Mäkiniemi, J.-P., e al. (2020). A No el Cons uc To Measu e Employees’ Technology-
Rela ed Expe iences o Well-Being: Empi ical Valida ion o he Techno-Wo k
Engagemen Scale (TechnoWES).
Scandina ian Jou nal o Wo k and O ganiza ional
Psychology
, 5(1): 4, 1–14. DOI: h ps://doi.o g/10.16993/sjwop.79
In oduc ion
Digi aliza ion o wo king li e
Beginning in he 1960s, di e gen mic oelec onics and
obo ics echnologies we e in oduced in manu ac u ing,
inc easing he le el o au oma ion (S ock e al., 2018). The
compu e iza ion o wo k began in 1970, and now, abou
52% o Eu opean employees wo k wi h compu e s a leas
25% o hei wo k ime (Ko unka and Hoonake , 2014;
Ko unka and Va iainen, 2017). I is commonly sugges ed
we a e now in he middle o he Fou h Indus ial
Re olu ion, and wo k p ocesses will change d ama ically
because o no el echnological b eak h oughs, such as
a i icial in elligence, obo ics, he In e ne o Things,
au onomous ehicles, 3D p in ing, bio echnology, and
quan um compu ing (Schwab, 2015); in addi ion, big
da a analy ics and cloud echnology will be one o he
key d i e s o business g ow h (Wo ld Economic Fo um,
2018). Fo example, in he u u e, nu ses will wo k in
collabo a ion wi h medical obo s, 3D p in e s will become
pa o manu ac u ing p ocesses, and a leas some ace-
o- ace in e ac ions will be eplaced by cha bo s.
E iden ly, he e appea o be pi alls ela ed o he
de elopmen o digi aliza ion, such as he possible dis-
placemen o employees. Being a ype o con ol, no el
digi al measu ing sys ems o acking pe o mance and
p oduc i i y may also cause conce ns among employees
(Rolandsson e al., 2019). Howe e , cu en de elopmen s
ha e also posi i e e ec s, such as sa e and mo e ewa ding
jobs i di e en echnologies ake ca e o he demanding,
mono onous, and dange ous asks. Fo example, indus ial
obo s can assis wo ke s wi h hea y objec s (Fische and
Pöhle , 2018). To con ol he ongoing e olu ion, we
need a comp ehensi e iew o how (digi al) echnology
a ec s ou li es (Schwab, 2015). Thus, he e is a need o a
balanced pic u e o bo h he nega i e and posi i e e ec s
o in ensi e digi aliza ion o wo k on employee well-
being. The aim o he cu en pape is o p esen a new
concep called echno-wo k engagemen and o alida e a
no el scale o measu ing i wi h wo di e en s udies.
Techno-wo k engagemen as a posi i e and ul illing
well-being expe ience
Techno-wo k engagemen is based on he concep o
wo k engagemen , which is a posi i e and ul illing wo k-
ela ed s a e o mind; his can be u he di ided in o h ee
dimensions: igo , dedica ion, and abso p ion (Schau eli
ORIGINAL ARTICLE
A No el Cons uc To Measu e Employees’ Technology-
Rela ed Expe iences o Well-Being: Empi ical Valida ion
o he Techno-Wo k Engagemen Scale (TechnoWES)
Jaana-Piia Mäkiniemi, Salla Ahola and Johanna Joensuu
Because o he cu en de elopmen s in digi aliza ion, no el echnological solu ions ha e changed wo k
p ocesses and acco dingly in luenced employee well-being. The e is a lack o measu es o posi i e well-being
expe iences ega ding echnology a wo k. The e o e, he cu en s udy in oduces a no el cons uc —namely
echno-wo k engagemen —and a scale o measu e i . Techno-wo k engagemen is de ined as a posi i e
expe ience o well-being ega ding he use o echnology a wo k. The s udy es ed he ac o ial, disc iminan ,
and con e gen alidi y o he scale, TechnoWES-9. Using da a om wo samples, he ac o s uc u e o he
cons uc was analyzed wi h a con i ma o y ac o analysis (CFA). In addi ion, in S udy 2, CFAs, co ela ions,
and mean di e ences we e used o assess he disc iminan and con e gen alidi y and whe he he sho e
scale can be used as an al e na i e o he longe one. The esul s conce ning he ac o ial alidi y suppo
he iew ha echno-wo k engagemen is be e ep esen ed as a h ee- ac o han as a unidimensional
cons uc . Like TechnoWES-9, TechnoWES-3 is posi i ely co ela ed wi h echnology- ela ed job esou ces;
also, bo h can be disc imina ed om echnos ess. TechnoWES-9, al hough no lawless, is a eliable and alid
indica o o echno-wo k engagemen , and he sho e e sion can be used as i s al e na i e. In he u u e,
he ela ionships be ween wo k engagemen , echnos ess, and echno-wo k engagemen could be measu ed
and he scale could be es ed in di e en coun ies and occupa ions.
Keywo ds: wo k engagemen ; echnology; echno-wo k engagemen ; echnos ess; con i ma o y ac o
analysis; alida ion
Tampe e Uni e si y, FI
Co esponding au ho : Jaana-Piia Mäkiniemi
(jaana-pi[email p o ec ed])
Mäkiniemi e al: A No el Cons uc To Measu e Employees’ Technology-Rela ed Expe iences o Well-BeingA . 4, page 2 o 14
e al., 2002). Vigo e e s o high le els o ene gy, men al
esilience, and a willingness o pu in e o and o be
pe sis en . Dedica ion is cha ac e ized by en husiasm,
inspi a ion, and p ide, while abso p ion in ol es ull
concen a ion on one’s wo k (Schau eli e al., 2002). Wo k
engagemen desc ibes how employees eel in ela ion o
hei wo k in gene al; whe eas, echno-wo k engagemen
is a mo e speci ic s a e o wo k well-being in ela ion o he
use o (digi al) echnology a wo k (c. . Mäkiniemi, Ahola
and Joensuu, 2019). The e o e, echno-wo k engagemen
can be de ined as ollows: Techno-wo k engagemen is a
posi i e and ul illing well-being s a e o expe ience ha
is cha ac e ized by igo , dedica ion, and abso p ion wi h
espec o he use o echnology a wo k.
Wo k engagemen was selec ed as a base because
i is a well-known, es ablished, and alida ed concep
( o a e iew, see Kulikowski, 2017). The le els o wo k
engagemen can a y wi hin people om day o day and
when hey wo k on di e en wo k asks ( o a e iew, see
Sonnen ag, 2017). In line wi h he ask-speci ic app oach
(e.g., Sonnen ag, 2017), we ocus on employees’ wo k
engagemen expe iences as hey wo k on wo k asks ha
in ol e echnology. O he speci ic scales ha e also been
de eloped, such as he Schoolwo k Engagemen In en o y
(EDA) (Salmela-A o and Upadaya, 2012). We assume echno-
wo k engagemen is a speci ic ype o wo k engagemen .
In p io s udies on echnology- ela ed wo k well-being
expe iences, he ocus has been mos ly on employees’
nega i e expe iences wi h echnology, namely echnos ess
expe iences. Technos ess e e s o a speci ic ype o wo k
s ess expe ienced by end use s because o hei use o
in o ma ion and communica ion echnologies (ICT) a wo k
(Salano a, Llo ens and Ci e, 2013). Typical echnos ess
expe iences include ou ypes o eelings: anxie y, a igue,
skep icism, and belie s conce ning ine icacy ela ed o he
use o echnologies (Salano a e al., 2013; Salano a, Llo ens
and Ven u a, 2014). Thus, echnos ess can be de ined as a
nega i e well-being expe ience o s a e; whe eas, echno-
wo k engagemen is a posi i e one. Acco dingly, we expec
echnos ess is a di e gen concep om echno-wo k
engagemen .
Rela ions wi h echno-wo k engagemen and
echnology- ela ed job esou ces
Se e al s udies ha e ound a posi i e associa ion be ween
job esou ces and wo k engagemen (Bakke , Alb ech and
Lei e , 2011; Halbesleben, 2010). Typical job esou ces
include social suppo , a iabili y, app ecia ion, eedback,
and au onomy, all o which s imula e pe sonal g ow h,
lea ning and de elopmen , assis in achie ing he goals o
he job, and bu e he nega i e e ec s o demands (Bakke
and Deme ou i, 2007). Because we assume echno-wo k
engagemen is a speci ic ype o wo k engagemen , we
also expec he e will be a posi i e associa ion be ween
echnology- ela ed job esou ces and echno-wo k enga-
gemen . The e o e, echnology- ela ed job esou ces may
enhance o suppo echno-wo k engagemen . This is in
line wi h he heo e ical amewo k de eloped by Day,
Sco , and Kelloway (2010): employees can expe ience
he use o echnology ela ed o wo king li e—such as
ICT—ei he as a job demand o job esou ce. Simply pu ,
when echnology ac s as a job esou ce, i assis s wi h
he comple ion o wo k goals and p omo es lea ning and
de elopmen . Fo example, a eache who uses educa ion
echnology may use echnology o imp o e he quali y
o s uden s’ lea ning, which is he eache ’s wo k goal;
i also gi es he eache an oppo uni y o lea n new
hings and suppo s p o essional de elopmen . The use
o ICT a wo k can assis in e ec i e in o ma ion ans e ,
imp o e wo k pe o mance, and gi e mo e eedom and
lexibili y o employees in e ms o wo king places and
wo k-li e balance, hus enhancing employee well-being
(Bo di e al., 2018; Day e al., 2010). In addi ion, ICT a
wo k can enhance p oduc i i y, help o p o ide be e
se ice quali y (Ko unka and Hoonake , 2014; Ko unka
and Va iainen, 2017), and assis human decisions wi h
complex da a analysis (Fische and Pöhle , 2018).
In he cu en s udy, we examined he ela ion be ween
echno-wo k engagemen and ou echnology- ela ed
job esou ces, namely echnology- ela ed au onomy
(Lam, Cheng and Choy, 2010; Mäkiniemi e al., 2019),
echnology- ela ed social/collegial suppo (Lam e al.,
2010; Mäkiniemi e al., 2019), echnology- ela ed sel -
e icacy (Wang, E me and Newby, 2004; Mäkiniemi e al.,
2019), and echnology- ela ed alue cong uence (Skaal ik
and Skaal ik, 2011). All he abo e-men ioned esou ces,
when measu ed as gene al job esou ces, a e shown o be
posi i ely associa ed wi h gene al wo k engagemen (e.g.,
Bakke and Deme ou i, 2007; Huh ala and Feld , 2016;
Li e al., 2015; Nielsen e al., 2017; Schau eli and Bakke ,
2004; So heix e al., 2013; Ven u a, Salano a and Llo ens,
2015; Xan hopoulou e al., 2007). Acco dingly, we assume
echnology- ela ed job esou ces a e posi i ely ela ed o
echno-wo k engagemen .
On he o he hand, when echnology ac s as a job
demand, i equi es sus ained and ex ended physical o
psychological e o o skills, and i migh be associa ed
wi h inc eased physical and psychological cos s, such
as exhaus ion o s ess. Fo example, ypical ICT- ela ed
demands include ICT mal unc ions, incompa ible
echnologies, expec a ions o con inuous lea ning, as
esponses and cons an a ailabili y, in o ma ion o e load,
and poo quali y o communica ion (Day e al., 2010;
S ich e al., 2015). Usually, lea ning new echnologies
equi es ex a ime and e o , po en ially causing s ess
(c. . Ragu-Na han e al., 2008). Likely, echnology- ela ed
job demands also can be u he di ided as challenge
(e.g., lea ning new hings) o hind ance demands, as
is he case wi h gene al job demands (c. . Tadić, Bakke
and Oe lemans, 2015). Technology- ela ed job demands
sha e a lo o simila i ies wi h echno-s esso s, which
c ea e o a e he sou ce o echnos ess. Typical echno-
s esso s include o ced changes ela ed o wo k habi s,
he complexi y o echnology, ea o being eplaced,
and cons an echnological change (Ragu-Na han e al.,
2008). Employees migh also ha e eelings and hough s
conce ning wo k- ela ed echnologies wi hou p io
expe ience o he echnologies. Fo example, employees
can eel insecu e because o he cu en end owa d
he au oma ion and digi aliza ion o wo k. In addi ion,
Mäkiniemi e al: A No el Cons uc To Measu e Employees’ Technology-Rela ed Expe iences o Well-Being A . 4, page 3 o 14
echnological changes, including he adop ion o new
echnologies, may in luence he wo k sys em and he
psychosocial wo k en i onmen , including job con ol
and social ela ions (Ca ayon and Smi h, 2014; O’D iscoll,
Bi on and Coope , 2009).
Need o a no el scale o measu ing posi i e
well-being expe iences wi h espec o he use o
echnology a wo k
Taken oge he , based on p io s udies, i is well-known
wha kinds o ac o s a e conside ed demanding (e.g.,
in o ma ion o e load as a echnology- ela ed job demand)
and inspi ing (e.g., lexibili y as a echnology- ela ed job
esou ce) when using echnology a wo k. P io s udies
ha e ocused la gely on echno-s esso s (i.e., echnology-
ela ed job demands) wi hou analyzing hei ela ion
o any nega i e o posi i e well-being s a es. Especially,
li le is known abou employees’ posi i e expe iences o
s a es o well-being ega ding he use o echnology. We
assume his o s em pa ly om he ac he e is a lack
o measu emen ins umen s o assess hese kinds o
posi i e expe iences o well-being.
We a gue he e a e ou main easons why a new
cons uc and scale is needed. Fi s , digi aliza ion o wo k
and wo k p ocesses will con inue o ad ance (e.g., Schwab,
2015), so he e is a need o ob ain a comp ehensi e pic u e
o i s e ec s on employee well-being. Second, as s a ed
ea lie , mos p io s udies on employee well-being and
echnology use a wo k ha e ocused on nega i e well-
being expe iences and echno-s esso s (Day e al., 2010;
Day e al., 2012; Ragu-Na han e al., 2008; S ich e al.,
2015). This scope migh be oo na ow when conside ing
ha one aim o wo k digi aliza ion is o inc ease employee
well-being, o ins ance, by educing he physical
demands o wo k. Wi h he nega i ely loaded i ems,
i is no possible o cap u e he posi i e aspec s o he
phenomenon because he lack o a nega i e expe ience
(e.g., low le el o echnos ess) does no necessa ily
indica e he p esence o a posi i e expe ience (e.g., high
echno-wo k engagemen ). Also, leading echnos ess
esea che s ha e poin ed ou a need o a mo e posi i e
app oach and concep s such as good echnos ess, o
eus ess, when using echnology a wo k (Ta a da , Coope
and S ich, 2019). Techno-wo k engagemen is a po en ial
candida e o measu ing hese kinds o posi i e well-being
expe iences. Thi d, echnology has been o en limi ed o
ICT, and he ocus o hese s udies has been on he e ec s
o compu e -media ed wo k on employee well-being ( o a
e iew, see S ich e al., 2015). The p io scope migh be oo
limi ed when conside ing he new ypes o echnologies
being in oduced o employees, such as obo s, cha bo s,
3D p in e s, and i ual eali y. The e o e, he e is a need
o a measu emen scale ha is no limi ed o a speci ic
echnology (e.g., a compu e ) bu ha can be adjus ed o
di e en con ex s and echnologies. Fou h, we belie e
wi h he no el scale i will no only be possible o explo e
and unde s and he le els o echno-wo k engagemen ,
bu also o unde s and which ac o s (e.g., echnology-
ela ed job esou ces) a e associa ed wi h i ; i will also be
possible o de elop employee well-being in he con ex
o digi alized wo k a he han jus ying o elimina e
he demanding aspec s. Thus, wi h a alid sho scale,
o ganiza ions can ack he e ec s o digi aliza ion o
wo k p ocesses in a mo e comp ehensi e way.
The e o e, he main aim o he cu en s udy is o
assess he ac o ial alidi y o he no el Techno-Wo k
Engagemen Scale (TechnoWES) in wo s udies and o
explo e which ac o ial s uc u e is mo e alid. Based on
he wo k engagemen heo y and empi ical indings, we
hypo hesize a h ee- ac o s uc u e will be supe io o
a one- ac o s uc u e (H1) (Bakke e al., 2011). Fu he ,
because wo k engagemen can be eliably measu ed wi h
an ul asho h ee-i em scale (Schau eli e al., 2017), we
assume a h ee-i em e sion o he TechnoWES is a alid
indica o o echno-wo k engagemen along wi h he
longe nine-i em e sion. We analyze his possibili y in
S udy 2 and simul aneously assess he con e gen and
disc iminan alidi y o bo h e sions o he TechnoWES.
Mo eo e , we assess he disc iminan alidi y by es ing
whe he he TechnoWES is a unique scale di e ing
om he echnos ess scale (Salano a e al., 2013). We
hypo hesize ha because echnos ess is a nega i e
well-being expe ience and echno-wo k engagemen is
a posi i e one (Mäkiniemi e al., 2019; Salano a e al.,
2013), echno-wo k engagemen can be disc imina ed
om echnos ess and he e will be a weak o mode a e
nega i e co ela ion be ween he cons uc s o bo h
e sions o he TechnoWES (H2).
Mo eo e , in line wi h he assump ion echno-wo k
engagemen migh be a speci ic ype o wo k engagemen
and wi h p e ious s udies sugges ing a posi i e associa ion
be ween job esou ces and wo k engagemen (e.g.,
Nielsen e al., 2017; Schau eli e al., 2017), we assume
echnology- ela ed au onomy, echnology- ela ed social
suppo , echnology- ela ed sel -e icacy, and echnology-
ela ed alue cong uence a e posi i ely associa ed wi h
echno-wo k engagemen a a weak o mode a e le el (c. .
Schau eli e al., 2017), and his applies o bo h e sions
o he TechnoWES (H3). In line wi h he indings on he
h ee-i em scale o wo k engagemen (Schau eli e al.,
2017), we expec he h ee-i em and nine-i em e sions
o TechnoWES a e highly co ela ed (H4). Obse ing hese
co ela ions would also suppo he con e gen alidi y o
he TechnoWES. Finally, in line wi h he p e ious indings
sugges ing demog aphic di e ences on wo k engagemen
(e.g., Hakanen e al., 2019) and on echnos ess (e.g.,
Sy änen e al., 2016), we explo e whe he he e a e
di e ences in he mean sco e o echno-wo k engagemen
and whe he he ela ionships o demog aphics wi h he
TechnoWES a e simila when measu ed wi h he h ee-
i em and he nine-i em scale.
Summa y o he hypo heses o S udy 1 and S udy 2
H1: A h ee- ac o s uc u e o TechnoWES will be supe io
o a one- ac o s uc u e o i (S udy 1; S udy 2).
H2: Techno-wo k engagemen can be disc imina ed
om echnos ess, and he e will be a weak o mode a e
nega i e co ela ion be ween he cons uc s o bo h
e sions o he TechnoWES (S udy 2).
Mäkiniemi e al: A No el Cons uc To Measu e Employees’ Technology-Rela ed Expe iences o Well-BeingA . 4, page 4 o 14
H3: Technology- ela ed au onomy, echnology- ela ed social
suppo , echnology- ela ed sel -e icacy, and echnology-
ela ed alue cong uence a e posi i ely associa ed wi h
echno-wo k engagemen a a weak o mode a e le el, and
his applies o bo h e sions o he TechnoWES (S udy 2).
H4: The h ee-i em and nine-i em e sions o TechnoWES
a e highly co ela ed (S udy 2).
S udy 1
Me hod
Pa icipan s and da a collec ion
O iginally, 830 Finnish employees om eigh public
and p i a e o ganiza ions pa icipa ed. A o al o 5,136
employees we e con ac ed, ei he by sending an in i a ion
o pa icipa e and a link o he elec onic ques ionnai e
di ec ly o each employee’s email add ess o by in o ming
he employees abou he s udy ia he o ganiza ion’s
in ane (o o he wise wi hin he o ganiza ion), depending
on he o ganiza ion’s policies. All he p ocedu es ollowed
e hical p inciples and he codes o conduc o he
Ame ican Psychological Associa ion. In o med consen
was ob ained om he pa icipan s. Al oge he , 16%
comple ed he ques ionnai e, wi h he esponse a es
a ying om 4–87% ac oss he o ganiza ions. Based on
he da a sc eening p io o analysis, 101 esponden s had
no disc imina ed be ween hei answe s on he Techno-
Wo k Engagemen Scale (TechnoWES) bu had used
he same esponse op ion o all nine i ems. O hem,
21.8% had chosen he lowes ex eme op ion o he scale
o all nine i ems, and 24.8% had chosen he highes
ex eme op ion. No disc imina ing be ween answe s was
conside ed an indica ion o possible esponse bias, which
e e s o a sys ema ic endency o espond o a ange o
i ems on a basis o he han he speci ic i em’s con en .
Pa icula ly wi hin alida ion s udies, such esponse bias
may a ec he alidi y o he esul s; he e o e, we ook
his in o accoun . The analyses we e conduc ed wi hou
hese esponden s’ da a, esul ing in a o al numbe o
729 esponden s, o whom 495 (67.9%) we e emale, 213
(29.2%) we e male, and 21 (2.9%) chose no o indica e
hei gende . The a e age age o he esponden s was
45.9 (SD = 10.8; ange 17–67) yea s. The esponden s
we e highly educa ed, wi h 45.5% ha ing comple ed
uni e si y educa ion and 23.3% ha ing comple ed a
uni e si y o applied sciences educa ion. A o al o 22.5%
o he esponden s had comple ed oca ional educa ion,
7.1% had gene al uppe seconda y educa ion, and 1.5%
had basic educa ion. The majo i y (55.3%) we e o icials
o expe s, 21.4% we e wo ke s, 16.9% we e supe iso s
o managemen , and 5.3% we e senio managemen ( he
emaining 1.1% iden i ied hei job as o he ).
Measu es
Techno-Wo k Engagemen Scale
We designed a new ins umen — he TechnoWES-9—
o measu ing wo k engagemen ega ding he use o
echnology a wo k. The Finnish e sion o he U ech Wo k
Engagemen Scale (UWES-9; Hakanen, 2009; Schau eli e
al., 2006) was used as a s a ing poin o build he new scale.
UWES cap u es he h ee dimensions o wo k engagemen
and is ypically used o measu e he le el and ac o ial
s uc u e o wo k engagemen . The sho e , nine-i em
e sion—UWES-9 (Schau eli, Bakke and Salano a, 2006)—
has become mo e popula han he 17-i em e sion (UWES-
17) and is now conside ed he s anda d (Kulikowski, 2017).
Mo eo e , bo h e sions o he UWES ha e been ansla ed
and alida ed o many di e en coun ies (Kulikowski,
2017). Fo example, in Finland, he UWES-9 is commonly
used by scien is s and p ac i ione s (e.g., Hakanen, 2009).
Recen ly, a h ee-i em e sion (UWES-3) was in oduced
and was shown o be a eliable and alid al e na i e o
measu e wo k engagemen (Schau eli e al., 2017).
The e a e somewha inconsis en indings conce ning
he ac o ial alidi y o he UWES, namely whe he he
heo e ical h ee- ac o s uc u e—which includes he
igo , dedica ion, and abso p ion subscales— i s he
empi ical da a be e han o he ac o s uc u es. The
commonly es ablished iew is he i o he h ee- ac o
s uc u e is supe io o he o he models (e.g., Bakke
e al., 2011). Howe e , a ecen e iew o 21 s udies
(Kulikowski, 2017) wi hin he CFA app oach assessed he
ac o ial alidi y o he UWES-9 and UWES-17 and ound
no common ag eemen . Kulikowski (2017) assumed one
eason o he inconsis en indings may be ha wo k
engagemen is mo e con ex a ian han uni e sal.
In line wi h he UWES-9, he TechnoWES-9 consis s
o nine i ems ha ep esen h ee subscales (i.e., igo ,
dedica ion, and abso p ion), and each is measu ed by
h ee i ems. Eigh o he i ems we e adop ed om he
UWES-9. We conside ed one o he UWES-9 i ems om
he igo subscale o be unsui able in he con ex o
echno-wo k engagemen , namely ‘When I ge up in he
mo ning, I eel like going o wo k,’ and he i em was
excluded. To eplace i , we adap ed he i em ‘A my wo k,
I always pe se e e, e en when hings do no go well’
om he igo subscale o he UWES-17. We conside ed
his i em bo h sui able and ele an because when using
echnology a wo k, people may ace echnological
p oblems ha demand pe se e ance. The selec ed i ems
we e hen modi ied and ew i en (in Finnish) o i a
echnology con ex . In p ac ice, o example, he o iginal
i em ‘I am en husias ic abou my wo k’ was modi ied o
‘I am en husias ic abou u ilizing echnology in my job’
(see Table 1 o he English ansla ions o all nine i ems).
The o iginal Finnish i ems a e a ailable om he i s
au ho upon eques .
The esponden s we e asked o answe ‘How o en do
you ha e he ollowing kinds o eelings and hough s’
using a 7-poin Like scale anging om ‘ne e ’ (sco ed
as 0) o ‘always’ (sco ed as 6) (c. . Hakanen, 2009). A he
beginning o he ques ionnai e, i was explained digi al
echnology e e s o elec onic da a ansmission and
he de ices and applica ions ha enable p oducing and
u ilizing knowledge, such as email, mobile phones, and
social media. The esponden s we e no asked o hink
abou any speci ic echnology o applica ion when
answe ing he TechnoWES-9 i ems. The scale was pa o
he sec ion ha included ques ions abou he esponden s’
wo k well-being and wo k condi ions.
Mäkiniemi e al: A No el Cons uc To Measu e Employees’ Technology-Rela ed Expe iences o Well-Being A . 4, page 5 o 14
Da a analysis
We used LISREL 8.80 o pe o m CFAs o assess he i
o he hypo hesized h ee- ac o second-o de s uc u e
(M1) and an al e na i e one- ac o s uc u e (M2) o
he TechnoWES. We modeled he h ee- ac o s uc u e
using a second-o de model (B unne , Nagy and Wilhelm,
2012) because he p elimina y CFA analyses indica ed
high co ela ions (0.73–0.91, Sample 1; 0.82–0.87,
Sample 2) be ween he h ee ac o s. In he h ee- ac o
second-o de model, each ac o (i.e., each subscale) was
speci ied o include h ee i ems, and one i em loading o
each ac o was cons ained o 1 o de e mine he scale
o he la en a iables. On he second-o de le el, one
ac o loading was cons ained o 1 (and e o a iance
was cons ained o 0.01) o achie e iden i ica ion o
he model. In he one- ac o model, all he i ems we e
speci ied o load on o a single ac o , wi h one i em
loading cons ained o 1. Because o he non-no mali y
o he da a (mean skewness = –0.40, ange om –0.70 o
–0.03; mean ku osis –0.89, ange om –1.30 o –0.41,
Sample 1; mean skewness = 0.09, ange om –0.31 o
0.73, mean ku osis = –1.07, ange om –1.23 o –0.79,
Sample 2), we used he diagonally weigh ed leas squa es
(DWLS) es ima ion me hod. We used DWLS ins ead o
obus ML because i has been shown o be less biased
and mo e sensi i e in he case o non-no mal i em
dis ibu ions and in scales wi h i e o mo e ca ego ies
(e.g., Li, 2016).
The CFA models we e e alua ed wi h he chi-squa ed
es and o he i indica o s: oo mean squa e e o o
app oxima ion (RMSEA), s anda dized oo mean squa e
esidual (SRMR), non-no med no med i index (NNFI, o
he Tucke –Lewis index; TLI), and compa a i e i index
(CFI). The Akaike in o ma ion c i e ion (AIC) was used o
compa e he models, wi h a smalle alue indica ing a
be e i (Sch eibe e al., 2006).
The accep able model i is indica ed by a χ²/d a io
o 3 o less (Sch eibe e al., 2006). The widely used cu -
o alues o e alua ing he i o CFA models o he da a
come om Hu and Ben le ’s (1999) simula ion s udy;
hey a e 0.06 o RMSEA, 0.08 o SRMR, and 0.95 o CFI
and TLI. Howe e , some schola s ha e sugges ed hese
cu o s may no be alid in all la en a iable models and
should he e o e no be ea ed as uni e sal golden ules
o o e gene alized in o all con ex s (e.g., Ma sh, Hau
and Wen, 2004; McNeish, An and Hancock, 2018). Mo e
speci ically, ecen s udies ha e shown measu emen
quali y (i.e., he magni ude o he s anda dized ac o
loadings in he model) a ec s he size o he goodness-o - i
alues, so models wi h high-quali y measu emen end o
yield wo se goodness-o - i alues (leading o conclusions
ha he o e all da a–model i is poo ) han models wi h
lowe measu emen quali y—phenomenon e e ed o
as he eliabili y pa adox (Hancock and Muelle , 2011;
o a e iew and simula ion, see McNeish e al., 2018).
McNeish and colleagues (2018) demons a ed ha wi h
poo e measu emen quali y, an RMSEA alue o 0.06 can
indica e a poo i . On he o he hand, hey showed ha
when he measu emen quali y is e y high (s anda dized
ac o loadings o a ound .90), an RMSEA alue o 0.20
can indica e an accep able i . In he cu en s udy, we
ollow McNeish and colleagues’ (2018) ecommenda ion
o epo he s anda dized ac o loadings along wi h he
goodness-o - i indices o con ex ualize and in e p e he
la e and he eby be e assess he da a–model i .
Table 1: Techno-Wo k Engagemen Scale (TechnoWES).
No. om he
o iginal UWES-17
Dimension O iginal UWES P oposed Techno-Wo k Engagemen Scale
(TechnoWES)
VI1aVigo A my wo k, I eel ha I am
bu s ing wi h ene gy.
Techno_VI1.b When I u ilize echnology in my wo k, I
eel ha I am bu s ing wi h ene gy.
VI2aVigo A my job, I eel s ong and
igo ous.
Techno_VI2. I eel s ong and igo ous when I use
echnology in my job.
VI6 Vigo A my wo k I always pe se e e,
e en when hings do no go well.
Techno_VI3. I always pe se e e wi h using echnology in
my wo k, e en when i does no go well.
DE2aDedica ion I am en husias ic abou my job. Techno_DE1.b I am en husias ic abou u ilizing
echnology in my job.
DE3aDedica ion My job inspi es me. Techno_DE2. U ilizing echnology inspi es me in my job.
DE4aDedica ion I am p oud o he wo k ha I do. Techno_DE3. I am p oud ha I u ilize echnology in my
wo k.
AB3aAbso p ion I eel happy when I am wo king
in ensely.
Techno_AB1. I eel happy when I am imme sed in using
echnology in my wo k.
AB4aAbso p ion I am imme sed in my wo k. Techno_AB2b. I am comple ely imme sed in using
echnology in my wo k.
AB5aAbso p ion I ge ca ied away when I’m
wo king.
Techno_AB3. I ge ca ied away when I’m wo king wi h
echnology.
No e: VI = Vigo ; DE = Dedica ion; AB = Abso p ion.
a The i em belongs o he UWES-9.
b The i em belongs o he TechnoWES-3.

Mäkiniemi e al: A No el Cons uc To Measu e Employees’ Technology-Rela ed Expe iences o Well-BeingA . 4, page 6 o 14
Resul s
The χ² and he o he goodness-o - i indices o he CFA
models a e p esen ed in he uppe pa o Table 2. The
h ee- ac o second-o de model (M1 3F-9_1) demons a ed
a be e i han he one- ac o model (M2 1F-9_1), wi h
χ² = 402.34, d = 25, p < 0.001, RMSEA = 0.14, SRMR =
0.078, NNFI = 0.95, and CFI = 0.96. The esul s showed
an accep able i acco ding o he ecommended cu -o
c i e ia o Hu and Ben le (1999), excep o he RMSEA,
which exceeded he cu -o o .06. The χ²/d a io was 16.1,
which exceeded he ecommended (Sch eibe e al., 2006)
cu -o o 3.
The s anda dized ac o loadings o he CFA models
o he TechnoWES-9 a e p esen ed in Table 3 (see he
columns labeled S udy 1). In he hypo hesized h ee-
ac o second-o de model, all ac o loadings we e ≥0.80,
excep o he i em adap ed om he UWES-17, ‘I always
pe se e e wi h using echnology in my wo k, e en when i
does no go well’ (abb e ia ed in he ollowing as Techno_
VI3), which was 0.49. Simila ly, in he one- ac o model,
he loading o ha i em was he lowes a 0.47; whe eas,
all o he loadings we e ≥0.79.
The ac o eliabili ies we e calcula ed wi h he omega
coe icien , which anges om 0 (no eliabili y) o 1
(pe ec eliabili y; B unne e al., 2012; see he las ow
o Table 3).
The o e all le el o echno-wo k engagemen (M = 3.49,
SD = 1.47) did no di e s a is ically signi ican ly be ween
emales (M = 3.52, SD = 1.50) and males (M = 3.40,
SD = 1.41), (706) = –1.04, p = 0.299, which was ue
o he h ee subscales. The o e all le el o echno-wo k
engagemen also did no di e be ween o icials/expe s
(M = 3.50, SD = 1.47), wo ke s (M = 3.34, SD = 1.53),
supe iso s/managemen (M = 3.62, SD = 1.36), senio
Table 2: Goodness-o - i s a is ics o he al e na i e CFA models o he TechnoWES.
Sample Models χ2(d )RMSEA
[90% CI]
SRMR NNFI CFI AIC ∆
χ
2(∆d )p
S udy 1
(n = 729) M1 3F-9_1 402.34 (25) 0.14
[0.13, 0.16]
0.078 0.95 0.96 442.34
(n = 729) M2 1F-9_1 737.61 (27) 0.19
[0.18, 0.20]
0.087 0.91 0.93 773.61 331.27 (2) <0.001
S udy 2
(n = 213) M1 3F-9_2 118.59 (25) 0.13
[0.11, 0.16]
0.069 0.96 0.97 158.59
(n = 213) M2 1F-9_2 278.58 (27) 0.21
[0.19, 0.23]
0.077 0.91 0.93 314.58 155.99 (2) <0.001
No e: TechnoWES = Techno-Wo k Engagemen Scale; M1 = hypo hesized h ee- ac o second-o de model; M2 = al e na i e
one- ac o model.
Table 3: S anda dized ac o loadings o he CFA models o he TechnoWES.
S udy 1 (n = 729) S udy 2 (n = 213)
3- ac o
second-
o de model
1- ac o
model
3- ac o
second-
o de model
1- ac o
model
VI DE AB To al O e all
TechnoWES
VI DE AB To al O e all
TechnoWES
Techno_VI1 0.95 0.92 0.95 0.93
Techno_VI2 0.97 0.93 0.97 0.94
Techno_VI3 0.49 0.47 0.66 0.61
Techno_DE1 0.86 0.82 0.86 0.81
Techno_DE2 0.86 0.82 0.91 0.85
Techno_DE3 0.84 0.82 0.83 0.79
Techno_AB1 0.84 0.82 0.89 0.86
Techno_AB2 0.80 0.79 0.89 0.87
Techno_AB3 0.84 0.82 0.93 0.91
Omega coe icien 0.82 0.88 0.87 0.95 0.94 0.88 0.89 0.93 0.96 0.96
No e: TechnoWES = Techno-Wo k Engagemen Scale; VI = Techno_Vigo ; DE = Techno_Dedica ion; AB = Techno_Abso p ion.
Fo he comple e i ems, see Table 1.
Mäkiniemi e al: A No el Cons uc To Measu e Employees’ Technology-Rela ed Expe iences o Well-Being A . 4, page 7 o 14
managemen (M = 3.72, SD = 1.47), and ‘o he ’ (M = 2.54,
SD = 1.69), F(4, 724) = 1.70, p = 0.148. This was also
he case o he TechnoWES_Vigo and TechnoWES_
Abso p ion subscales. Fo he TechnoWES_Dedica ion
subscale, he o e all e ec was s a is ically signi ican
F(4, 724) = 2.48, p = 0.043, bu he pos hoc es (Sche e)
could no loca e a di e ence. Age did no s a is ically
signi ican ly co ela e wi h he o e all le el o echno-
wo k engagemen ( = –0.04, p = 0.287) o wi h any o he
h ee subscales.
B ie discussion
Using da a om employees o eigh di e en o ganiza ions,
we ound ha he TechnoWES-9 wo ked easonably well
al hough one i em u ned ou o be less han ideal. S udy
1 p o ides suppo o he hypo hesis ha he h ee- ac o
s uc u e o he TechnoWES i s he da a be e han he
one- ac o s uc u e (Hypo hesis 1). S udy 2 es s whe he
his inding can be eplica ed in ano he da ase collec ed
om eache s. Al hough he esponden s in S udy 1
we e no asked o hink abou any speci ic echnology
when answe ing he TechnoWES i ems, in S udy 2, he
pa icipan s we e ins uc ed o hink abou educa ional
echnology. The e o e, S udy 2 adds o S udy 1 by es ing
he scale in a speci ic con ex o wo k- ela ed echnology
use. Fu he mo e, S udy 2 assessed he disc iminan and
con e gen alidi y o he TechnoWES.
S udy 2
Me hod
Pa icipan s and da a collec ion
O iginally, 216 eache s and p incipals om 15 Finnish
schools answe ed a web-based ques ionnai e. All he
p ocedu es we e execu ed ollowing he e hical p inciples
and code o conduc o he Ame ican Psychological
Associa ion. In o med consen was ob ained om he
pa icipan s. The esponse a e was 67%, which a ied om
24–100% ac oss he schools. Th ee p incipals epo ed
hey could no answe he TechnoWES because hey did
no cu en ly use educa ional echnology. Thei esponses
we e excluded om he cu en analyses, so he o al
numbe o esponden s was 213, o whom 160 (75.1%)
we e emale and 53 (24.9%) we e male. The a e age age
o he esponden s was 44.5 (SD = 9.3, ange 23–63) yea s.
The a e age leng h o he wo k expe ience a ied om 0
o 40 yea s, he a e age being 15.6 yea s (SD = 8.9). A o al
o 94 (44.1%) subjec eache s, 110 (51.6%) class oom
eache s, and 9 (4.2%) p incipals pa icipa ed. Rega ding
he school le el, 113 (53.1%) o he esponden s augh
mainly a p ima y school, 44 (20.7%) a lowe seconda y
school, 11 (5.2%) a bo h p ima y and lowe seconda y
school, 6 (2.8%) a bo h lowe seconda y and uppe
seconda y school, and 37 (17.4%) a uppe seconda y
school (2 esponses we e missing).
Measu es
Techno-Wo k Engagemen Scale
The TechnoWES-9 was used. Howe e , he e was one
di e ence: in he in oduc o y ex p eceding he scale, i
was speci ied he wo d ‘ echnology’ e e s o he educa ional
echnology he esponden u ilizes in he o his job.
The e o e, he esponden s we e asked o hink abou
educa ional echnology in pa icula and we e in o med
educa ional echnology e e s o he di e en o ms o ICT
used in eaching.
Technology- ela ed job esou ces
To measu e echnology- ela ed job esou ces, we adap ed
and modi ied he scales and i ems om p e ious s udies,
each o which was measu ed wi h h ee i ems: Technology-
ela ed au onomy (C onbach’s α = 0.67; e.g., ‘I use
educa ional echnology in eaching olun a ily’; Lam e al.,
2010), echnology- ela ed social suppo (α = 0.86; e.g., ‘My
colleagues suppo me i I encoun e di icul ies in using
educa ional echnology’; Lam e al., 2010), echnology-
ela ed sel -e icacy (α = 0.86; e.g., ‘I eel con iden ha I
ha e he necessa y skills in educa ional echnology’; Wang
e al., 2004), and echnology- ela ed alue cong uence
(α = 0.82; e.g., ‘My alues ela ed o educa ional echnology
a e in acco dance wi h he alues which a e emphasized
a his school’; Skaal ik and Skaal ik, 2011). Fo example,
he o iginal i em ‘My educa ional alues a e in acco dance
wi h he alues which a e emphasized a his school’ was
changed o ‘My alues ela ed o educa ional echnology
a e in acco dance wi h he alues which a e emphasized
a his school’ (see he epo by Mäkiniemi e al., 2017).
Technos ess
To measu e echnos ess, we used he 16-i em echnos ess
scale (Salano a e al., 2013) ha was ansla ed om
Spanish o Finnish using a back ansla ion p ocedu e and
modi ied o i he con ex o educa ional echnology.
The CFA o he echnos ess scale suppo ed he ou -
ac o solu ion (χ² = 198.39, d = 98, p < 0.001, CFI = 0.99,
RMSEA = 0.07), which is in line wi h p e ious s udies
(e.g., Salano a e al., 2013). Each o he ou dimensions
was measu ed wi h ou i ems: anxie y (e.g., ‘I eel ense
and anxious when I wo k wi h educa ional echnology’),
a igue (e.g., ‘I is di icul o me o elax a e a day’s wo k
using educa ional echnology’), skep icism (e.g., ‘As ime
goes by, educa ional echnology in e es me less and less’),
and ine icacy (e.g., ‘In my opinion, I am ine icacious
when using educa ional echnology’). The C onbach’s
alpha o he o e all echnos ess was 0.95, and i anged
om 0.83 o 0.92 o he subscales.
Da a analysis
We used he same CFA analy ic p ocedu es as in S udy 1
o assess he dimensionali y and ac o s uc u e o he
TechnoWES-9.
Fu he , ollowing he p ocedu es o Schau eli and
colleagues (2017), we examined he possibili y echno-
wo k engagemen could be measu ed wi h h ee i ems
only because he UWES-3 can be used as an al e na i e
o he UWES-9. The h ee i ems included in he analysis
(TechnoWES-3) we e as ollows: ‘When I u ilize echnology
in my wo k, I eel ha I am bu s ing wi h ene gy’ (Techno_
VI1), ‘I am en husias ic abou u ilizing echnology in my
job’ (Techno_DE1), and ‘I am comple ely imme sed in
using echnology in my wo k’ (Techno_AB2).
Fi s , we assumed bo h e sions o he TechnoWES can
be disc imina ed agains om echnos ess. We es ed his
Mäkiniemi e al: A No el Cons uc To Measu e Employees’ Technology-Rela ed Expe iences o Well-BeingA . 4, page 8 o 14
assump ion by conduc ing wo se s o CFAs (using LISREL
8.80), ha is, one o each e sion o he TechnoWES. In
he i s model, he null model (M0_d ), all i ems we e
speci ied o load on one la en ac o ; whe eas, in he
second model (M1_d ), each (sub)scale ep esen ed a
sepa a e la en ac o (i.e., one TechnoWES ac o and
ou echnos ess ac o s). The di e ence be ween he
wo models was es ed using he ∆
χ
2 s a is ic. Ob aining
a signi ican di e ence would indica e he supe io i y o
i o he second model wi h sepa a e ac o s (M1_d ) and
hence ha he TechnoWES can be disc imina ed om
echnos ess.
Second, we analyzed he in e nal consis ency o bo h
e sions wi h C onbach’s alphas. Thi d, we compu ed
he co ela ion be ween bo h e sions o he TechnoWES
scale o als, he co ela ion o each single i em wi h he
o al o he es o he i ems o each e sion, and he
co ela ions o he single i ems o he TechnoWES-3 wi h
he o al sco e o TechnoWES-9. Fou h, we analyzed
he demog aphic ac o s (age, gende , eache ype) in
ela ion o he TechnoWES-9 and TechnoWES-3, assuming
simila ela ions would be obse ed o bo h. Fi h, we
analyzed whe he he co ela ions o bo h TechnoWES
e sions wi h echnos ess a e simila . Six h, we analyzed
whe he he co ela ions o bo h TechnoWES e sions
wi h echnology- ela ed job esou ces a e simila . Fo
he analyses in s eps wo o six, we used IBM SPSS
S a is ics 25.
Resul s
The χ² and he o he goodness-o - i indices o he CFA
models a e p esen ed in he lowe pa o Table 2. The
h ee- ac o second-o de model (M1 3F-9_2) demons a ed
a be e i han he one- ac o model (M2 1F-9_2), wi h
χ² = 118.59, d = 25, p < 0.001, RMSEA = 0.13, SRMR = 0.069,
NNFI = 0.96, and CFI = 0.97. The esul s showed accep able
i acco ding o he ecommended cu -o c i e ia o Hu
and Ben le (1999), excep o he RMSEA, which exceeded
he cu -o o 0.06. The χ²/d a io was 4.7, exceeding he
ecommended cu -o o 3 (Sch eibe e al., 2006).
The s anda dized ac o loadings o he CFA models o
he TechnoWES a e p esen ed in Table 3 (see columns
labeled S udy 2). As in S udy 1, all loadings we e ≥0.83 in
he h ee- ac o second-o de model and ≥0.79 in he one-
ac o model, excep o he i em ‘Techno_VI3’ (adap ed
om he UWES-17), which was 0.66 in he h ee- ac o
second-o de model and 0.61 in he one- ac o model. The
ac o eliabili ies a e epo ed in he las ow o Table 3.
The o e all le el o echno-wo k engagemen (M = 2.85,
SD = 1.51) did no di e signi ican ly be ween emale
(M = 2.80, SD = 1.48) and male (M = 3.00, SD = 1.60)
esponden s, (211) = 0.84, p = .405; also he means o
he h ee subscales did no di e be ween emales and
males. The o e all le el o he TechnoWES a ied be ween
di e en ypes o eache s, F(2, 210) = 6.24, p = 0.002,
wi h p incipals (M = 4.48, SD = 1.36) sco ing signi ican ly
(p < 0.01) highe han class oom eache s (M = 2.68,
SD = 1.43) and subjec eache s (M = 2.88, SD = 1.54),
as indica ed by he pos hoc Sche e es . This was also
he case o all h ee subscales, wi h p incipals sco ing
highe han class oom and subjec eache s. Age did no
s a is ically signi ican ly co ela e wi h he o e all le el o
he TechnoWES ( = –0.11, p = 0.111) o wi h he subscales
TechnoWES_Vigo ( = –0.13, p = 0.063) and TechnoWES_
Abso p ion ( = –0.04, p = 0.563) bu was nega i ely
associa ed wi h TechnoWES_Dedica ion ( = –0.14,
p = 0.037).
Disc iminan alidi y
The esul s p esen ed in Table 4 indica e he null models
(M0_d ) wi h one gene al well-being ac o did no i he
da a; whe eas, he i o he models wi h sepa a e ac o s
(M1_d ) was su icien al hough no lawless. The i o he
M1_d was supe io o ha o he M0_d (∆
χ
2 = 4315.66;
d = 10, p < 0.001 o he TechnoWES-9 and ∆
χ
2 = 682.01,
d = 10, p < 0.001 o he TechnoWES-3), indica ing ha
bo h TechnoWES e sions can be disc imina ed om
echnos ess.
In e nal consis ency
The C onbach’s alphas o he TechnoWES-9 (0.94) and
TechnoWES-3 (0.81) we e su icien .
Co ela ions be ween bo h e sions
The co ela ion be ween he TechnoWES-9 and
TechnoWES-3 was 0.96, indica ing a sha ed a iance o
92% be ween he e sions. The co ela ion o each single
i em wi h he o al o he es o he i ems anged om
0.62 o 0.71 o he TechnoWES-3 and om 0.58 o 0.84 o
he TechnoWES-9. The co ela ions o he single i ems o
he TechnoWES-3 wi h he o al sco e o he TechnoWES-9
we e 0.84 o Techno_VI1, 0.82 o Techno_DE1, and 0.81
Table 4: CFA i indices o he models assessing disc iminan alidi y o he TechnoWES om echnos ess (S udy 2;
n = 213).
Concep s Models χ2(d )χ2/d RMSEA SRMR CFI NFI ∆
χ
2(∆d )p
TechnoWES-9 and echnos ess M0_d _9 4938.15 (275) 18.0 0.28 0.22 0.71 0.70
M1_d _9 622.49 (265) 2.3 0.08 0.06 0.98 0.96 4315.66 (10) <0.001
TechnoWES-3 and echnos ess M0_d _3 931.94 (152) 6.1 0.16 0.12 0.93 0.92
M1_d _3 249.93 (142) 1.8 0.06 0.05 0.99 0.98 682.01 (10) <0.001
No e: TechnoWES = Techno-Wo k Engagemen Scale; M0 = he null model in which all i ems a e speci ied o load on one gene al
ac o ; M1 = he model wi h sepa a e la en ac o s.
Mäkiniemi e al: A No el Cons uc To Measu e Employees’ Technology-Rela ed Expe iences o Well-Being A . 4, page 9 o 14
o Techno_AB2, indica ing he i ems cons i u ing he
TechnoWES-3 ep esen well he pool o he TechnoWES-9
i ems.
Rela ions wi h demog aphic a iables
The Pea son co ela ion o he TechnoWES-3 wi h age
( = –0.10, p = 0.133) was simila o ha o he TechnoWES-9
( epo ed abo e). The e we e no gende di e ences in
TechnoWES-3: (211) = 1.19, p = 0.234, emales (M = 2.71,
SD = 1.48) and males (M = 3.00, SD = 1.60), which was also
ue o he TechnoWES-9. Rega ding he eache ype,
simila di e ences as hose epo ed o he TechnoWES-9
we e obse ed also o he TechnoWES-3, F(2, 210) = 4.97,
p = 0.008, wi h p incipals (M = 4.22, SD = 1.40) sco ing
signi ican ly (p < 0.05) highe han class oom eache s
(M = 2.62, SD = 1.43) and subjec eache s (M = 2.84,
SD = 1.55), as indica ed by he pos hoc Sche e es s.
Rela ions wi h echnos ess
Table 5 shows ha all co ela ions o echno-wo k
engagemen wi h echnos ess we e nega i e, as expec ed:
o e all echnos ess = –0.39/–0.37, Technos ess_
Scep icisim = –0.42/–0.43, Technos ess_Fa igue
= –0.24/–0.22, Technos ess_Anxie y = –0.32/–0.29,
and Technos ess_Ine icacy = –0.36/–0.34, o he
TechnoWES-3 and TechnoWES-9, espec i ely. Gene ally,
he co ela ions we e e y simila , wi h an a e age
di e ence o only 0.02.
Rela ions wi h echnology- ela ed job esou ces
As can be seen om Table 5, all co ela ions wi h
echnology- ela ed job esou ces we e posi i e: echnology-
ela ed au onomy = 0.40/0.43, echnology- ela ed social
suppo = 0.22/0.23, echno-e icacy = 0.46/0.47, and
echnology- ela ed alue cong uence = 0.36/0.38, o he
TechnoWES-3 and TechnoWES-9, espec i ely. Gene ally,
he co ela ions we e e y simila , wi h an a e age
di e ence o 0.02.
B ie discussion
We ound he TechnoWES-9 wo ked easonably well;
al hough, again, one i em u ned ou o be somewha
p oblema ic. S udy 2 p o ides u he suppo o
ou hypo hesis ha he h ee- ac o s uc u e o he
TechnoWES i s he da a be e han he one- ac o
s uc u e (Hypo hesis 1). Also, hypo heses 2–4 we e
suppo ed. Bo h e sions o he scale can be disc imina ed
om he echnos ess scale, and he e was a mode a e
nega i e co ela ion be ween echno-wo k engagemen
and echnos ess. The sizes o he co ela ions we e e y
simila in bo h e sions. The e was a posi i e mode a e
co ela ion be ween echno-wo k engagemen and all
ou echnology- ela ed job esou ces, which applies o
bo h scale e sions. The sizes o he co ela ions we e e y
simila . As assumed, he TechnoWES-9 and TechnoWES-3
we e highly co ela ed, wi h a sha ed a iance o 92%.
Finally, he ela ionships o he demog aphics wi h
echno-wo k engagemen we e e y simila o bo h he
h ee-i em and nine-i em e sions o he scale.
Gene al discussion
The aim o he cu en s udy was o p esen a no el
cons uc called echno-wo k engagemen and o
assess he ac o ial, di e gen , and con e gen alidi y
o he new TechnoWES. The e is a clea need o a new
cons uc because he e is a lack o scales ocusing on he
posi i e well-being expe iences ega ding echnology a
wo k. One excep ion is a scale assessing low expe iences
in he con ex o ICT wo k (Rod íguez-Sánchez e al.,
2008).
In line wi h he p e ious indings and es ablished hinking
on wo k engagemen , we i s hypo hesized he h ee- ac o
s uc u e o he TechnoWES-9 would i he da a be e han
he one- ac o s uc u e; he esul s o he wo samples
suppo ed his hypo hesis. The main inding indica es
echno-wo k engagemen can be de ined as a second-
o de ac o e lec ed in employees’ echnology- ela ed
Table 5: Pea son’s co ela ions o he TechnoWES wi h echnos ess and echnology- ela ed job esou ces (S udy 2;
n = 213).
TechnoWES-9 TechnoWES-3 Techno_
Vigo
Techno_
Dedica ion
Techno_
Abso p ion
Technos ess –0.37*** –0.39*** –0.34*** –0.39*** –0.29***
Technos ess_Scep icism –0.43*** –0.42*** –0.39*** –0.46*** –0.35***
Technos ess_Fa igue –0.22** –0.24*** –0.20** –0.25*** –0.16*
Technos ess_Anxie y –0.29*** –0.32*** –0.27*** –0.31*** –0.22**
Technos ess_Ine icacy –0.34*** –0.36*** –0.31*** –0.34*** –0.29***
Technology- ela ed au onomy 0.43*** 0.40*** 0.38*** 0.45*** 0.36***
Technology- ela ed social suppo 0.23*** 0.22** 0.21** 0.22** 0.22**
Techno-e icacy 0.47*** 0.46*** 0.44*** 0.41*** 0.45***
Technology- ela ed alue cong uence 0.38*** 0.36*** 0.33*** 0.36*** 0.36***
No e: TechnoWES = Techno-Wo k Engagemen Scale.
*** p < 0.001; ** p < 0.01; * p < 0.05.