Resea ch epo
Employees’ a i udes owa ds
wel a e echnology in subs ance
abuse ea men in Finland
Teemu Ran anen
Lau ea Uni e si y o Applied Sciences, Van aa, Finland
Timo Toikko
Uni e si y o Tampe e, Facul y o Social Sciences, Po i, Finland
Abs ac
Aim: The signi icance o wel a e and heal h echnology has been highligh ed in ecen yea s.
Howe e , employees’ a i udes owa ds wel a e echnology in subs ance abuse ea men ha e
ecei ed li le a en ion. This a icle examines employees’ eadiness o in oduce wel a e ech-
nology in subs ance abuse ea men and hei a i udes owa ds i s use. Design: The heo e ical
amewo k o his s udy is based on Ajzen’s (1991, 2001) heo y o planned beha iou , and he
ongoing discussion abou he adop ion o new echnology in heal hca e. The esea ch da a (N¼
129) we e collec ed in he o m o an elec onic ques ionnai e in Finland in 2015. Resul s: The
esul s a e consis en wi h he heo y o planned beha iou and p e ious s udies on he accep ance
o in o ma ion sys ems in heal hca e. Employees’ eadiness o in oduce new wel a e echnology
applica ions and de ices in subs ance abuse ea men is in luenced by hei pe sonal app ecia ion
o wel a e echnology, he expec a ions o hei colleagues and supe iso s, as well as hei own
pe cep ions o hei capaci y o lea n o use he applica ions. Conclusions: The s udy ound some
links be ween demog aphic ac o s and cogni ions ela ed o wel a e echnology. In pa icula ,
employees wi h a heal hca e backg ound a e mo e inclined o adop he echnology han o he
employees in subs ance abuse ea men . In addi ion, a pe son’s age has a nega i e ela ionship
wi h hei pe cei ed echnology managemen . Howe e , age has no signi ican connec ion wi h
a i udes and no di ec independen e ec on he eadiness o in oduce a new wel a e echnology.
Las ly, he esul s show ha p e ious posi i e expe iences o wel a e echnology make i easie o
in oduce new echnologies.
Submi ed: 21 Decembe 2016; accep ed: 9 Janua y 2017
Co esponding au ho :
Teemu Ran anen, Lau ea-amma iko keakoulu, Ra a ie 22, 013 00 Van aa, Finland.
Email: [email p o ec ed]
No dic S udies on Alcohol and D ugs
2017, Vol. 34(2) 131–144
ªThe Au ho (s) 2017
Rep in s and pe mission:
sagepub.co.uk/jou nalsPe missions.na
DOI: 10.1177/1455072517691060
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wo k is a ibu ed as speci ied on he SAGE and Open Access pages (h ps://us.sagepub.com/en-us/nam/open-access-a -sage).
Keywo ds
a i udes, compu e -based in e en ions, echnology-based he apies, heo y o planned beha-
iou , wel a e echnology
The impo ance o wel a e echnology has
g ea ly inc eased in ecen yea s. The eason o
his is based no only on echnological
ad ances, bu also on he educed a ailabili y
o public inances in many Wes e n coun ies
and he consequen need o inc ease p oduc i -
i y in social ca e and heal hca e. The in oduc-
ion o echnology has been shown o imp o e
pa ien sa e y, as well as quali y, accessibili y
and he e iciency o ca e (e.g., Fe e , 2009).
Technology aimed o help olde people o cope
a home is also being de eloped o mee he
challenges o an aging popula ion. Howe e ,
he use o wel a e echnology in subs ance
abuse ea men has been limi ed.
The concep o wel a e echnology is ex en-
si e. Acco ding o Ho mann (2013), i is a gen-
e ic e m o a he e ogeneous g oup o
echnologies. Also, in he con ex o subs ance
abuse ea men , wel a e echnology can en ail
qui e a numbe o applica ions and de ices.
Many o he heal h echnology applica ions
such as d ug deli e y au oma ion, o he medi-
ca ion echnologies, and subs ance abuse es -
ing echnologies a e use ul in he p o ision o
subs ance abuse ea men . A a ie y o secu i y
echnologies can also be u ilised, along wi h,
o example, elec onic pa ien in o ma ion sys-
ems and e-p esc ip ion se ices. F om he pe -
spec i e o subs ance abuse, he signi icance o
e-communica ion is pa icula ly no able, and
he e a e a wide a ie y o ways o ake
ad an age o digi al heal h echnologies in he
sphe e o alcohol ea men (Muench, 2014). A
ange o elec onic applica ions ha e been
de eloped o subs ance abuse p e en ion, such
as open websi es ha p o ide in o ma ion on
d ugs, web-based es s and mobile apps o he
sel -moni o ing o alcohol consump ion. Web-
based cha ooms o pee suppo o subs ance
abuse s a e also one example o he new
in o ma ion echnology which is a ailable. In
con as , applica ions which suppo subs ance
abuse ehabili a ion ha e seen only limi ed use,
al hough, o example, a ne wo k-suppo ed
subs ance abuse ehabili a ion p og amme has
been de eloped in Finland based on a cogni i e
beha iou al amewo k (A-Clinic Founda ion,
2016). Some ehabili a i e games ha e been
de eloped o addic s, e.g., he kine ic Take
Con ol game by Clinical Tools Inc., which is
based on cue exposu e he apy (CET). The
basic idea o he echnique is he epea ed and
con olled exposu e o pic u es o a subs ance.
In his case, he pe son p ac ices hei coping
esponses wi h he aim o a oiding a elapse,
and by using a i ual eali y (VR) en i onmen
i is possible o expose a pe son o s imuli in a
sa e manne (Chambe s, Bielin, & O’Laughlin,
2013).
Acco ding o Moo e, Fazzino, Ga ne , Cu -
e , and Ba y (2011), compu e -based in e en-
ions o d ug use diso de s show ini ial
e idence o e icacy du ing ea men , and
some e idence ha e ec s con inue a e ea -
men . In addi ion, compu e -based in e en-
ions we e associa ed wi h high le els o
clien sa is ac ion. Howe e , esea ch me hods,
samples and in e en ion ypes ha e been he -
e ogeneous and only a ew andomised con-
olled s udies ha e been conduc ed (Moo e
e al., 2011). Acco ding o Newman, Szkodny,
Lle a, and P zewo ski (2011), echnology-
based sel -help and minimal con ac he apies
a e e ec i e and low-cos in e en ions o
anxie y and mood diso de s. Dennis and
O’Toole (2014) ha e s udied he e ec i eness
o ehabili a i e mobile apps among ai -
anxious adul s, and McTa ish, Chih, Shah, and
Gus a son (2012) ha e s udied he A-CHESS
sys em (Alcohol Comp ehensi e Heal h
Enhancemen Suppo Sys em), which is a
132 No dic S udies on Alcohol and D ugs 34(2)
sma phone-based sys em o p e en ing a
elapse o hea y d inking among people who a e
in he p ocess o lea ing ac i e alcohol depen-
dence ea men . Vi ual eali y he apy has been
s udied qui e a lo , bu mainly in he con ex o
he he apeu ic e icacy o VR ela ed o popula-
ions wi h a diagnosis o nico ine dependence
(Hone-Blanche , Wensing, & Fec eau, 2014).
Vi ual eali y has been applied less o subs ance
abuse ea men ; howe e , Lee e al. (2009) ha e
s udied he e ec i eness o VR he apy among
alcohol-dependen pa ien s. Acco ding o hem,
VR he apy may be use ul as an adjunc o ea -
ing alcohol dependence, and may also se e as
an e alua ion ool o iden i y high- isk pa ien s.
The in oduc ion o wel a e echnology
changes he wo k pa e n in social ca e and
heal hca e in many ways. Especially, i s use
in ol es many po en ial isks, and a numbe
o challenges ega ding how o success ully
in eg a e digi al heal h echnologies in o ea -
men ha e been iden i ied (Muench, 2014).
Challenges a e ela ed o echnology and com-
pe encies, and also o issues o leade ship and
managemen , educa ion, communica ion and
collabo a ion, in o ma ics design, and cul u e
and policy (Fe e , 2009). Employees’ a i udes
and hei unce ain y o hei own echnological
skills a e he key ac o s ha impede he in o-
duc ion o new echnology. S udies ha e shown
ha he use o in o ma ion echnology and a i-
udes owa ds in o ma ion echnology depend
on a pe son’s age, educa ion and income (e.g.,
Po e & Don hu, 2006). Social and heal h ca e
p o essionals’ a i udes owa ds echnology
ha e also been shown o depend on, in e alia,
gende , na ionali y and ope a ional expe iences
(Alqu aini, Alhashem, Shah, & Chowdhu y,
2007). In addi ion, i has been ound ha wo k-
e s ha e a ea ha he use o echnology dehu-
manises he p ocess o ca e (Hu yk, 2010).
This a icle ocuses on Finnish subs ance
abuse wo ke ’s a i udes owa ds wel a e ech-
nology, and add esses how he e ec o demo-
g aphic and cogni i e ac o s on he eadiness
o use wel a e echnological applica ions
becomes isible among Finnish social wo ke s,
nu ses and o he employees in subs ance abuse
ea men uni s. The heo e ical amewo k o
his s udy is based on Ajzen’s (1991, 2001)
heo y o planned beha iou . The adop ion o
echnology has been ex ensi ely s udied in he
heal hca e se ing (Adams, 2006; Holden &
Ka sh, 2010), bu less so in alcohol and d ug
esea ch (e.g., Bu i e al., 2013).
Concep s and heo e ical
pe spec i es
A i ude is a concep comp ising many aspec s
and does no ha e a single consis en de ini ion.
Typically, i is de ined as a psychological en-
dency which is exp essed by e alua ing a pa -
icula en i y wi hin some dimension (Eagly &
Chaiken, 1993). T adi ionally, a i ude esea ch
has examined gene al a i udes, bu Ajzen and
Fishbein (2000, pp. 16–17) a gue ha speci ic
a i udes explain and p edic beha iou in spe-
ci ic si ua ions much be e han gene al a i-
udes. Hence, i is necessa y o examine
a i udes owa ds speci ic wel a e echnology
applica ions o hei in oduc ion, ins ead o
only gene al a i udes owa ds echnology.
Wel a e echnology a i udes ha e been con-
cep ualised in di e en ways. Fo example,
Bose and Daughe y (1998) examine i e com-
ponen s o a i udes: a gene al in e es in ech-
nology, gene al a i ude owa d echnology,
echnology as an ac i i y o bo h boys and
gi ls, consequences o echnology and he di -
icul y o echnology. Many s udies ha e
ocused echnology a i udes owa ds heal h-
ca e in o ma ion sys ems. On he o he hand,
e y speci ic a i ude scales ha e also been
de eloped, such as B oadben ’s (2012) a i ude
scale which is ela ed o a i udes owa ds
heal hca e obo s.
Acco ding o he echnology accep ance
model (TAM) (Da is, Bagozzi, & Wa shaw,
1989), a i udes owa ds echnology can be
explained by pe cei ed usabili y. The TAM is
based on Ajzen and Fishbein’s (1980) heo y o
easoned ac ion, bu la e Ajzen (1991) com-
ple ed he heo y and ended up wi h he heo y
Ran anen and Toikko 133
o planned beha iou (TPB). Many s udies
(e.g., Chau & Hu, 2001; Holden & Ka sh,
2010; Raws o ne, Jayasu iya, & Capu i, 2000)
ha e shown ha bo h models a e use ul o
explaining he adop ion o new echnology
applica ions in heal hca e.
The main concep s o his s udy a e a i udes
owa ds wel a e echnology and i s in oduc-
ion, as well as beha iou al in en ion.In he
heo y o planned beha iou (TPB: Ajzen,
1991), he e m “in en ion” e e s o a speci ic
ac ion-in en ion – in o he wo ds, an in en ion
o beha e in a ce ain way o o pe o m a ce -
ain ac . Howe e , in a i ude discussions, in en-
ions ha e been unde s ood in di e en ways
and a e ela ed no jus o he ac ual in en ion,
bu also o he desi abili y o he objec and how
likely he pe son belie es he a ainmen o he
objec o be (A mi age & Conne , 2001).
Looking a wel a e echnology, people’s beha-
iou undamen ally depends on he decisions
aken by an o ganisa ion. In many cases, an
employee may in oduce a echnological
applica ion only i he o ganisa ion decides
o ac i a e i . Simila ly, i an o ganisa ion
decides o in oduce a pa icula in o ma ion
sys em, hen he indi idual employee mus
s a o use i . In his s udy, he concep o
beha iou al in en ion e e s o a pe son’s spe-
ci ic desi e o eadiness o in oduce o use
some new echnological de ice o applica ion.
Raws o ne e al. (2000) ha e shown ha he
TPB can also be applied o cases whe e he use
o echnology is manda o y.
Acco ding o he TBP, beha iou al in en ions
a e in luenced no only by a i udes, bu also by
subjec i e no ms and pe cei ed beha iou al con-
ol (Ajzen, 1991) (c . Figu e 1). Pe cei ed
beha iou al con ol is conce ned wi h how well
people hink hey can cope wi h lea ning and
using new echnology. I is based on Bandu a’s
(1982) social lea ning heo y and on he concep
o pe cei ed sel -e icacy. As an example, p e-
ious s udies ha e shown ha compu e sel -
e icacy is an impo an explana o y ac o when
examining he use and lea ning o ca e echnol-
ogy among nu sing s uden s (Kuipe , 2010).
The concep o a subjec i e no m ep esen s
he belie o how closely people alue he desi -
abili y o a pa icula beha iou (Ajzen, 1991,
2001). In he con ex o heal h echnology, i
means pe cep ion o he impo ance o ele-
ance o o he s’ belie s abou a pe son’s use
o a sys em, and hus i can e e , e.g., o he
opinions o doc o s, colleagues, supe io s,
senio managemen o a hospi al, o he impo -
an people o subo dina es (Holden & Ka sh,
2010). The esul s ega ding he impo ance o
subjec i e no m in e ms o echnology adop-
ion ha e been con adic o y. In some s udies,
subjec i e no ms ha e been ound o ha e no
signi ican e ec on beha iou al in en ion (e.g.,
Chau & Hu, 2001; Da is e al., 1989), bu on he
o he hand, acco ding o he e iew by Holden
and Ka sh (2010), hal o he s udies (4/8) ha e
ound a signi ican dependence. Bu i e al.
(2013) ha e s udied in en ions o use a
compu e -assis ed in e en ion in subs ance
abuse ea men using he heo y o easoned
ac ion, and acco ding o hei esul s, pe cei ed
social no ms we e a signi ican con ibu o o
clinician in en ion o adop web-based in e en-
ions while a i ude was no .
Ques ions and hypo heses
This s udy analyses he a i udes and cogni ions
ela ed o new wel a e echnology among sub-
s ance abuse wo ke s in Finland. The s a ing
poin o he s udy is he ques ion o which
demog aphic and cogni i e ac o s explain an
employee’s eadiness o in oduce new echnol-
ogy applica ions o de ices. P e ious s udies
ha e shown ha a i udes owa ds echnology
pa icula ly depend on gende , age, educa ion
and ope a ing expe ience (e.g., Alqu aini e al.,
2007; Bose & Daughe y, 1998; Po e &
Don hu, 2006). In ela ion o hese demog aphic
ac o s, he ollowing hypo heses will be es ed:
H1: An employee’s eadiness o in oduce new
wel a e echnologies (i.e., beha iou al in en ion)
in subs ance abuse ea men depends on his/he
134 No dic S udies on Alcohol and D ugs 34(2)
gende , age, educa ion and p e ious expe iences
o wel a e echnology.
In pa icula , he s udy supposes ha you h,
male gende , high le el o educa ion, nu sing edu-
ca ion and posi i e expe iences o wel a e echnol-
ogy inc ease a pe son’s eadiness o in oduce new
wel a e echnologies, and co espondingly nega-
i e expe iences o wel a e echnologies will
educe his eadiness. Based on he heo y o
planned beha iou (Ajzen, 1991, 2001), he ol-
lowing hypo hesis will also be es ed:
H2: Posi i e a i udes owa ds he wel a e ech-
nology, pe cei ed beha iou al con ol and sub-
jec i e no m ha e posi i e e ec s on beha iou al
in en ion ( eadiness o in oduce new wel a e
echnologies) in subs ance abuse ea men .
Me hodology
Sample
The da a we e collec ed ia an elec onic su ey
in 2015. The esponden s we e wo ke s in a la ge
non-p o i o ganisa ion (NGO) in Finland. The
ounda ion ope a es in se e al egions in Finland
and main ains a a ie y o subs ance abuse
se ices, such as subs ance abuse clinics, an
addic ion hospi al, p e en a i e ac i i ies, online
se ices, e c. The elec onic ques ionnai e was
sen by email o 700 pe sons (a o al o 724
employees wo ked o he ounda ion, bu 24
employees we e solely in ol ed in adminis a-
i e du ies and we e excluded om he su ey).
O hese, 129 pe sons (18%) engaged in he
ques ionnai e.
Six y- wo pe cen o esponden s wo ked in
ea men and ehabili a ion wo k; 12%
wo ked as an immedia e supe iso o ea -
men and ehabili a ion, and he es wo ked
in de elopmen , adminis a i e o manage ial
posi ions. Fi een pe cen o esponden s
wo ked in an addic ion hospi al; 7%wo ked
in a de elopmen uni and he emainde
wo ked in subs ance abuse clinics. Mo e han
one- hi d o he esponden s (39%)we e
nu ses and one- hi d (34%) we e social wo k-
e s (wi h a bachelo ’s deg ee in social se ices
o a mas e ’s deg ee majo ing in social wo k).
Eigh y wo pe cen o he esponden s we e
emale which is sligh ly highe han seen in he
o e all a ge popula ion (78.6%). The a e age
age o he esponden s (46 yea s) was sligh ly
highe han he a e age age in he a ge pop-
ula ion (44.8 yea s).
Me hods and analysis p ocess
The ques ionnai e con ained a o al o 71 ques-
ions, he majo i y o hem being Like - ype
scale i ems (1 ¼“ o ally disag ee”, 2 ¼
Figu e 1. The heo y o planned beha iou .
Ran anen and Toikko 135

“pa ially disag ee”, 3 ¼“nei he ag ee no dis-
ag ee”, 4 ¼“pa ially ag ee”, 5 ¼“ o ally
ag ee”), and heo e ical a iables we e con-
s uc ed by pe o ming a summa ion o indi id-
ual issues. Each o he heo e ical a iables was
o med om i e ques ions, excep a i ude
which was o med om eigh ques ions. In
o ming he sum a iables o a i ude,subjec-
i e no m and pe cei ed beha iou al con ol,
ques ions we e examined using explo a o y ac-
o analysis (maximum likelihood, a imax
wi h Kaise no malisa ion). On he basis o he
analysis, some ques ions we e excluded om
he measu e o a i ude, bu no changes we e
made in he measu es o subjec i e no m and
pe cei ed beha iou al con ol. In pa icula ,
issues ela ed o gene al a i udes owa ds ech-
nology we e emo ed om he measu e, and
his is also consis en wi h he heo y o planned
beha iou (Ajzen, 1991) which iews ha
beha iou in a speci ic si ua ion canno be
explained by gene al a i udes.
The eliabili ies o he sum a iables we e
examined using C onbach’s alpha coe icien s,
and all had alues g ea e han 0.7. In his s udy,
he no mali y o dis ibu ions was checked
using his og ams and he Kolmogo o –Smi -
no ’s es . The dis ibu ions o all o he a i-
ables we e no comple ely no mal. Because a
Like scale is o dinal and he dis ibu ions a e
skewed, he i s dependencies we e examined
using non-pa ame ic me hods. The e we e only
mino di e ences be ween pa ame ic (Pea son
p oduc -momen co ela ion coe icien ) and
non-pa ame ic co ela ions (Spea man’s ank
co ela ion coe icien ). Thus, he dependencies
we e examined pa ame ically.
I is no ewo hy ha he dis ibu ions o
Like - ype summa ed scales a e ne e exac ly
no mal. Clason and Do mody (1994) ha e
shown ha he e a e no ha d and as ules o
su icien ly deciding how “no mal” is no mal in
he case o Like scales, hence i is necessa y o
make hese decisions using di e en c i e ia.
Acco ding o No man (2010), pa ame ic s a is-
ics can be used wi h Like da a, wi h small
sample sizes, unequal a iances, and wi h
non-no mal dis ibu ions, wi hou ea o com-
ing o inco ec conclusions.
The ac ual s a is ical analyses we e con-
duc ed using linea eg ession analysis wi h he
s epwise me hod. Be o e ca ying ou he
eg ession analyses, he alidi y o he condi-
ions was checked. The no mali y o he esi-
dual dis ibu ions and he linea i y condi ion
we e checked g aphically and o mul icolli-
nea i y be ween he independen a iables by
using a iance in la ion ac o (VIF) coe i-
cien s. Dicho omous a iables (gende , heal h-
ca e educa ion) we e excluded om he
analysis o co ela ion and hei e ec was s ud-
ied only by means o a eg ession analysis.
Measu es
In he li e a u e e iew, i was no ed ha Ho -
mann (2013) made a dis inc ion be ween eigh
classes o wel a e echnology, paying a en ion
o hei pu pose and unc ion: communica ion
echnology, compensa o y and assis i e echnol-
ogy, “help wi h e e yday p ac ical asks”, dis-
ease moni o ing, emo e ea men , ehabili a ion
echnology, en e ainmen , as well as echnology
o social and emo ional suppo and s imula ion
echnology. Howe e , Ho mann’s classi ica ion
ela es o wellness echnology in gene al, and i
is no ac ually aimed a subs ance abuse ea -
men . In he cu en s udy, he hemes o ques-
ions a e d awn om he esul s o he Finnish
Lea ning and De elopmen Cen e o Subs ance
Abuse T ea men – Li ing Lab p ojec . The p oj-
ec ound a ious de elopmen a ge s ela ed o
he use o wel a e echnologies in subs ance
abuse ea men , such as sa e y (wo k sa e y
echnology and he sa e y echnology o medica-
ion), di e en ne wo k applica ions in subs ance
abuse ea men and pee suppo , as well as
echnology use in, o example, ehabili a i e
games (Ran anen & Weck o h, 2015).
The measu es o beha iou al in en ion (a¼
0.78), pe cei ed beha iou al con ol (a¼0.84)
and subjec i e no ms (a¼0.81) con ained i e
ques ions each, and he ques ions connec ed o
i e hemes: he use o echnology in gene al,
136 No dic S udies on Alcohol and D ugs 34(2)
wo k sa e y echnology, sa e y echnology o
medica ion, communica ion echnology, and
end-game applica ions. The measu e o a i ude
(a¼0.81) consis ed o h ee issues, combining
speci ic a i udes aimed a web-based ools o
pee suppo , web-based ools o ehabili a ion,
and ehabili a i e games (Table 1).
In his s udy, gende (male s. emale) and
educa ion (heal hca e educa ion s. o he edu-
ca ion) we e examined as dummy a iables,
and he educa ional le el o esponden s was
conside ed using ou ca ego y a iables (pos -
g adua e educa ion, mas e ’s deg ee, bachelo ’s
deg ee, and a lowe le el o educa ion). Good
and bad expe iences o wel a e echnologies
we e elici ed using s a emen s esponded o
wi h a Like scale (“I ha e good expe iences
wi h he unc ioning o wel a e echnology”
and “I is my expe ience ha wel a e echnol-
ogy does no wo k as desi ed”). In addi ion o
issues ela ed o heo e ical concep s and back-
g ound ques ions, he ques ionnai e add essed
i e sepa a e issues conce ning he main obs a-
cles o he in oduc ion o wel a e echnology
(“In ou o ganisa ion he main obs acles o he
in oduc ion o new echnologies a e ela ed
o ...”).
Resul s
Gene al desc ip ion
O e all, esponden s es ima ed ha hey we e
qui e p epa ed o in oduce new echnological
applica ions, i hey a e opical and ele an o
hei o ganisa ion (Table 2). Fo y wo pe cen
o he esponden s o ally ag eed and 46%pa -
ially ag eed wi h he s a emen o “In gene al I
am eady and e en en husias ic abou he
in oduc ion o new echnological applica ions,
i hey a e capable o imp o ing he quali y o
e ec i eness o he wo k”. The e was la gely
posi i e suppo o in oducing new echnol-
ogy (e.g., ala m sys ems, secu i y phone sys-
ems, access con ol) which imp o es sa e y a
wo k (60% o ally ag ee and 32%pa ially
ag ee). Acco ding o he esponden s’ assess-
men s, he main obs acles o he in oduc ion
o new echnologies ela ed o economic ac o s
(78% o ally o pa ially ag ee), lack o ime
(69% o ally o pa ially ag ee) and sho com-
ings in people’s skills (66% o ally o pa ially
ag ee). Hal he esponden s (53%) o ally o
pa ially ag eed wi h he s a emen “In ou o ga-
nisa ion he main obs acles o he in oduc ion
o new echnologies a e ela ed o people’s
(s a , manage s) a i udes”, and less han hal
(43% o ally o pa ially ag ee) sugges ed ha
he main obs acles we e ela ed o decision-
making and managemen .
The esponden s had qui e a posi i e a i ude
owa ds wel a e echnology, i.e., hey belie ed
in he use ulness o wel a e echnology in sub-
s ance abuse ea men (see Table 2). The
majo i y o esponden s conside ed ha i is
impo an o ha e web-based ools o pee sup-
po (78%o esponden s o ally o pa ially
ag ee wi h he claim) and o sel -
ehabili a ion (78% o ally o pa ially ag ee).
Thei a i ude owa ds he in oduc ion o game
applica ions was less posi i e, bu s ill, 62%o
he esponden s el ha game applica ions a e
use ul in subs ance abuse ea men ( o ally o
pa ially ag ee). Howe e , esponden s el ha
hei colleagues’ a i udes owa ds wel a e ech-
nology we e no qui e as posi i e. Fo example,
only 5%o esponden s o ally ag eed wi h he
claim “In gene al my wo k communi y
Table 1. Sum a iables and hei eliabili ies.
Va iable NI ems Mean SD C onbach’s alpha
Beha iou al in en ion 129 5 4.01 0.721 0.78
A i ude 129 3 3.86 0.847 0.81
Subjec i e no m 129 5 3.50 0.751 0.81
Pe cei ed beha iou al con ol 129 5 4.01 0.760 0.84
Ran anen and Toikko 137
Table 2. Ag eemen s a emen s ela ed o he heo y o planned beha iou (N¼129).
Ques ion
1 o ally
disag ee
2 pa ially
disag ee
3 nei he
ag ee no
disag ee
4 pa ially
ag ee
5 o ally
ag ee
Beha iou al in en ion
“In gene al I am eady and e en en husias ic abou he
in oduc ion o new echnological applica ions, i
hey a e capable o imp o ing he quali y o
e ec i eness o he wo k.”
0.8% 4.7% 7.0% 45.7% 41.9%
“I would be eady o expe imen and in oduce new
echnology o inc ease he sa e y o medica ions
(e.g., au oma ic medicine dispense ), i I would ha e
he oppo uni y o do so.”
1.6% 6.2% 18.6% 31.8% 41.9%
“I would be eady o he in oduc ion o a new
echnology ha imp o es sa e y a wo k (e.g., ala m
sys ems, secu i y phone sys ems, access con ol).”
0.8% 5.4% 2.3% 31.8% 59.7%
“I would be e y mo i a ed owa ds he in oduc ion
o new communica ion echnologies in subs ance
abuse ea men and ad ise clien s o use i .”
5.4% 6.2% 17.1% 43.4% 27.9%
“I would be in e es ed o in oduce a a ie y o game
applica ions o subs ance abuse ea men .”
6.2% 17.1% 14.7% 45.0% 17.1%
A i ude
“I hink i would be impo an o de elop web-based
ools o pee suppo .”
0.8% 9.3% 12.4% 45.0% 32.6%
“I hink i would be impo an o de elop web-based
ools o help subs ance abuse s in hei sel -
ehabili a ion.”
3.1% 8.5% 10.9% 41.9% 35.7%
“I belie e ha game applica ions could be use ul in he
ehabili a ion o subs ance abuse s.”
3.9% 9.3% 24.8% 45.7% 16.3%
Subjec i e no m
“In gene al, my wo k communi y sympa hises wi h he
in oduc ion o new echnology in subs ance abuse
ea men .”
2.3% 24.0% 25.6% 42.6% 5.4%
“I belie e ha my wo k communi y would suppo he
in oduc ion o new echnological applica ions o
inc ease he sa e y o medica ions.”
2.3% 7.8% 19.4% 47.3% 23.3%
“My wo k communi y conside s he in oduc ion o
new echnology ela ing o pe sonnel sa e y o be
impo an .”
1.6% 9.3% 23.3% 39.5% 26.4%
“My wo k communi y conside s he disco e y o web-
based solu ions o subs ance abuse ea men and
pee suppo o be impo an .”
4.7% 17.1% 31.8% 33.3% 13.2%
“I belie e ha my wo k communi y would suppo he
in oduc ion o a a ie y o game applica ions in
subs ance abuse ea men .”
5.4% 14.7% 34.9% 35.7% 9.3%
Pe cei ed beha iou al con ol
“In gene al, I hink my own abili ies o use echnology
a e good.”
6.2% 11.6% 17.8% 44.2% 20.2%
“I am su e ha I could easily lea n o use a new
echnology ha inc eases he sa e y o medica ions
i he ma e was app op ia e in ou uni .”
2.3% 3.1% 17.8% 40.3% 36.4%
(con inued)
138 No dic S udies on Alcohol and D ugs 34(2)
sympa hises wi h he in oduc ion o new ech-
nology in subs ance abuse ea men ”, al hough
43%pa ially ag eed wi h he claim.
Pe cei ed beha iou al con ol was exam-
ined using i e ques ions. O e all, espon-
den s’ con idence in hei own echnological
capaci y was s ong. Fo example, 64%o
esponden s we e o he opinion ha hei own
echnology skills we e good, and only 18%
o ally o pa ially disag eed wi h his pe cep-
ion. O e 90%o esponden s us ed hei
abili y o lea n easily, in o de o use new ech-
nology applica ions ha would inc ease sa e y
in hei wo k. Responden s also alued hei
own abili ies o lea n o use new elec onic
communica ions echnology (78%o espon-
den s o ally o pa ially ag ee wi h he claim),
new game applica ions (71% o ally o pa -
ially ag ee), and echnology ha inc eased he
sa e use o medicinal p oduc s (77% o ally o
pa ially ag ee).
Co ela ions be ween a iables
The co ela ions be ween a iables we e exam-
ined using he Pea son p oduc -momen co e-
la ion coe icien . Table 3 shows ha a pe son’s
eadiness o in oduce new echnology depends
s ongly on ha pe son’s a i ude owa ds ech-
nology, a sense o con ol ega ding he use o
wel a e echnology, as well as hei no ma i e
expec a ions. These esul s a e ully consis en
wi h he heo y o planned beha iou .
The examina ion o hese co ela ions also
e eals some links be ween demog aphic ac-
o s and cogni ions ela ed o wel a e echnol-
ogy. In pa icula , he analysis shows ha he
pe cei ed beha iou al con ol depends s ongly
on he esponden ’s age. Young wo ke s end o
ely mo e on hei own echnology skills han
olde wo ke s; howe e , he ela ion be ween
age and beha iou al in en ion is much weake .
Acco ding o co ela ion analysis, he le el o
educa ion seems o inc ease posi i e a i udes
owa ds wel a e echnology, bu he beha-
iou al in en ion is no signi ican ly dependen
on he le el o educa ion.
Acco ding o Table 3, p e ious posi i e
expe iences o wel a e echnology ha e signi -
ican co ela ions (p< .01) wi h echnology a i-
udes, pe cei ed con ol and a eadiness o
in oduce new wel a e echnologies in sub-
s ance abuse ea men . In con as , nega i e
expe iences do no seem o be associa ed wi h
cogni ions ela ed o he in oduc ion o wel a e
echnology. On he con a y, posi i e and neg-
a i e expe iences a e co ela ed wi h each
o he , and hus, i may be no ed ha he expe-
ience o di e en wel a e echnologies makes
i easie o in oduce a new echnology.
Table 2. (con inued)
Ques ion
1 o ally
disag ee
2 pa ially
disag ee
3 nei he
ag ee no
disag ee
4 pa ially
ag ee
5 o ally
ag ee
“I belie e ha i would be easy o me o lea n how o
use new echnology applica ions ha inc ease sa e y
a wo k (e.g., ala m sys ems, secu i y phone
sys ems, access con ol).”
– 2.3% 6.2% 35.7% 55.8%
“I belie e ha I can easily lea n o use he new
communica ion echnology o a deg ee ha I am
able o guide he o he s, i he new echnology is
in oduced.”
3.9% 4.7% 13.2% 36.4% 41.9%
“I belie e ha I could easily lea n o use new game
applica ions, and guide o he s in hei use i
needed.”
3.1% 7.8% 17.8% 44.2% 27.1%
Ran anen and Toikko 139