job now—like cleaning he anks o
checking he ines”.
8. En i onmen al Unce ain y
• Ma ke swings (e.g., shi ing consume p e e ences o expo challenges)
emain he bigge unknowns. The so wa e, howe e , con inues o p o e
i s wo h by o e ing eal- ime da a on p oduc ion cos s, enabling nimble
eac ions.
4. TPB-Based Analysis
Asked abou adop ing a sha ed business model, such as pooling esou ces wi h o he
cella s o bo ling lines o s o age acili ies, Joan’s inpu :
1. A i ude Towa d he Beha iou
64
5| Da a P esen a ion
• In heo y, “a sha ed bo ling acili y o uni ied logis ics could be
bene icial.” Howe e , he highligh s conce ns abou comme cial
con iden iali y and b and independence: “I don’ wan o he cella s
seeing my clien s’ labels o olumes”.
2. Subjec i e No m
• He no es a gene al eluc ance among compe ing wine ies o sha e
equipmen . “We’ e all small b ands in he same egion, so we gua d ou
sales leads qui e jealously”.
• Family/s a opinions echo hese conce ns, as hey ea losing con ol
o e p op ie a y p ocesses.
3. Pe cei ed Beha iou al Con ol
• Technically, he belie es a sha ed logis ic o bo ling plan “makes pe ec
sense o educe o e head” bu he in angible issues (con iden ial da a,
ma ke ing compe i ion) hampe ac ual easibili y.
4. Beha iou al In en ion
• Joan exp esses low in en ion o ully adop a b oad sha ed model o
p ocessing o wa ehousing. He migh conside pa ial s eps—e.g.,
en ing space o se ice om a specialized acili y—bu no ully ceding
bo ling o s o age o a communal en i y.
5. In insic Sha ed Model P e e ences
Joan elabo a ed:
• Con iden iali y & B and In eg i y: He is especially cau ious abou le ing
compe i o s see p oduc olumes, p icing, o labels.
• Flexible Scheduling: A cen alized bo ling line as a sha ed model can c ea e
scheduling bo lenecks, “and we need quick eac ion i an o de a i es o i we
see a pe ec window o bo le a in age.”
• Co-In es men s. Ex e nal Se ice: He p e e s a p o essional ex e nal se ice
o co-owne ship wi h di ec local compe i o s.
6. Addi ional Insigh s
• Expansion as Mul iple Businesses in One: He unde sco es ha unning a
wine y is like managing h ee sepa a e uni s: ineya d, cella (indus ial), and
comme cial sales. Each s age has dis inc compliance and echnology needs.
• Pe sonal Mindse : He acknowledges ha many small wine ies s ill ely hea ily
on adi ion and a e slowe o adap o digi al solu ions: “I you don’ ack cos s
p ope ly, you migh be losing money and ne e know i .”
5| Da a P esen a ion
65
• Fu u e: He sees mo e digi al in eg a ion in ineya d moni o ing (e.g., wi h
senso s), bu only i he cos –bene i a io is clea ly posi i e.
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6| Resul s and Discussion
5. Technology Adop ion and Sha ed Business Models: Explo ing he
Connec ion
Gab iel’s case highligh s ha sha ed business models do no necessa ily p omo e
sha ed echnology adop ion. While he emb aces coope a i e he d managemen ,
echnology decisions emain highly indi idual and subsidy-dependen .
6.1.2. Jo di
1. Technology Adop ion
Figu e 6.3: Jo di’s indi idual UTAUT amewo k. Sou ce: Own elabo a ion
1. Key Findings on Beha iou al In en ion (BI):
Beha iou al In en ion (BI) is s ongly linked o Pe o mance Expec ancy (PE), Social
In luence (SI), Facili a ing Condi ions (FC), and P ice Value (PV), while E o
Expec ancy (EE) has a mode a e in luence. PE has a s ong impac , as Jo di ini ially
expec ed echnology o inc ease p oduc ion and e iciency, making i a c ucial d i e
o his BI. SI is also decisi e, as his amily’s decision no o con inue he a m
signi ican ly in luences his pe cep ion, educing mo i a ion o adop new
echnologies. FC plays a key ole, as Jo di equen ly men ions inancing cons ain s,
s a ing ha wi hou g an s, some implemen a ions would no ha e been possible. PV
also has a s ong in luence, as pas bene i s ini ially suppo ed his willingness o adop ,
bu now, wi h no successo and shi ing p io i ies, i nega i ely a ec s BI. EE has a
mode a e link, as aining ini ially mo i a ed him, bu unce ain y abou he u u e can
also de e u he engagemen .
2. Key Findings on Use Beha iou (UB):
Jo di’s BI and Use Beha iou (UB) ha e a mode a e connec ion—he ully in eg a ed
he milking pa lou in o daily ope a ions, bu i s con inued use ul ima ely depended
on b oade economic ac o s, no jus his ini ial in en ion. While he sys em me
expec a ions, i did no p e en him om e en ually lea ing dai y a ming when he

6| Resul s and Discussion
73
indus y became less p o i able. In con as , Facili a ing Condi ions (FC) and UB had
a s ong connec ion— he a ailabili y o unding, and s able milk p ices ensu ed high
ini ial use, bu once ma ke condi ions declined, use was no longe iable despi e he
echnology’s e ec i eness.
3. Key Findings on Sus ained Adop ion (SA):
Jo di’s BI and Sus ained Adop ion (SA) ha e a mode a e connec ion. Al hough he
milking pa lou emained use ul and e icien , long- e m use depended on ex e nal
ac o s, leading him o e en ually phase ou dai y ope a ions. Simila ly, P ice Value
(PV) and SA also showed a mode a e link, while he pa lou was p o i able a he ime
o adop ion, declining milk p ices and inc eased egula ion la e unde mined i s
economic sus ainabili y. Wi hou a con inued inancial e u n, sus ained use became
un easible, making PV a c i ical bu uns able ac o in long- e m adop ion.
4. En i onmen al Unce ain y
EU di ec ly impac s Pe o mance Expec ancy (PE), E o Expec ancy (EE), Social
In luence (SI), Facili a ing Condi ions (FC) and P ice Value (PV), c ea ing
unce ain y in Jo di’s adop ion decisions: PE is weakened as unp edic able policies
and ma ke ins abili y make echnology bene i s unce ain. “E en i I adop he bes
machines, i p ices collapse o policies change, i won’ sa e my a m.” EE is also
a ec ed, as uns able condi ions make lea ning new sys ems eel like an unnecessa y
bu den. SI is educed, as pee s hesi a e o in es , ein o cing cau ion. “No one a ound
me is making big in es men s now. We’ e all wai ing o see wha happens.” FC is
uns able, as access o inancing and subsidies is unce ain, limi ing Jo di’s abili y o
upg ade. “I banks won’ lend o subsidies disappea , he e’s no way I can upg ade
my equipmen .” PV is diminished, as inancial insecu i y makes long- e m
in es men s iskie . “Spending 50,000 € on au oma ion only makes sense i I know I’ll
s ill be in business in 10 yea s”.
5. Added exogenous a iable: Fa m Succession (FS)
Fa m Succession (FS) is a key Exogenous Mechanism added as i di ec ly impac s
Pe o mance Expec ancy (PE), Social In luence (SI), and P ice Value (PV). FS
weakens PE as Jo di sees no long- e m bene i in adop ing echnology i his a m will
no con inue beyond him. SI is modula ed, as his sons’ decision no o con inue in
a ming signi ican ly in luences his pe cep ion o social in luence, educing he
ele ance o pee adop ion ends. PV is also weake , as Jo di pe cei es less alue in
in es men s, knowing hei use will be discon inued in a ew yea s.
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6| Resul s and Discussion
2. Sha ed Business Model Engagemen
Figu e 6.4: Jo di’s indi idual TPB amewo k. Sou ce: Own elabo a ion
1. Key Findings on In en ion o Engage in a Sha ed Model:
• A i ude: Jo di is scep ical o coope a i es due o a nega i e pas expe ience
wi h a abbi - a ming coope a i e. “You ge s uck i he coope a i e ails,
and you lose you eedom”.
• Subjec i e No m: His amily s ongly discou ages coope a i e
in ol emen , aluing independence. “A home, hey’d say: ‘Be e o be ee
han ied o some hing ha migh sink us all’”.
• Con ol: He belie es he could pa icipa e in a small-scale in o mal sha ing
a angemen bu sees la ge coope a i e s uc u es as o e ly es ic i e.
2. Key Findings on Ac ual Engagemen in a Sha ed Model:
• Jo di does engage in in o mal esou ce-sha ing (e.g., lending machine y o
us ed pee s).
• He ejec s o mal coope a i e models due o ea o inancial
en anglemen and loss o lexibili y. “I I see a sec o is going downhill, I
wan o exi quickly”.
3. Sha ed Business Model as a Vehicle o Technology Adop ion
1. D i e s In luencing Technology Adop ion Wi hin Sha ed Models:
• Sha ed Financial Risk and In es men : Jo di ecalls ha high-cos
pu chases (e.g., ad anced milking pa lou s, new ac o s) used o be easible
when a ming income was s ong. In cu en condi ions, he no es ha
pooling esou ces could lowe indi idual deb s and make cos ly echnology
mo e a ainable i he e is genuine us among he membe s.
• Po en ial E iciency Gains: When echnology clea ly boos s p oduc i i y o
hygiene (as wi h his olde milking sys em), i inspi es him exci emen . In a
6| Resul s and Discussion
75
sha ed se ing, ha same en husiasm could sp ead, encou aging mul iple
a me s o adop imp o emen s h ough a sha ed model.
• Relie om Upkeep and Main enance: Managing mode n equipmen alone
can be daun ing. Al hough Jo di is eluc an o o malize pa ne ships, he
does acknowledge ha in heo y, dis ibu ing main enance and epai asks
among se e al a me s could help. Fewe pe sonal headaches may make
ad anced ools mo e a ac i e.
• Coope a i e F amewo ks A ac ing Ex e nal Suppo : Jo di bene i ed
om pas public unding (e.g., “young a me ” g an s). While he pe sonally
doub s cu en bu eauc acies, a well-s uc u ed coope a i e migh access
bigge subsidies, which could d i e g oup echnology adop ion.
• Pee -Based Lea ning and Resou ce Exchange: Al hough Jo di p e e s
in o mal a angemen s (“i I know you, I migh loan ou equipmen ”), he
ecognizes ha knowledge-sha ing eme ges na u ally when a me s help
each o he . Wi hin a us ed g oup, hey could sha e bes p ac ices and adap
echnology mo e con iden ly.
2. Ba ie s Wi hin Sha ed Models:
• Dis us Roo ed in Pas Coope a i e Failu es: Jo di explici ly men ions a
“bad as e” om p e ious coope a i e e o s (e.g., wi h abbi s). He ea s
inancial collapses ha would en angle all membe s: “I i ails, hey migh
emba go e e yone.” This expe ience makes him cau ious abou o mal
g oup owne ship.
• Obliga o y Dependence and Loss o Au onomy: In Jo di’s wo ds, once
inside a coope a i e, “you can’ suddenly decide o sell elsewhe e.” Being
ied o g oup decisions clashes wi h his “ ee agen ” mindse —he p e e s
adap ing o changing di ec ion quickly when ma ke s shi .
• Complex In e nal Coo dina ion: Sha ing any piece o equipmen o acili y
(e.g., milking pa lou ) o en equi es ca e ul scheduling o animals and asks
in he same space. Jo di poin s ou ha i mul iple he ds come in, logis ics
become icky unless all animals a e physically close, and all pa ies s ic ly
coo dina e usage.
3. Ba ie s Ou side Sha ed Models
• Ma ke Vola ili y and Low P ices: Jo di emphasizes ha a ming e enue
once suppo ed la ge capi al in es men s bu now is highly unp edic able
(“like he s ock ma ke ”). Technology adop ion becomes isky i incomes
canno gua an ee cos eco e y.
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6| Resul s and Discussion
• Bu eauc a ic Complexi y: He inds o icial equi emen s and sanc ions
inc easingly ha sh. F equen policy shi s make i ough o in es in mode n
ools; a single egula o y slip can esul in hea y ines.
• Age and Lack o Gene a ional Succession: A nea ly 60, Jo di sees no amily
membe s o ca y on he a m. Fo him, majo in es men s in ad anced
equipmen eel unjus i ied wi hou an assu ed u u e hando e .
• P e e ence o Simple, In o mal Exchanges: Jo di’s com o zone is iend-
o- iend a angemen s: “I I know you, I migh ade o loan machine y.”
Scaling his in o o mal g oup owne ship o mul i-pa y con ac s is less
appealing in his con ex .
• Shi ing Sec o Economics: Jo di unde sco es how d as ically a ming
o unes ha e changed. He once easily ea ned enough o new machine y;
now, ex e nal compe i ion and lowe p o i ma gins discou age big ou lays
on mode n ech.
4. De ia ions om Li e a u e Re iew:
• Pas Coope a i e Failu es as a De e en : Al hough he li e a u e no es us
issues, Jo di highligh s a s ong “bad as e” om pe sonally wi nessed
coope a i e collapses—making him eluc an o engage in o mal g oup
owne ship again.
• P e e ence o In o mal, One-on-One Exchanges: S anda d amewo ks
o en emphasize s uc u ed coope a i es; Jo di ins ead desc ibes a com o
zone o “ a ou s and small ades” aligning mo e wi h ad-hoc mu ual help
han a well-de ined sha ed model.
• Fa m Succession as an Unde lying Conce n: Jo di’s case highligh s he
absence o gene a ional succession as a key ac o shaping his adop ion
decisions. Jo di’s eluc ance o in es in new echnology s ems om
unce ain y abou he a m’s u u e. This sugges s ha , o a me s wi hou
a successo , he pe cei ed bene i s o adop ion may diminish, making sho -
e m p ac icali y a s onge d i e han long- e m sus ainabili y.
5. Technology Adop ion and Sha ed Business Models: Explo ing he
Connec ion
Jo di’s case shows ha s ong ma ke condi ions, a he han collabo a ion, d o e his
echnology adop ion. While he engages in in o mal ag eemen s, he iews s uc u ed
coope a i es as oo isky. His ocus on inancial independence makes sha ed
echnology in es men s unappealing.
6| Resul s and Discussion
77
6.1.3. Be a
1. Technology Adop ion
Figu e 6.5: Be a’s indi idual UTAUT amewo k. Sou ce: Own elabo a ion
1. Key Findings on Beha iou al In en ion (BI):
Beha iou al In en ion (BI) is s ongly linked o Pe o mance Expec ancy (PE) and
P ice Value (PV), wi h Social In luence (SI) and Facili a ing Condi ions (FC) playing
a seconda y ole. PE was a decisi e ac o , as Be a adop ed he colla sys em expec ing
imp o ed e ili y a es and he d managemen . PV also had a s ong impac , as she
belie ed he sys em would pay o i sel h ough inc eased milk p oduc ion. SI was
no a key d i e bu p o ided a inal push, as ecommenda ions om o he a me s
ein o ced he decision. The same case is o FC as i p o ided he endo guidance o
eased adop ion. O he ac o s, such as he exci emen she inds in using i (Hedonic
Mo i a ion) and i s seamless adap a ion o he ou ine (Habi ), we e posi i e aspec s
she highligh ed bu we e nei he c i ical no de e minan in he adop ion.
2. Key Findings on Use Beha iou (UB):
Be a’s Beha iou al In en ion (BI) and Use Beha iou (UB) ha e a s ong connec ion,
as she ully in eg a ed he colla sys em in o daily ope a ions, egula ly using e ili y
acking ea u es o op imize insemina ion iming. Howe e , Facili a ing Condi ions
(FC) ha e a mode a e link o UB, as while he sys em is e ec i e, i s use depends on
s able a m ope a ions and access o necessa y esou ces (mode a e because i ’s
unlikely o change). E o Expec ancy (EE) also plays a mode a e ole, as limi ed
aining and amilia i y wi h all a ailable ea u es cons ain he sys em’s ull
u iliza ion
3. Key Findings on Sus ained Adop ion (SA):
Be a’s Beha iou al In en ion (BI) and Sus ained Adop ion (SA) ha e a s ong
connec ion, as she con inues using he sys em due o i s di ec impac on a m
e iciency. P ice Value (PV) also has a s ong link o SA, as she pe cei es he

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6| Resul s and Discussion
echnology as an in es men ha gene a es inancial e u ns. The only eason she
would conside discon inuing i s use is i new inno a ions eme ge ha signi ican ly
imp o e his PV e iciency. She o esees con inued use. Despi e ma ke luc ua ions
in he dai y indus y, she conside s he colla s indispensable o main aining he d
heal h and maximizing e ili y.
4. En i onmen al unce ain y
En i onmen al Unce ain y (EU) is a key Exogenous Mechanism as i di ec ly
impac s Pe o mance Expec ancy (PE) and P ice Value (PV), ein o cing Be a’s
con idence in he echnology adop ion. PE is s eng hened, as she ecognizes ha he
colla sys em emains e ec i e despi e ex e nal challenges, ensu ing s able e ili y
acking ega dless o ma ke o policy shi s. PV is also ein o ced, as she pe cei es
he echnology as a eliable in es men ha con inues o gene a e inancial e u ns
e en in unce ain economic condi ions. Unlike cases whe e EU discou ages adop ion
due o poo echnology s abili y in on o changes, in Be a’s case, i enhances he
pe cei ed eliabili y and alue o he sys em due o i s obus ness, making sus ained
use mo e likely.
2. Sha ed Business Model Engagemen
Figu e 6.6: Be a’s indi idual TPB amewo k. Sou ce: Own elabo a ion
1. Key Findings on In en ion o Engage in a Sha ed Model:
• A i ude: Be a iews sha ed models as highly p ac ical, pa icula ly o
la ge, in equen ly used equipmen . “Many machines a e only used a ew
imes a yea , so i makes sense o sha e.”
• Subjec i e No m: While she is pe sonally open o sha ed models, she
acknowledges cul u al esis ance wi hin he a ming communi y. “I he
sec o we e mo e coope a i e, we’d all bene i .”
6| Resul s and Discussion
79
• Con ol: She eels ully capable o implemen ing a sha ed sys em bu
iden i ies us as he p ima y challenge. “I’d do i i e e yone played ai .
The p oblem is always us .”
2. Key Findings on Ac ual Engagemen in a Sha ed Model:
• Be a has no pa icipa ed in a o mal coope a i e bu concep ually
suppo s sha ed owne ship.
• She acknowledges ha compe i ion among a me s ("wan ing he bigges
ac o ") limi s collabo a ion, despi e he clea economic ad an ages.
3. Sha ed Business Model as a Vehicle o Technology Adop ion
1. D i e s In luencing Technology Adop ion Wi hin Sha ed Models:
• Reduced Indi idual In es men and Risk: Be a acknowledges ha
mode n ag icul u al echnology—such as ad anced colla sys ems—o en
in ol es a big inancial commi men . In a sha ed model, mul iple a me s
could spli hese cos s, making subs an ial ech in es men s less daun ing.
• Highe Ope a ional E iciency: She highligh s how echnology boos s daily
p oduc i i y (e.g., au oma ed hea de ec ion o insemina ion). Ex ending
his logic o sha ed machine y o esou ces, Be a sees he po en ial o each
pa icipan o s eamline asks wi hou each pe son buying e e y hing
indi idually.
• Be e Access o Specialized Equipmen : Be a’s a m occasionally uses
machine y only a ew mon hs pe yea (e.g., plan e s, slu y anks).
Acqui ing expensi e ools join ly would le mul iple a ms bene i om
high-quali y equipmen wi hou incu ing he en i e pu chase cos .
• Posi i e A i ude Towa d Collabo a ion: Concep ually, Be a s ongly
suppo s coope a i e solu ions— “Fo me, I hink i ’s ideal…”— o minimize
was e and imp o e inancial sus ainabili y. She belie es ha i pa icipan s
we e genuinely commi ed, hey could coo dina e usage e ec i ely.
• Syne gy wi h Technologically P og essi e Mindse : Be a eadily
emb aces inno a ion (colla s, compu e acking), so in p inciple, she eels a
collec i e a angemen would enable e en mo e ad anced solu ions.
2. Ba ie s Wi hin Sha ed Models:
• Dis us and Social F ic ion: Be a no es ha some neighbou s ea o he s
will abuse o b eak sha ed machine y: “I ’s ine un il someone damages
some hing— hen who pays?” She sees mu ual suspicion as a p ima y hu dle
p e en ing s able coope a i e owne ship.
• Complex Scheduling and Coo dina ion: E en i a me s ag eed o sha e a
esou ce, hey mus juggle peak usage imes. Be a migh need a ool exac ly
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6| Resul s and Discussion
when ano he a me also does, equi ing nego ia ion and us -based
lexibili y.
• Resis ance om Family o Pa ne s: She emphasizes ha in a amily- un
a m, e e yone’s opinions ma e : “We all ha e o ag ee.” Some ela i es
migh be isk-a e se o p e e exclusi e owne ship, making i ough o
commi o a sha ed plan.
• P e e ence o Familia i y: Be a would only conside sha ing echnology
wi h us ed pa ne s a he han un amilia a me s, as us and eliabili y
a e c ucial o he . She belie es ha wi hou s ong pe sonal ela ionships,
sha ed owne ship could lead o con lic s o e main enance, scheduling, o
esponsibili y.
• Po en ial Legal/Con ac ual En anglemen s: I a pa icipan qui s mid-
p ojec , owne ship sha es become complica ed. Be a acknowledges ha
while a clea con ac would help, many a me s a e wa y o legal
o mali ies, u he inhibi ing coope a i e en u es.
3. Ba ie s Ou side Sha ed Models:
• Ma ke Vola ili y and Unce ain P o i s: Al hough Be a s a es ha cu en
dai y and bee ma ke s a e ela i ely a ou able, she ecognizes how quickly
p ices can plumme . High-cos ini ia i es—e en i sha ed— emain isky
wi hou s able u u e e enues.
• Ongoing Bu eauc a ic Bu dens: Like many a me s, Be a complains abou
adminis a i e demands. Adding a sha ed business s uc u e could b ing
ex a pape wo k (con ac s, cos spli s, e c.).
• Technology Dependence: Be a wo ies ha “I e e y hing is oo digi al,
wha i i ails?” Expensi e o highly au oma ed sys ems can be double-
edged i main enance o b eakdowns go beyond he g oup’s capaci y o
manage hem.
• Local Cul u e o Compe i ion: In he egion, some a me s p ize ha ing he
“bigges ac o ” and ope a e compe i i ely a he han coope a i ely. This
mindse unde cu s en husiasm o join owne ship, despi e any po en ial
cos o e iciency ad an ages.
4. De ia ions om Li e a u e Re iew:
• Tension Be ween En husiasm and Local Dis us : The impo ance o us
is well documen ed (De To o and Hansson 2004), ye Be a’s en husiasm o
“ideal” sha ing con as s sha ply wi h he local eluc ance she obse es—
unde sco ing how subjec i e no ms and a i udes owa d sha ing can
di e ge e en wi hin he same communi y.
6| Resul s and Discussion
81
• Highly Selec i e Collabo a ion: Whe eas s anda d coope a i e models
assume a ela i ely open g oup (Laukkanen and Tu a 2020), Be a insis s on
sha ing only wi h pe sonal con ac s. This na owe app oach demons a es
ha a me s may adop pa ial o “closed-ci cle” coope a ion, shaped by
s ong social ies.
• Scep icism o O e -Reliance on Ad anced Tools: The li e a u e discusses
acili a ing condi ions and e o expec ancy, ye Be a adds a “wha i i all
ails?” wo y—an insigh ha ad anced digi al solu ions can igge ea o
po en ial b eakdowns o co e age gaps no ully add essed in ypical cos -
bene i analyses.
• Cul u al Ba ie s O e Financial Incen i es: While esea ch highligh s
inancial incen i es as a key d i e in sha ed models, Be a sees cul u al
a i udes as a majo ba ie . Fa me s p io i ize independence and
compe i ion, esis ing collabo a ion e en when esou ce pooling would be
inancially ad an ageous. S udies discuss cos -sa ing incen i es as a
mo i a o o coope a ion (De To o and Hansson 2004), bu he case
highligh s how dis us can o e ide economic logic, limi ing sha ed
echnology adop ion.
5. Technology Adop ion and Sha ed Business Models: Explo ing he
Connec ion
Be a’s case shows ha echnology adop ion occu s independen ly o sha ed business
models. He decision o implemen he colla sys em was d i en by pe o mance
bene i s a he han collabo a i e oppo uni ies. While she suppo s sha ed models in
heo y, he eali y is ha she isn’ in ol ed in any. When eally conside ing i , she sees
us , adi ion, and indi idualism as g ea e ba ie s, e en wi h inancial gains. Unlike
some a me s, she does no ejec sha ed owne ship ou igh , bu he willingness
depends a lo on he igh condi ions, in his case no well e alua ed by he TPB
amewo k alone.
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6| Resul s and Discussion
• A i ude: Joan sees po en ial bene i s in sha ed logis ics and bo ling
acili ies, bu he p io i izes b and con iden iali y and ope a ional con ol.
“I don’ wan o he cella s seeing my clien s’ labels o olumes”.
• Subjec i e No m: Indus y cul u e discou ages sha ing, as wine ies gua d
hei clien lis s and p oduc ion de ails. “We’ e all small b ands in he same
egion, so we gua d ou sales leads qui e jealously”.
• Con ol: While he belie es a sha ed model could wo k o logis ics,
bo ling and s o age in ol e oo many p op ie a y conce ns.
2. Key Findings on Ac ual Engagemen in a Sha ed Model:
• Joan has no engaged in a sha ed business model and emains uncon inced
by hypo he ical scena ios.
• He acknowledges po en ial sa ings bu belie es us ba ie s and
con iden iali y issues ou weigh he cos bene i s.
3. Sha ed Business Model as a Vehicle o Technology Adop ion
1. D i e s In luencing Technology Adop ion Wi hin Sha ed Models:
• Cos E iciency and Resou ce Op imiza ion: Joan ope a es an in eg a ed
wine y ha al eady equi es signi ican capi al (e.g., o aceabili y
so wa e, bo ling lines, s ainless-s eel anks). He no es ha pooling
esou ces ac oss mul iple wine ies could, in heo y, educe indi idual
o e head o expensi e machine y.
• Inc eased Ope a ional Agili y: While Joan emphasizes he impo ance o a
apid u na ound (e.g., bo ling on sho no ice), a sha ed sys em wi h
p o essional s a ing could o e as , on-demand se ices—po en ially
su passing wha smalle wine ies can achie e alone.
• S eamlined S o age and Dis ibu ion: A cen alized wa ehouse o bo ling
acili y could educe edundan in es men s (each wine y ha ing i s own
pa ial se up). Joan poin s ou ha , on pape , “i would be economically
posi i e.”
• Willingness o Collabo a e I “Done Righ ”: He is open o alliances ha
mee p o essional, con iden iali y, and scheduling s anda ds. His main
p io i y is ecei ing imely, high-quali y se ice ha suppo s a quick
esponse when a p oduc needs bo ling o shipping.
2. Ba ie s Wi hin Sha ed Models:
• Simul aneous Peak Demand: Joan no es ha wine ies gene ally need
bo ling o p essing equipmen a oughly he same ha es window.
Sha ing specialized machines (e.g., de-s emme s, p esses) is challenging i
e e yone equi es hem a once.

6| Resul s and Discussion
89
• Comme cial Con iden iali y Conce ns: P oduce s o en hesi a e o e eal
clien s, a ge ma ke s, o p oduc ion olumes. A sha ed wa ehouse o
bo ling line equi es showing “labels, palle s, o shipping da a” which some
wine ies deem oo sensi i e.
• Compe i i e / Indi idualis ic Mindse : Al hough coope a i es exis in
o he ag icul u al ields, he wine sec o o en ea u es nume ous small,
independen b ands ie cely compe ing o limi ed consume a en ion. This
clima e can impede he us equi ed o equipmen co-owne ship.
• Complex Con ac ual Commi men s: Se ing up a join acili y (e.g., a mul i-
wine y bo ling plan ) en ails legal s uc u es o handle buy-ins, usage ees,
exi clauses, and expansions. Joan s esses he necessi y o “s ong
p o essionaliza ion and cla i y” in hese ag eemen s.
3. Ba ie s Ou side Sha ed Models:
• Regula o y Complexi y: Laws demand igo ous aceabili y and labelling.
Al hough a communal app oach migh lowe cos s, i also equi es
ad anced compliance sys ems ha can a y ac oss di e en wine ies’
p oduc lines.
• Geog aphical and Logis ical Cons ain s: Joan’s wine y is in a emo e
egion, whe e anspo is slow and labou is sca ce. E en i a sha ed bo ling
o s o age acili y exis ed, dis ance and iming migh ende i imp ac ical
o day- o-day use.
• Cul u e o Au onomy and Ma ke Vola ili y: Wine p oduc ion is deeply
ied o indi idual iden i y, as b and epu a ion and p emium posi ioning
ely on unique cella p ocesses and es a e-speci ic e oi . Ma ke ola ili y
and shi ing consume p e e ences u he ein o ce he need o
di e en ia ion, making s anda diza ion o sha ed solu ions less appealing.
Su ende ing aspec s o p oduc ion o a communal acili y isk dilu ing a
wine y’s dis inc iden i y, which is essen ial o main aining exclusi i y and
compe i i e ad an age.
4. De ia ions om Li e a u e Re iew:
• B and Con iden iali y s. Sha ed E iciency: Joan unde sco es high-end
wine p oduce s’ eluc ance o expose labelling o dis ibu ion de ails. This
dimension o “compe i i e con iden iali y” is only b ie ly no ed in b oade
coope a i e s udies and is less common among small a me s.
• In eg a ed Mul i-S age P oduc ion: S udies on machine y sha ing o en
ocus on a me s adop ing a single echnology (e.g., digi al machine y) o
speci ic asks (A z & Nae e, 2016). Howe e , Joan’s e ically in eg a ed
model—co e ing ineya d managemen , winemaking, bo ling, and
dis ibu ion—demands mul iple specialized ools ac oss di e en
90
6| Resul s and Discussion
p oduc ion s ages, making “one sha ed machine” models less applicable
and mo e complex o implemen .
• Demand o On-Demand Access: While scheduling con lic s a e
acknowledged (De To o and Hansson 2004), Joan’s push o nea -ins an
a ailabili y aises he ba : sha ed solu ions mus unc ion “wi hin days”
lea ing minimal ole ance o ypical o a ion-based app oaches o smalle
co-op scheduling no ms.
5. Technology Adop ion and Sha ed Business Models: Explo ing he
Connec ion
Joan in eg a es echnology when i enhances e iciency bu emains scep ical abou
sha ed models mainly due o b and secu i y conce ns. While he acknowledges he
po en ial o logis ical coope a ion, he does no see sha ed p oduc ion as iable, as
main aining con ol o e winemaking p ocesses is c ucial o b anding and ma ke
posi ioning. Unlike o he a me s who hesi a e due o un amilia i y wi h sha ed
models, Joan’s eluc ance is s a egic, d i en by indus y-speci ic challenges a he
han a gene al esis ance o collabo a ion.
6.2. C oss-Case Analysis
While he wi hin-case analysis p o ided an in-dep h in e p e a ion o indi idual
decision-making p ocesses, his c oss-case analysis adop s a compa a i e pe spec i e
o enhance he gene alizabili y and heo e ical con ibu ion o he s udy. By
combining indi idual UTAUT amewo ks, a gene alized UTAUT amewo k will be
de eloped, allowing o he s udy o mode a o s. Howe e , i s eliabili y will be
limi ed due o he small sample size. This limi a ion will be add essed in 6.4.Discussion,
whe e indings will be u he examined in ela ion o he li e a u e o ei he con i m
o challenge indings. The same app oach applies o TPB.
Addi ionally, he pa o he analysis whe e sha ed business models a e s udied as a
ehicle o echnology adop ion will consolida e he d i e s and ba ie s iden i ied in
he wi hin-case analysis, g ouping hem o highligh he mos common ones. I will
also de e mine whe he any unique o case-speci ic ba ie s eme ge due o con ex ual
ac o s.
As a di e ence wi h he wi hin-case scena io, he compa ison wi h he li e a u e will
be conduc ed in he 6.4.Discussion in as he las i e a ion, p o iding alida ion and a
deepe e lec ion on he s udy's indings.
1. C oss-Case Pa e ns in Technology Adop ion
To c ea e he combined amewo k, quali a i e da a will be quan i ied in a simple way
using ixed sco e assignmen (Solid = 2, Dashed = 1, No Line = 0). Each connec ion, as
epea ed ac oss he i e a me s, will be summed. This means he maximum possible
6| Resul s and Discussion
91
sco e o a connec ion is 10, while a connec ion ha is ne e men ioned ecei es a 0.
To de ine he h esholds, connec ions sco ing 6 o g ea e will ecei e a solid line
(indica ing ha a leas h ee a me s conside i impo an ), while connec ions sco ing
be ween 3 (inclusi e) and 6 will ecei e a dashed line (ensu ing ha a leas wo
a me s men ion i , wi h a leas one assigning high impo ance, o ha h ee conside
i mode a ely impo an ). The ollowing Table 6.1 con ains hose connec ions ha a e
signi ican when small a me s adop a new echnology:
Fac o A
Fac o B
Sco e
PV
BI
10
PE
BI
9
FC
UB
9
BI
SA
8
BI
UB
8
PV
SA
7
FC
BI
6
SI
BI
6
EU
PE
6
EU
PV
6
EU
FC
4
EE
UB
3
Table 6.1: Cons uc connec ions and a ing. Sou ce: Own elabo a ion
Besides he link be ween cons uc s, he mode a o e ec has also o be conside ed.
They a e p esen ed in a common able o iden i y po en ial pa e ns ha may in luence
he UTAUT collec i e amewo k. Each mode a o is di ided in o wo g oups, and
hese g oups will be analysed o de e mine whe he subs an ial di e ences exis ,
sugges ing a possible mode a ing e ec . While he indings may no be highly
conclusi e, hey will p o ide insigh s ha can be compa ed wi h he li e a u e o
assess hei ele ance. The jus i ica ion o adding Geog aphical Region is below he
ables. Check Table 6.2. Mode a o G ouping o UTAUT. Whi e cells ep esen G oup
One, while ligh blue cells ep esen G oup Two. No e ha g ouping is done by
92
6| Resul s and Discussion
a iable, meaning each a me is ca ego ized independen ly wi hin each a iable,
a he han being assigned o a single o e all g oup.
Fa me
Technology
Familia i y
Age
Gende
Expe i
ence
(yea s)
Volun a ines
s o Use
Geog aphical
Region
Gab iel
Low
42
Male
4
High
Mon seny
Jo di
Mode a e
60
Male
42
High
Mon seny
Be a
Mode a e
27
Female
10
High
Mon seny
Sancho
Mode a e
55
Male
20
High
To osa
Joan
Mode a e
52
Male
50
Mode a e
To osa
Table 6.2. Mode a o G ouping o UTAUT. Sou ce: Own elabo a ion
He e a e he g ouping c i e ia o each a iable:
• Technology Familia i y: Low (No p io exposu e) s. Mode a e (Some p io
exposu e bu no ex ensi e).
• Age: Younge (≤50 yea s old) s. Olde (>50 yea s old).
• Gende : Male s. Female.
• Expe ience (yea s): Less Expe ienced (≤20 yea s) s. Mo e Expe ienced (>20
yea s).
• Volun a iness o Use: High (Sel -d i en adop ion) s. Mode a e (Ex e nal
ac o s in luence adop ion).
• Geog aphical Region: Mon seny s. To osa
The s udy aimed o assess whe he echnology amilia i y, age, gende , expe ience,
olun a iness o use and geog aphical egion in luence he ela ionships be ween
di e en Fac o A o Fac o B pai s. To do his, a mode a ion analysis was conduc ed
using mul iple linea eg essions, whe e each pai was analysed sepa a ely, and
eg essions we e un independen ly o each mode a o . Howe e , he analysis did
no yield signi ican esul s due o limi ed obse a ions pe g oup and, in cases whe e
eg ession was possible, no mode a o showed a s a is ically signi ican p- alue (p <
0.05). Gi en hese limi a ions, a quali a i e app oach based on in e iew esponses
was chosen gi en is he bes al e na i e. The esul s o applying his line a e:
• Technology Familia i y: Fa me s wi h mode a e echnology amilia i y (e.g.,
Be a, Jo di, Joan) end o ely mo e on pee in luence while hose wi h low ech
amilia i y (Gab iel) show mo e eluc ance and p ima ily use echnology when
6| Resul s and Discussion
93
ex e nal ac o s like subsidies acili a e adop ion. So, i is assumed ha
echnology amilia i y mode a es he ela ionship be ween Social In luence
(SI) and Facili a ing Condi ions (FC) wi h Beha iou al In en ion (BI).
• Age: Olde a me s (Jo di, Sancho, Joan) migh exhibi mo e scep icism be o e
acqui ing a new echnology. Younge a me s (Be a) display g ea e openness
o inno a ion and exp ess ewe conce ns abou e o expec ancy. E en Jo di
showed in he in e iew ha when he was younge EE wasn’ e en conside ed
a di icul y: "I you a e exci ed abou some hing, you can lea n o ly a plane."
Fo his eason, age is conside ed o mode a e he ela ionship be ween E o
Expec ancy (EE) and Beha iou al In en ion (BI). As he link EE o BI hasn' been
a signi ican one, his connec ion isn’ shown in he diag am.
• Gende : s udying Be a’s p o ile as unique ep esen a i e o he emales, no
signi ican mode a ion has been iden i ied compa ed o he male g oup.
• Expe ience: Conside ing he g oup o high expe ienced a me s (Jo di and
Joan) he common ai compa ed o hose wi h lowe expe ience was he
e alua ion o PV. Highe expe ience, mo e isk-a e se become and only adop
i hey see s ong cos -bene i impac . Lowe g oups alue i also a lo , bu
in e iews showed ha in some cases i s adop ion was p oduced e en no clea
bene i s we e shown. So, in ou cases, PV was a signi ican ac o in all he cases
bu , his de ec ed pa e n migh be in luen ial in o he cases. So, expe ience
migh mode a e he ela ionship be ween P ice Value and Beha iou al
In en ion.
• Volun a iness o use: The olun a iness o use in ou cases is in luenced by
ex e nal ac o s. Joan, o ins ance, is mode a ely obliga ed by policies. When
adop ion is no olun a y, Facili a ing Condi ions (FC) become mo e c i ical o
ensu e ha Use Beha iou is possible. This explains why Joan e alua ed he
si ua ion and ealized he needed o hi e a echnical eam o acili a e a smoo h
implemen a ion, ensu ing ha all needed condi ions we e me . So,
olun a iness o use mode a es he in e ac ion be ween FC and UB making i
s ic ly impo an in non- olun a y adop ions.
• Geog aphical Region (Added mode a o ): The e a e many easons o include
Geog aphical Region as a mode a o . To osa is a mo e s uc u ed and policy
d i en en i onmen due o he ac i i ies done he e, e y common ag icul u e
(Joan & Sancho). In Mon seny li le a me s a e mo e communi y-based and less
s uc u ed. The e ec o Facili a ing Condi ions on BI in Mon seny i ’s less
impo an as i ’s mo e sel -d i en while in To osa i ’s he opposi e. Social
In luence o BI connec ion is mode a ed by how compe i i e he egion is: in
Gab i, Jo di & Be a’s mo e in o mal coope a ions a e ound, while Joan &
Sancho ope a e in a mo e compe i i e en i onmen . Las ly, Joan & Sancho

94
6| Resul s and Discussion
equi e clea e inancial bene i s be o e adop ing (P ice Value o BI), whe eas
Gab i, Jo di & Be a ake mo e adop ion isks.
Taking in o conside a ion he cons uc s a ing sys em and he mode a o ’s analysis,
he Gene alized UTAUT amewo k is shown in Figu e 6.11.
Figu e 6.11: Gene alized UTAUT amewo k o small a me ’s Technology Adop ion.
Sou ce: Own elabo a ion
2. C oss-Case Pa e ns in Sha ed Business Model Engagemen
Simila ly o UTAUT, a gene alized amewo k will be p esen ed (see igu e Figu e
6.12). In his case, he coun ing scale is a ed humb-down is coun ed as -1, a mid-
humb as 0, and a g een humb-up as 1. This app oach allows us o de e mine he
combined TPB amewo k based on he esponses o he in e iewed a me s.
Figu e 6.12: Gene alized TPB amewo k o small a me ’s engagemen in a Sha ed Business
Model. Sou ce: Own elabo a ion
6| Resul s and Discussion
95
The e alua ion o UTAUT and TPB gene alized amewo ks will be in 6.3. Resul s
Summa y and u he e lec ed in 6.4. Discussion.
3. Sha ed Business Model as a Vehicle o Technology Adop ion: Compa a i e
Findings
In his sec ion, he d i e s and ba ie s iden i ied in he wi hin-case analysis ha e
been g ouped. This ca ego iza ion allows o o ganizing he mos common d i e s
and ba ie s unde b oade concep ual umb ellas, ensu ing ha all iden i ied ac o s
a e sys ema ically classi ied. Howe e , pa icula d i e s and ba ie s a e also
conside ed impo an , as hey highligh speci ic challenges ha indi idual a me s
ace. The ac ha hey a e less common does no make hem any less signi ican and
hey a e a ailable in he 3.7.1.Wi hin-Case Analysis. The gene ic d i e s and ba ie s
will be p esen ed in able o ma o a be e unde s anding:
• D i e s a e he mo i a ions iden i ied in a me s' in e iews o adop ing new
echnology h ough a sha ed business model. See Table 6.3:
D i e s
Desc ip ion
Subdi is
ion (i
applicabl
e)
Case O igin
1. Cos
Sha ing
Fa me s see cos sha ing as a way o make
echnology adop ion mo e a o dable by
spli ing expenses among mul iple use s
ins ead o bea ing he ull cos alone.
Cos Pooling o Majo
In es men s (SANCHO)
Cos E iciency and
Resou ce Op imiza ion
(JOAN)
Reduced Indi idual
In es men and Risk
(BERTA)
2.Financial
Risk and
subsidies
Fa me s see inancial isk educ ion and
subsidies as key ac o s in making
echnology adop ion mo e easible. By
dis ibu ing inancial esponsibili y
among mul iple use s o secu ing ex e nal
unding, hey can lowe indi idual deb
isks and ensu e mo e s able in es men s.
Access o subsidies u he eases he
inancial bu den.
Po en ial o Ongoing
Subsidies (Gab i)
Sha ed Financial Risk
and In es men (JORDI)
Coope a i e F amewo ks
A ac ing Ex e nal
Suppo (JORDI)
96
6| Resul s and Discussion
3.
E iciency
Gains
Fa me s see e iciency gains as
a majo mo i a ion o
adop ing echnology h ough
sha ed models, as i helps
hem op imize esou ces and
imp o e p oduc i i y. Scaling
up ope a ions becomes mo e
manageable, imp o ing
e iciency allows asks o be
comple ed as e and wi h
be e esul s, and educing
los ime on non- alue-adding
ac i i ies. Addi ionally,
a me s highligh g ea e
agili y, imp o ed logis ics
e iciency, educed idle ime,
and be e quali y as key
bene i s o his app oach.
Scale-up
G ea e Ope a ional Scale
(Gab i)
E iciency
Po en ial E iciency Gains
(JORDI)
Highe Ope a ional
E iciency (BERTA)
Los ime
Relie om Upkeep and
Main enance (JORDI)
Agili y
Inc eased Ope a ional
Agili y (JOAN)
Logis ics
S eamlined S o age and
Dis ibu ion (JOAN)
Idle ime
Maximizing Equipmen
Usage (SANCHO)
Quali y
Be e Access o
Specialized Equipmen
(BERTA)
4. Pee
Lea ning
Fa me s see pee lea ning as an impo an
mo i a ion o adop ing echnology
h ough sha ed models, as i allows hem
o exchange knowledge and gain
con idence in using new ools. Lea ning
om o he s educes unce ain y, speeds
up he adop ion p ocess, and some imes
helps hem ecognize new ools o ea u es
ha could be use ul.
Pee Suppo and
Lea ning (Gab i)
Collec i e Lea ning and
Tech Upg ades
(SANCHO)
Pee -Based Lea ning and
Resou ce Exchange
(JORDI)
6| Resul s and Discussion
97
5. A i ude
Fa me s good a i ude is a d i e o
adop ion. A posi i e a i ude owa d
collabo a ion encou ages a me s o wo k
oge he and sha e esou ces and a
echnologically p og essi e mindse
makes hem mo e open o inno a ion and
new solu ions.
Posi i e A i ude Towa d
Collabo a ion (BERTA)
Syne gy Wi h
Technologically
P og essi e Mindse
(BERTA)
Willingness o
Collabo a e I "Done
Righ " (JOAN)
Gene al Openness o
Collabo a ion (SANCHO)
6.
Nego ia io
n powe
No ha ex ended bu being pa o a
coope a i e makes a me s gain s onge
ba gaining powe , allowing hem o
nego ia e lowe p ices, be e se ice
ag eemen s, and imp o ed access o
echnology, a d i e ha Sancho
ecognized.
S onge Ba gaining
Powe (SANCHO)
Table 6.3: D i e s iden i ied as key mo i a ions o small a me s o adop echnology
h ough a sha ed business model. Sou ce: Own elabo a ion
• Ba ie s Wi hin Sha ed Models a e he challenges iden i ied in a me s'
in e iews ha hinde he adop ion o new echnology wi hin a sha ed business
model, see Table 6.4:
Ba ie s
Wi hin
Sha ed
Models
Desc ip ion
Subdi i
sion (i
applica
ble)
Case O igin
1. Dis us
Fa me s see dis us as a key
ba ie o adop echnology
h ough sha ed models. I can
a ise om scep icism owa d
o he a me s beha iou o on
On
o he
Fa me s
Unce ain In e pe sonal
Dynamics (GABRI)
Dis us and Social
F ic ion (BERTA)
104
6| Resul s and Discussion
esea ch, hei ole has o en been o e looked. This s udy p o ides a clea , empi ical
demons a ion o P ice Value’s impo ance, which ep esen s a aluable con ibu ion
o he ield. Simila ly, he in oduc ion o Sus ained Adop ion as a key ou come
a iable is a no el addi ion o he li e a u e, as i has no been ex ensi ely s udied.
Ex e nal ac o s a e o en o e looked in adop ion models, bu his esea ch iden i ies
Regula o y P essu e, Fa m Succession, and Technological Limi a ions (e.g., uns able
GPS connec ions) as ele an in luences. Mos no ably, En i onmen al Unce ain y
eme ges as a c i ical ex e nal ac o , signi ican ly shaping se e al key cons uc s. This
inding is pa icula ly impo an , as i unde sco es he need o in eg a e con ex ual
a iables in o adop ion models.
Finally, he ole o mode a o s in his s udy p esen s some de ia ions om p e ious
esea ch. Technology Adop ion Expe ience was in oduced as a mode a o , as p io
s udies had ecognized i s in luence, ye i was a ely conside ed in UTAUT-based
esea ch. I s inclusion in he model demons a ed a angible impac . Simila ly,
Geog aphical Region p o ed o be an in luen ial mode a o . Expe ience has
adi ionally been iewed as a s onge mode a ing ac o han Age, his s udy
con i ms ha Expe ience plays a mo e signi ican ole. In con as , Volun a iness o
Use—despi e being equen ly no ed in he li e a u e—has been less s udied in
UTAUT, and his esea ch con i ms i s ele ance. As p io s udies sugges ed,
Volun a iness o Use may lead o di e en beha iou al pa e ns, ein o cing i s
impo ance in echnology adop ion amewo ks. The inal mode a o o discuss is
gende . While he li e a u e sugges s ha Gende could ac as a mode a o , he
indings o his s udy indica e o he wise. This may be due o he socioeconomic and
cul u al con ex o he s udy, as i was conduc ed in a egion wi h lowe gende
inequali ies compa ed o o he pa s o he wo ld. As a esul , Gende did no eme ge
as a signi ican mode a ing ac o in his esea ch.
Rega ding he s udy o small a me s in en ion and beha iou in engaging wi h
sha ed business models h ough TPB, as p e iously men ioned, his app oach is less
common in he li e a u e. The gene al indings in he hesis sugges ha Subjec i e
No ms emain he mos c i ical aspec ha needs imp o emen o os e a mo e
posi i e In en ion o engage wi h hese models. In con as , A i ude and Pe cei ed
Beha iou al Con ol show sligh ly mo e a ou able ends. These indings a e
unde explo ed due o he limi ed applica ion o his amewo k in p io esea ch bu
in e es ing because show in wha o ocus i i is wan ed o imp o e he In en ions o
engage wi h a SBM.
Fu he mo e, he case s udy analysis demons a es ha In en ion does no always
ansla e in o ac ual engagemen Beha iou . Some pa icipan s who exp essed a
nega i e opinion o low in en ion s ill engaged in sha ed business models, while
o he s wi h a posi i e in en ion ul ima ely did no pa icipa e in p ac ice.

6| Resul s and Discussion
105
The easons behind hese misalignmen s a e examined h ough he analysis o
d i e s and ba ie s. The li e a u e e iew p esen s a lis o ac o s in luencing small
a me s echnology adop ion, and many o hem a e consis en wi h he indings in
he hesis. Howe e , he li e a u e sugges s ha some o he iden i ied ba ie s, when
pu in o p ac ice, do no always ma e ialize. Fo ins ance, Complex Scheduling and
Coo dina ion, o en conside ed a challenge, in some cases is mo e o a pe cei ed issue
han a eal ba ie (G. A z and Nae e 2016). Ne e heless, he iden i ied d i e s and
ba ie s in his s udy a e no ewo hy, as some a e unique o a ely discussed in p io
esea ch. Examples include Age and Lack o Gene a ional Succession (JORDI) and
Technology Dependence (BERTA).
The p ima y con ibu ion o his hesis ega ding d i e s and ba ie s lies no in
p o iding a gene al o e iew—as many o hese ac o s a e al eady ecognized in
p e ious s udies—bu in iden i ying speci ic, con ex -dependen d i e s and
ba ie s. Exis ing esea ch o en ocuses on b oad gene aliza ions, which can dilu e
a en ion om he oo causes o adop ion challenges. By ocusing on wi hin-case
s udies, his esea ch highligh s speci ic p oblems ha , al hough less equen ly
men ioned in s udies, can be jus as signi ican as he commonly ci ed ba ie s.
The e o e, he con ibu ion o his hesis is no only in con i ming gene al indings
bu also in o e ing a deep, case-speci ic analysis ha p o ides a mo e nuanced
unde s anding o he eal-wo ld complexi ies a ec ing his model o inno a ion
adop ion ocused on small a me s.
The las impo an inding o his in es iga ion, which is no well add essed in he
li e a u e, is he disco e o no co ela ion be ween openness o adop new
echnology and willingness o pa icipa e in sha ed business models, as e idenced
by he di e si y o cases s udied.
6.4.2. Theo e ical and P ac ical Implica ions
The heo e ical implica ions o his hesis lie in he expansion o UTAUT and TPB
wi hin he ag icul u al con ex , pa icula ly o small a me s. I con ibu es o he
li e a u e by alida ing new cons uc s such as P ice Value, Sus ained Adop ion, and
En i onmen al Unce ain y, which ha e p o en o be c ucial in explaining small
a me s' decision-making p ocesses. Addi ionally, i iden i ies new key mode a o s
ha a e ypically o e looked, such Technology Familia i y o Geog aphical Region,
as well as he ole o ex e nal ac o s in shaping adop ion beha iou .
Fu he mo e, his esea ch has highligh ed he gene al nega i i y o Subjec i e No ms
and i ’s in luence dec easing he In en ion o engage in a SBM. A mo e de ailed
classi ica ion o d i e s and ba ie s a e included, bo h wi hin and ou side he sha ing
cul u e, o e ing a clea e unde s anding o he ac o s in luencing a me s'
engagemen wi h sha ed business models o adop inno a ions. Las ly, he
106
6| Resul s and Discussion
dissocia ion be ween openness o echnology and willingness o sha e esou ces
in oduces p ac ical implica ions, which a e discussed in he ollowing pa ag aph.
This esea ch in oduces se e al p ac ical implica ions ha can guide policymake s,
indus y s akeholde s, and a me s owa d mo e e ec i e adop ion o echnology and
sha ed business models. Policymake s can le e age hese indings o design subsidies
and egula o y amewo ks ailo ed o small a me s’ speci ic needs. Addi ionally, he
insigh s p o ide s a egic ecommenda ions o de eloping s uc u ed sha ed
business model o ganiza ions, ensu ing hey align wi h a me s' eal-wo ld
challenges. Ano he c ucial aspec is he need o imp o e in as uc u e and
accessibili y o p e en inequali ies in he implemen a ion o hese models, ensu ing
ha all a me s, ega dless o loca ion o esou ces, can bene i . Mo eo e , he esea ch
highligh s he impo ance o educa ion and awa eness, emphasizing how
sus ainabili y and economic ad an ages can encou age a me s o engage in sha ing
p ac ices and sus ainable business models. Finally, echnology p o ide s and
indus y s akeholde s can in eg a e hese insigh s in o hei alue c ea ion, deli e y,
and cap u e s a egies, adap ing hei o e ings o suppo he easibili y and
a ac i eness o sha ed business models in he ag icul u al sec o .
6.4.3. Limi a ions
Despi e he aluable con ibu ions o his esea ch, se e al limi a ions should be
acknowledged.
One key limi a ion is he limi ed sample size and egional scope. Wi h only i e case
s udies, he esea ch p o ides in-dep h quali a i e insigh s, bu his inhe en ly
es ic s he gene alizabili y o he indings. Fu he mo e, he ocus on only wo
egions may no ully cap u e a ia ions in echnology adop ion and sha ed business
model engagemen ac oss di e en geog aphical, economic, o cul u al con ex s.
Ag icul u al egions wi h dis inc ma ke s uc u es, coope a i e adi ions, o policy
en i onmen s migh exhibi di e en pa e ns.
Ano he cons ain is he quali a i e na u e and subjec i i y o he da a. While he
in e iew-based app oach allows o ich and de ailed pe spec i es, i emains
subjec i e and may no always be ully ep esen a i e o b oade ends.
Addi ionally, he s udy explo es po en ial mode a ing e ec s—such as Technology
Familia i y, Expe ience, and Geog aphical Region—bu due o he small sample
size, s a is ical alida ion was no easible. As a esul , hese mode a ing e ec s
emain quali a i e obse a ions a he han s a is ically con i med in luences.
Mo eo e , i is impo an o acknowledge he p e-exis ing limi a ions o TPB and
UTAUT, which ha e been ex ensi ely documen ed in p io s udies.
Beyond me hodological cons ain s, ex e nal economic and policy changes could
signi ican ly al e he adop ion landscape obse ed in his esea ch. Ma ke
luc ua ions, egula o y shi s, o changes in subsidy p og ams may in luence small
6| Resul s and Discussion
107
a me s’ willingness o adop new echnologies o pa icipa e in sha ed business
models, po en ially ede ining he ele ance o some ba ie s and d i e s iden i ied
in his s udy.
Finally, ano he limi a ion is he s udy’s ocus on machine y-sha ing and
coope a i es, which cons ained he explo a ion o o he sha ed business models.
O he eme ging models, such as land sha ing, knowledge-sha ing ne wo ks o
communi y-suppo ed ag icul u e (CSA), we e no analysed in de ail. In es iga ing a
b oade ange o sha ed models could p o ide addi ional insigh s in o hei
easibili y, challenges, and po en ial bene i s o small a me s.
109
7 Conclusions and Recommenda ions
This s udy se ou o explo e he d i e s and ba ie s in luencing small a me s’
decisions o adop new echnologies and engage wi h Sha ed Business Models. By
applying he UTAUT amewo k o echnology adop ion and he TPB o
unde s anding collec i e beha iou s in on o sha ed models, i has been
demons a ed ha indi idual ac o s (such as P ice Value and Pe o mance
Expec ancy), ex e nal elemen s (like En i onmen al Unce ain y) and he ac ion o
Mode a o s in e sec o shape he easibili y and sus ained use o a echnology, in
independen and collabo a i e app oaches o ag icul u al inno a ion.
In pa icula , he hesis indings con i m ha P ice Value— he pe cei ed cos -bene i
a io—exe s a s ong in luence on Beha iou al In en ion and Sus ained Adop ion.
Meanwhile, E o Expec ancy, al hough adi ionally linked only o In en ion,
appea ed o ha e a di ec albei mino e ec on Use Beha iou in ce ain cases. The
ole o mode a o s such as Technology Familia i y, Expe ience, Geog aphical
Region, and Volun a iness o Use also eme ged as con ex -dependen ac o s ha can
magni y o dampen adop ion dynamics.
The con i ma ion o he in luence o Pe o mance Expec ancy, Social In luence, and
Facili a ing Condi ions on Beha iou al In en ion—along wi h he la e ’s impac on
Use Beha iou —is also among he conclusions. Addi ionally, he ex e nal e ec o
En i onmen al Unce ain y is concluded o shape adop ion- ela ed e alua ions in a
mo e sub le and indi ec manne .
F om he pe spec i e o sha ed business models, Subjec i e No ms p o ed especially
c i ical and nega i e: e en when a me s see po en ial bene i s. Simul aneously,
indi idual A i ude and Pe cei ed Con ol we e o en posi i e, sugges ing ha
collabo a ion may be concep ually accep ed, bu hinde ed by a lis o ba ie s
con as ing wi h he ad an ages o he d i e s lis .
The p ac ical ecommenda ions a e add essed o:
• Policymake s and public ins i u ions: De elop a ge ed subsidies and g an s
o encou age he adop ion o sha ed models, s eamline bu eauc a ic
p ocedu es, and in es in in as uc u e.
• Ag icul u al Coope a i es and Associa ions: Fo malize go e nance s uc u es
and p o ide clea solu ions on he common con lic s, implemen lexible

110
7| Conclusions and Recommenda ions
scheduling sys ems ailo ed o sec o needs, and educa e s akeholde s on he
bene i s o sha ed business models.
• Technology P o ide s: O e cos -e ec i e solu ions, including sha ed models
ha accommoda e small a me s’ limi ed capi al, p o ide local echnical
suppo , and o e aining p og ams o ensu e a me s can ully u ilize he
echnology.
• Fa me s and Fa me G oups: Begin wi h in o mal exchanges o build us
g adually be o e commi ing o a sha ed business model, p ac ice open
communica ion and anspa ency o p e en dispu es, and le e age collec i e
ba gaining powe .
• O he In e es ed S akeholde s: Use his esea ch as a esou ce o gene a e
posi i e socie al impac by os e ing knowledge and collabo a ion in he
ag icul u al sec o .
Se e al a enues o u u e esea ch emain:
• Longi udinal S udies: T acking sus ained adop ion o e mul iple seasons
could cla i y how economic luc ua ions o policy changes in luence he
con inued use o sha ed echnology.
• Compa a i e S udies Ac oss Regions: Expanding he sample o include
addi ional geog aphical a eas would p o ide insigh s in o egional mode a ing
e ec s.
• Al e na i e Sha ed Models: Fu u e in es iga ions could explo e o he sha ed
models—such as knowledge-sha ing ne wo ks, communi y-suppo ed
ag icul u e, o pee - o-pee ma ke places— o de e mine whe he simila
bene i s and ba ie s apply.
• Non-Adop e s: A deepe unde s anding o a me s who comple ely ejec bo h
echnology and sha ed models would help iden i y absolu e ba ie s o
inno a ion, in o ming policy in e en ions aimed a he mos esis an
segmen s.
The inal e lexion o his hesis is ha he global push o sus ainable and esilien
ag icul u e will likely con inue. Placing small a me s a he o e on o inno a ion
and pa ne ship oppo uni ies is complica ed bu mus be done. By ecognizing he
mul i ace ed ba ie s and d i e s documen ed in his s udy, s akeholde s can ailo
policies, echnologies and coope a i e s uc u es o be e add ess small a me s’
economic eali ies and cul u al p e e ences, he eby ensu ing hey emain
compe i i e, sus ainable, and economically iable in he long e m.
111
Bibliog aphy
Aji Ni anjan. 2024. ‘“We Didn’ Realise How Ha d I Is”: Small Fa me s in Eu ope
S uggle o Ge By’. The Gua dian, 3 No embe 2024.
h ps://www. hegua dian.com/en i onmen /2024/no /03/we-didn - ealise-
how-ha d-i -is-small- a me s-in-eu ope-s uggle- o-ge -by.
Ajzen, Icek. 1991. ‘The Theo y o Planned Beha io ’. O ganiza ional Beha io and Human
Decision P ocesses 50 (2): 179–211.
Al Fagih, Khaled. 2022. ‘INVESTIGATING THE ADOPTION OF AN INNOVATION
USING AN EXTENDED UTAUT MODEL: THE CASE OF MOBILE
LEARNING TECHNOLOGY’. Jou nal o Theo e ical and Applied In o ma ion
Technology 100 (July).
Al-Gah ani, Said, Geo ey Hubona, and Jijie Wang. 2007. ‘In o ma ion Technology
(IT) in Saudi A abia: Cul u e and he Accep ance and Use o IT’. In o ma ion &
Managemen 44 (Decembe ):681–91. h ps://doi.o g/10.1016/j.im.2007.09.002.
Alpaslan Başa ık, Saade in Yıldı ım. 2015. ‘A Case S udy o Sha ing Fa m Machine y
in Tu key’. 2015, 2015.
h ps://www. esea chga e.ne /publica ion/334364284_A_Case_S udy_o _Sha i
ng_Fa m_Machine y_in_Tu key.
An wi-Boampong, Ahmed, Boison King Da id, Blay Augus ine, Dominic Ko i Louis,
and Ebeneze Malcalm. 2024. ‘Explo ing Beha io al D i e s Behind Fa me s’
Willingness o Emb ace he In e ne o Things’. In e na ional Jou nal o Religion 5
(11): 7518–35. h ps://doi.o g/10.61707/m9ajdd79.
A z, Geo geanne M., G ego y Colson, and Roge G. Ginde . 2010. ‘A Re u n o he
Th eshing Ring? A Case S udy o Machine y and Labo -Sha ing in Midwes e n
Fa ms’. h ps://doi.o g/10.22004/AG.ECON.100515.
A z, Geo geanne, and Linda Nae e. 2016. ‘The Bene i s and Challenges o Machine y
Sha ing Among Small-Scale F ui and Vege able G owe s’. Jou nal o
Ag icul u e, Food Sys ems, and Communi y De elopmen , 1–17.
h ps://doi.o g/10.5304/ja scd.2016.063.003.
B enne , Ba ba a, and Da ia D dla. 2023. ‘Business Model Inno a ion owa d
Sus ainabili y and Ci cula i y—A Sys ema ic Re iew o Inno a ion Types’.
Sus ainabili y 15 (15): 11625. h ps://doi.o g/10.3390/su151511625.
112
| Bibliog aphy
Cimino, An onio, Ilda Ma ia Coniglio, Vincenzo Co ello, F ancesco Longo, Juliana
Keiko Sagawa, and Vi o io Solina. 2024. ‘Explo ing Small Fa me s Beha io al
In en ion o Adop Digi al Pla o ms o Sus ainable and Success ul
Ag icul u al Ecosys ems’. Technological Fo ecas ing and Social Change 204
(July):123436. h ps://doi.o g/10.1016/j. ech o e.2024.123436.
Colapin o, Cinzia, Raja Jaya aman, Fouad Ben Abdelaziz, and Da ide La To e. 2020.
‘En i onmen al Sus ainabili y and Mul i ace ed De elopmen : Mul i-C i e ia
Decision Models wi h Applica ions’. Annals o Ope a ions Resea ch 293 (2): 405–
32. h ps://doi.o g/10.1007/s10479-019-03403-y.
Cu is, S e en Kane, and Ma hias Lehne . 2019. ‘De ining he Sha ing Economy o
Sus ainabili y’. Sus ainabili y 11 (3): 567. h ps://doi.o g/10.3390/su11030567.
Cu is, S e en Kane, and Oksana Mon . 2020. ‘Sha ing Economy Business Models o
Sus ainabili y’. Jou nal o Cleane P oduc ion 266 (Sep embe ):121519.
h ps://doi.o g/10.1016/j.jclep o.2020.121519.
Da is, F ed D. 1989. ‘Pe cei ed Use ulness, Pe cei ed Ease o Use, and Use
Accep ance o In o ma ion Technology’. MIS Qua e ly, 319–40.
De To o, A., and P.-A. Hansson. 2004. ‘Machine y Co-Ope a i es—a Case S udy in
Sweden’. Biosys ems Enginee ing 87 (1): 13–25.
h ps://doi.o g/10.1016/j.biosys emseng.2003.10.010.
Dhillon, Raj ee , and Qianna Moncu . 2023. ‘Small-Scale Fa ming: A Re iew o
Challenges and Po en ial Oppo uni ies O e ed by Technological
Ad ancemen s’. Sus ainabili y 15 (21): 15478.
h ps://doi.o g/10.3390/su152115478.
Donne , Mech hild, and Hugo De V ies. 2023. ‘Business Models o Sus ainable Food
Sys ems: A Typology Based on a Li e a u e Re iew’. F on ie s in Sus ainable Food
Sys ems 7 (May):1160097. h ps://doi.o g/10.3389/ su s.2023.1160097.
Eckha d , And eas, S en Laume , and Tim Wei zel. 2009. ‘Who In luences Whom?
Analyzing Wo kplace Re e en s’ Social In luence on I Adop ion and Non-
Adop ion’. JIT 24 (Feb ua y):11–24. h ps://doi.o g/10.1057/ji .2008.31.
En i onmen al Wo king G oup. 2024. ‘USDA Census: Smalle Fa ms Falling Fu he
Behind’. 13 Feb ua y 2024. h ps://www.ewg.o g/news-insigh s/news-
elease/2024/02/usda-census-smalle - a ms- alling- u he -behind.
Fadeyi, Oluwamayokun Anjo in, Anoma A iyawa dana, and Amma Abdul Aziz.
2022. ‘Fac o s In luencing Technology Adop ion among Smallholde Fa me s:
A Sys ema ic Re iew in A ica’, Janua y. h ps://doi.o g/10.17170/KOBRA-
202201195569.
Fa idi, Ami Ali, Mohammad Ka oosi-Kalashami, and Hamid El Bilali. 2020. ‘A i ude
Componen s A ec ing Adop ion o Soil and Wa e Conse a ion Measu es by
| Bibliog aphy
113
Paddy Fa me s in Rash Coun y, No he n I an’. Land Use Policy 99
(Decembe ):104885. h ps://doi.o g/10.1016/j.landusepol.2020.104885.
Gab iel, And eas, and Ma kus Gando e . 2023. ‘Adop ion o Digi al Technologies in
Ag icul u e—an In en o y in a Eu opean Small-Scale Fa ming Region’.
P ecision Ag icul u e 24 (1): 68–91. h ps://doi.o g/10.1007/s11119-022-09931-1.
Gholian-Jouyba i, Fa emeh, Mos a a Hajiaghaei-Kesh eli, A ya Ba a , A es a Ba a ,
and Behzad Mosallanezhad. 2023. ‘A Design o a Ci cula Closed-Loop Ag i-
Food Supply Chain Ne wo k—A Case S udy o he Soybean Indus y’. Jou nal
o Indus ial In o ma ion In eg a ion 36 (Decembe ):100530.
h ps://doi.o g/10.1016/j.jii.2023.100530.
Guyade , Hugo, and Lau a Piscicelli. 2019. ‘Business Model Di e si ica ion in he
Sha ing Economy: The Case o GoMo e’. Jou nal o Cleane P oduc ion 215
(Ap il):1059–69. h ps://doi.o g/10.1016/j.jclep o.2019.01.114.
Handoko Pu a, Ye y, Lia Wa lina, Dina Fa imah, Wan o o, and Sel a Sep iani Aulia.
2023. ‘Adop ion o he Ag icul u e Applica ion by Fa me s Using he UTAUT2
Me hod Focused on Communi y Beha io and Use Expe ience’. In e na ional
Jou nal O Compu e Sciences and Ma hema ics Enginee ing 2 (2): 52–65.
h ps://doi.o g/10.61306/ijecom. 2i2.22.
Ha is, And ea, and Mu ay E. Ful on. 2000. Fa m Machine y Co-Ope a i es: An Idea
Wo h Sha ing. Saska oon: Cen e o he S udy o Co-ope a i es, Uni e si y o
Saska chewan.
Hong, Insu, and Changsok Yoo. 2020. ‘Analyzing Spa ial Va iance o Ai bnb P icing
De e minan s Using Mul iscale GWR App oach’. Sus ainabili y 12 (11): 4710.
h ps://doi.o g/10.3390/su12114710.
Housman, Je . 2003. ‘Unde s anding Female Collegia e A hle es’ In en ions o
Consume Spo s Supplemen s: An Applica ion o he Theo y o Planned
Beha io ’, May.
Im, Il, Seong ae Hong, and Myung Soo Kang. 2011. ‘An In e na ional Compa ison o
Technology Adop ion Tes ing he UTAUT Model’. In o ma ion & Managemen
48 (Janua y):1–8. h ps://doi.o g/10.1016/j.im.2010.09.001.
Knaude , Hannelo e, and Co inna Koschmiede . 2018. ‘Indi idualized S uden
Suppo in P ima y School Teaching: A Re iew o In luencing Fac o s Using he
Theo y o Planned Beha io (TPB)’. Teaching and Teache Educa ion 77
(Oc obe ):66–76. h ps://doi.o g/10.1016/j. a e.2018.09.012.
Lan, Jing, Yuge Ma, Dajian Zhu, Diana Mangalagiu, and Thomas Tho n on. 2017.
‘Enabling Value Co-C ea ion in he Sha ing Economy: The Case o Mobike’.
Sus ainabili y 9 (9): 1504. h ps://doi.o g/10.3390/su9091504.
120
A| Appendix: In e iews empla e
Sec ion 2: Technology adop ion
- Which Equipmen , So wa e o Any da a acking, senso s, analysis sys em ha e you
acqui ed?
- Which Equipmen , So wa e o Any da a acking, senso s, analysis sys em ha e you
conside ed bu a he end no acqui ed?
- 2 S udy case (1s an acqui ed, 2nd a no acqui ed):
1. Volun a iness o use
• Yes o no
2. Pe o mance expec a ions
• Wha did you expec om i , in e ms o pe o mance? (Quicke , p oduc i i y,
less issues…)
• Has i achie ed i ? Any c i ics?
3. E o expec a ions
• Wha did you expec om i , in e ms o e o o lea n? (Unde s andable o
in ui i e, ease o become skil ully, simple o use…)
• Has i achie ed i ? Any c i ics?
4. In luence
• Has someone in luenced you? (Neighbou s, impo an people, amous,
es imonials ega ding he bene i s o a ne wo k o a me s…)
• To wha ex en do hei opinions in luence you decision when you adop ed
his echnology? Do you eg e us ing on hem?
5. Facili a ing condi ions
• Did you eel eady o use he echnology? (O he esou ces, knowledge,
compa ibili y, assis ance, aining)?
• Any c i ics ega ding his? Wha you mos o less like o i ?
6. P ice Value
• Did you hink he bene i s o his echnology jus i ied i s cos be o e buying i ?
• Has i achie ed i ? Any c i ics?
7. Hedonic mo i a ion
• We e you expec ing i o be enjoyable, unny o use?

A| Appendix: In e iews empla e
121
• Has i been? Has i been a p oblem?
8. Habi
• Did you hink his adop ion would adap well in you a ming ou ine o
dis up i ?
• Has i been a p oblem o no ?
9. En i onmen al Unce ain y
• I imagine you ag ee wi h he ac ha a ming is a e y e ol ing sec o and
e e yday he e a e new ex e nal clima ic, ma ke , and policy changes. Has he
echnology helped you manage hese unce ain ies?
• Has i been a p oblem o no ?
- Abou he 9 men ioned which we e he impo an ones in he inal decision?
- Do you see you sel s ill using his echnology in he long e m? Which o he 9
men ioned i ems would be he one causing o s op using i ?
122
A| Appendix: In e iews empla e
Sec ion 3: Focused on a Sha ed Model
He e I ha e lis ed some p oblems ha a me s can ind in hei job.
I need you o say YES o NO in each. Say yes i i a ec s, conce ns you o i you would
like o imp o e i .
No e: Now explain how a Sha ed model would imp o e he poin s he men ioned and o he
bene i s… ead he easons om he ollowing box:
- Cos P oblems
o In es men
o Ope a ional cos s (main enance, consumables…)
o access o inancing
- Time P oblems (Manual p ocesses slow down ope a ions)
- P oblem o access Ad anced Technology / Specialized Technology
o A o dable inno a ions (D ones, P ecision Fa ming Tools)
o Newe e sions
- High isks (Machine y b eak)
- Low scalabili y
- Ine iciency u iliza ion o esou ces (Tech no used equen ly)
- No eaching Sus ainabili y and En i onmen al goals
o Less machines
o Be e one
- Low ma ke powe
o Be e p oduc
o New ne wo k
o Mee ce i ica ion s anda ds
- Low lexibili y
o Rigid business s uc u e
- Low Communi y bonds
A| Appendix: In e iews empla e
123
No e: ex a explana ions o make him unde s and he wo king i he he a me is no amilia
wi h Sha ed business models
A sha ed business model is a sys em whe e mul iple a me s collabo a e o sha e esou ces,
educe cos s, and inc ease e iciency a he han ope a ing indi idually. Ins ead o each a me
- Cos P oblems
o In es men → Fa me s co-in es in machine y and in as uc u e,
educing indi idual inancial bu dens
o Ope a ional cos s → Sha ed owne ship sp eads main enance and
unning cos s among mul iple use s.
o access o inancing→ G oup unding applica ions inc ease
eligibili y o loans, g an s, and subsidies
- Time P oblems (Manual p ocesses slow down ope a ions) → Access o
mo e e icien ools
- P oblem o access Ad anced Technology / Specialized Technology
o A o dable inno a ions (D ones, P ecision Fa ming Tools) →
Fa me s sha e expensi e ools, making ad anced echnology
accessible.
o Newe e sions → Ins ead o buying ou da ed models, a me s
upg ade h ough sha ed access o he la es echnology
- High isks (Machine y b eak) → Sha ed equipmen migh ensu e backup
op ions
- Low scalabili y → Access o be e ools ha allows scaling up wi hou
equi ing majo pe sonal in es men s
- Ine iciency u iliza ion o esou ces (Tech no used equen ly)
- No eaching Sus ainabili y and En i onmen al goals
o Less machines → Fa me s educe edundan equipmen
o Be e one→ Sha ed in es men allows access o high-quali y,
- Low ma ke powe
o Be e p oduc → access machines ha imp o e quali y o you p oduc
o New ne wo k
o Mee ce i ica ion s anda ds→ access o ce i ica es ha imp o e
quali y
- Low lexibili y
o Rigid business s uc u e→ Sha ed business models allow adap i e
s a egies
- Low Communi y bonds→ s eng hening us , knowledge exchange
124
A| Appendix: In e iews empla e
owning all hei machine y, echnology, land, o wo k o ce, hey co-own, en , o access se ices
collec i ely o imp o e p oduc i i y and ma ke oppo uni ies.
Examples would be Equipmen and Resou ce Sha ing, Coope a i es, Land and In as uc u e
Sha ing, sha ing seasonal wo ke s o hi ing specialized s a oge he ,
✔
Lowe cos s – Reduces in es men in expensi e equipmen and in as uc u e.
✔
Inc eased e iciency – Maximizes he use o ools, land, and labou .
✔
Imp o ed ma ke access – S eng hens a me s’ ba gaining powe and access o
ce i ica ions.
✔
G ea e lexibili y – Enables a me s o scale p oduc ion up o down based on demand.
✔
Sus ainabili y – Reduces en i onmen al impac by minimizing edundan machines and
op imizing esou ces.
A| Appendix: In e iews empla e
125
TPB Focused ques ions
1. A i ude Towa d he Beha iou
• Wha is you o e all opinion abou using his business model?
• Do you hink i would b ing bene i s o challenges o you a m ope a ions?
2. Subjec i e No ms
• How do you hink o he a me s, amily membe s, o ad iso s would eel
abou you using his business model?
• Would hei opinions in luence you decision?
3. Pe cei ed Beha iou al Con ol
• How con iden a e you in you abili y o implemen and sus ain his business
model, conside ing you cu en esou ces, knowledge, and ime?
• Do you belie e you can o e come he ba ie s you ace o adop his business
model?
4. Beha iou al In en ion
• How likely a e you o conside implemen ing his business model on you
a m soon? Wha would mo i a e you o ake ha s ep, o wha conce ns
migh hold you back?
Addi ional ques ions o add ess gaps
1. Com o wi h Sha ing
• How many a me s would you eel com o able sha ing equipmen o
esou ces wi h? Would you p e e a small, us ed g oup o a la ge ne wo k?
• Would you p e e sha ing only wi h a me s you al eady know, o would you
be open o wo king wi h new collabo a o s?
2. Paymen P e e ences and Owne ship
• Which paymen model would you ind mos sui able o pa icipa ing in a
sha ed business model? Fixed Membe ship Fee, Pay-Pe -Use, Hyb id
Model…?
• Would you p e e o co-own he machine wi h o he a me s o access he
machine as a se ice p o ided by an ex e nal company o coope a i e?
3. Scheduling and Use Coo dina ion

126
A| Appendix: In e iews empla e
• How do you hink access o sha ed equipmen should be scheduled? Should
he e be a booking sys em, a p io i y lis , o a o a ion?
• How lexible would you be wi h adjus ing you wo k schedule based on he
a ailabili y o sha ed equipmen ?
4. Main enance P e e ences
• Who do you hink should be esponsible o main aining sha ed equipmen ?
Should i be a sha ed esponsibili y, a o a ing du y, o managed by an
ex e nal se ice?
5. T us and Con lic Resolu ion
• Wha conce ns would you ha e abou us ed o he s o use sha ed equipmen
esponsibly?
• I disag eemen s a ise o e scheduling, paymen , o main enance, how do you
hink hey should be esol ed?
6. Da a and Technology Use
• Would you be com o able using a digi al pla o m o manage scheduling,
paymen s, and equipmen use?
• Would you be willing o sha e da a (e.g., usage pa e ns, main enance needs)
o imp o e he e iciency o a sha ed sys em?
7. Long-Te m Commi men and Exi S a egies
• Would you p e e a sho - e m ial pe iod be o e commi ing o a sha ed
model?
• I you decide o lea e he sha ed model, wha exi op ions would you ind ai
(selling you sha e, ans e ing igh s o ano he a me , paying a p e ious
acco ded ee…)?
127
Lis o Figu es
Figu e 2.1: Illus a ion o he Technology Accep ance Model (TAM). Sou ce: (Mille
and Khe a 2010) ..................................................................................................................... 16
Figu e 2.2: Illus a ion o he UTAUT. Sou ce: (Ome e al. 2015) ................................. 18
Figu e 2.3: Types o UTAUT Ex ensions. Sou ce: (Venka esh, Thong, and Xu 2016) . 19
Figu e 2.4: Illus a ion o he TPB. Sou ce: (Knaude and Koschmiede 2018) ............ 21
Figu e 2.5: De ailed Illus a ion o TPB. Sou ce: (Housman 2003) ................................. 22
Figu e 4.1: UTAUT Templa e o Analysis. Sou ce: Own elabo a ion .......................... 47
Figu e 4.2: TPB empla e o Analysis. Sou ce: (Knaude and Koschmiede 2018) ..... 47
Figu e 6.1: Gab i’s indi idual UTAUT amewo k. Sou ce: Own elabo a ion ............ 68
Figu e 6.2: Gab i’s indi idual TPB amewo k. Sou ce: Own elabo a ion ................... 69
Figu e 6.3: Jo di’s indi idual UTAUT amewo k. Sou ce: Own elabo a ion .............. 72
Figu e 6.4: Jo di’s indi idual TPB amewo k. Sou ce: Own elabo a ion..................... 74
Figu e 6.5: Be a’s indi idual UTAUT amewo k. Sou ce: Own elabo a ion ............. 77
Figu e 6.6: Be a’s indi idual TPB amewo k. Sou ce: Own elabo a ion .................... 78
Figu e 6.7: Sancho’s indi idual UTAUT amewo k. Sou ce: Own elabo a ion .......... 82
Figu e 6.8: Jo di’s indi idual TPB amewo k. Sou ce: Own elabo a ion..................... 83
Figu e 6.9: Joan’s indi idual UTAUT amewo k. Sou ce: Own elabo a ion .............. 86
Figu e 6.10: Joan’s indi idual TPB amewo k. Sou ce: Own elabo a ion ................... 87
Figu e 6.11: Gene alized UTAUT amewo k o small a me ’s Technology Adop ion.
Sou ce: Own elabo a ion ...................................................................................................... 94
Figu e 6.12: Gene alized TPB amewo k o small a me ’s engagemen in a Sha ed
Business Model. Sou ce: Own elabo a ion ........................................................................ 94
129
Lis o Tables
Table 2.1: UTAUT’s concep s and de ini ions. Sou ce: (Papagiannidis 2022) .............. 19
Table 4.1: Addi ional UTAUT concep s and hei de ini ions. Sou ce: Own elabo a ion
.................................................................................................................................................. 46
Table 6.1: Cons uc connec ions and a ing. Sou ce: Own elabo a ion ........................ 91
Table 6.2. Mode a o G ouping o UTAUT. Sou ce: Own elabo a ion ........................ 92
Table 6.3: D i e s iden i ied as key mo i a ions o small a me s o adop echnology
h ough a sha ed business model. Sou ce: Own elabo a ion ......................................... 97
Table 6.4: Ba ie s iden i ied as key challenges wi hin sha ed business models o small
a me s. Sou ce: Own elabo a ion. ..................................................................................... 99
Table 6.5: Ba ie s iden i ied as key challenges ou side sha ed business models o
small a me s. Sou ce: Own elabo a ion. ......................................................................... 101