Employees' attitudes towards welfare technology in substance abuse treatment in Finland

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 jou nals.sagepub.com/home/nad C ea i e Commons CC BY-NC: This a icle is dis ibu ed unde he e ms o he C ea i e Commons A ibu ion-Non Comme cial 4.0 License (h p://www.c ea i ecommons.o g/licenses/by-nc/4.0/) which pe mi s non-comme cial use, ep oduc ion and dis ibu ion o he wo k wi hou u he pe mission p o ided he o iginal 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