The Development and Validation of the “Attitudes Towards

Digitalization” (Att-Dig) Questionnaire

Daniel Nierwzol

1,2

, Jan P. Ehlers

and Thomas Ostermann

Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany

SRH University of Applied Health Sciences, Gera, Germany

Didactics and Education Research in the Health Sector, Faculty of Health, Witten/Herdecke University, Witten, Germany

Keywords: Digitalization, Attitudes, Questionnaire, Assessment, Validation.

Abstract: Attitudes towards digitalization play a major role in almost all areas of human interaction including the health

care system. Unfortunately, existing assessments and respective instruments on attitudes towards

digitalization are often negatively framed, while balanced and broader approaches exist only marginally. The

aim of this work was therefore to develop an assessment instrument from a self-generated item pool capturing

a broad range of aspects of attitudes towards digitalization. Items were answered in an online survey by a total

of 214 participants (mean age: 30.8±14.4 years 56,1% female). A principal component analysis was

performed and 5 subscales “Digitalisation and Social Life” (5 items, Cronbach's alpha=0.789),” Digitalisation

and Loss of Control” (4 items, Cronbach's alpha=0.817), ” Digitalisation, Knowledge and Education” (4 items,

Cronbach's alpha=0.791), ” Digitalisation and Gain of freedom” (3 items, Cronbach's alpha=0.749), and ”

Digitalisation, Equity and Prosperity” (3 items, Cronbach's alpha=0.699) were extracted covering 63.5% of

the item variance, showing a sufficient internal consistency of the subscales. There were significant

differences for some of the subscales with regard to gender, age, and education. Only weak and non-significant

correlations were found with respect to the subscales “self-efficacy”, “optimism”, and “pessimism” of the

SWOP-K9 questionnaire. Thus, in sum, although there is a need for further research, the Att-Dig is a sound

survey instrument to economically assess the attitude towards digitalisation. It can be used in different areas

of public life and health care and is easy and quick to answer.

1 INTRODUCTION

Digitalization is one of the most important and

powerful trends affecting people’s lives as well as the

development of organizations and societies in the 21

century (Parviainen et al., 2017). With the growing

possibilities offered by digital technologies an

increasing number of digital services has emerged

and is being offered in all areas of life creating

transformations, new realities and opportunities for a

better life (Annoni et al. 2023). At the same time,

people experience digitalization as an enormous

challenge that requires extensive adaptation

processes that often lead to excessive demands, self-

doubt and anxiety (Hassani et al., 2021; Dabić et al.,

2023; Teepe et al., 2023).

In the field of healthcare, digitalization very early

was seen as a potential element of a utopia of a fair

and patient-oriented healthcare system. And even if

the first attempts at a digital apparatus for finding

medicines failed in the 19th century (Ostermann,

2019), there was great euphoria a hundred years later

when the first computers seemed to revolutionize the

doctor-patient relationship (Ostermann, 2023).

Pitkienen & Kenzelmann (1966) wrote about

computers used by physicians: “Technology does not

stultify the doctor, but increases his knowledge by

forcing him to deal with a greater number of

diagnostic options”. Or almost at the same time on a

more abstract level: “If physicians are to interact with

computers, the consequences of this behavior must be

reinforicing” (Slack et al., 1970).

However, it is not only the expected benefits but

also the general attitude towards the corresponding

technologies and their inherent transformations that

influence their acceptance by actors in the health care

system (Rivera Romero et al., 2024), which also

includes the patient’s views and experiences of this

topic (Kulzer et al., 2022; Gybel et al., 2024). So far,

Niewrzol, D., Ehlers, J. P. and Ostermann, T.

The Development and Validation of the “Attitudes Towards Digitalization” (Att-Dig) Questionnaire.

DOI: 10.5220/0013152700003911

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 2: HEALTHINF, pages 523-529

ISBN: 978-989-758-731-3; ISSN: 2184-4305

523

according to (Cresswell et al., 2023) this might also

include “unmeasurable” dimensions.

Thus, attitudes towards digital technologies play

an important role and should be explored in more

detail. Unfortunately, existing assessments on

attitudes towards digitalization are often negatively

framed and focus on fears or dystopian elements,

while balanced and broader approaches exist only

marginally. The picture is similar at the level of

assessment instruments: the only validated scale, the

Digitalisation Anxiety Scale (DAS), focuses on fear

of digitalization (Pfaffinger et al., 2021).

The aim of this work is therefore to develop an

assessment instrument from a self-generated item

pool that should be able to capture a broad range of

aspects of attitudes towards digitalization.

2 MATERIAL AND METHODS

In order to capture a broad range of aspects of

attitudes towards digitalisation, items were generated

in a psychological assessment class on test

construction with 38 students of psychology at the

University of Witten/Herdecke, which then were

handed over to an in-house expert panel consisting of

two health care experts, two psychologists and one

computer scientist, who finally selected 19 items,

which could be answered on a 6-point Likert scale

from 1 = absolutely disagree to 6 = absolutely agree.

From June to September 2018, participants aged

at least 18 years were recruited for the survey through

direct contact as well as through distribution on social

media groups. For data collection, the online survey

tool SoSciSurvey was used. Participants who were

not able to use the online survey, were allowed to

answer in a paper-pencil questionnaire version.

Ethical approval was obtained from the Ethical

Committee of Witten/Herdecke University (ID: S-

318/2023; approved on 19 December 2023).

Principal components analysis (PCA) was used to

analyse relations between the items and to detect

potential factors. Beforehand, Kaiser-Meyer-Olkin

criterion: (KMO≥ 0.50) and Bartlett test of sphericity

were calculated to determine whether the items were

appropriate for PCA. To determine the number of

reliable items, Item communalities were calculated. A

communality value of 0.5 was chosen as cutoff value

above which items were included in an exploratory

factor analysis (Schreiber, 2021).

To obtain a solution with independent factors

Varimax rotation was applied to arrive at a solution

explaining the maximum amount of variance. Kaiser-

Gutman criterion (Eigenvalue > 1) and Scree-Plot

investigation was used to categorize a factor as

meaningful. Internal consistency was examined by

calculating Cronbach’s alpha for the single factors.

Internal reliability was assessed by means of item-

factor correlations.

Sub-scale means were calculated for the total

sample and the groups based on the socio-

demographic parameters gender, age, education and

income. For that purpose one-factorial Analysis of

Variance (ANOVA) was calculated to detect

significant differences based on a level of

significance of 5%.

Finally correlations with the subscales of the

SWOP-K9 questionnaire (Scholler et al., 1999) were

performed. This short questionnaire consists of 9

items measuring “self-efficacy” (5 items),

“optimism”, and “pessimism” (2 items each). Due to

the fact that optimism and self-efficacy is positively

associated with an "affinity for technology" (Edison

et al., 2003) this questionnaire was used for external

validation.

3 RESULTS

3.1 Sample

A total of N=214 participants aged between 18 and 92

years (Mean age: 30.8± 14.4 years) of whom 120

(56.1%) were female completed the survey and were

included in the evaluation.

Table 1: Sociodemographic data of the total sample.

Gender

Male

Female

94 (43.9%)

120 (56.1%)

Age (years)

Mean ± SD

Median

30.8 ± 14.4

24.0

Relationship status

In a relationship/married

Single and other relationship

129 (60.3%)

87 (39.7%)

School Education

High-school

Other schools

173 (80.8%)

41 (19.2%)

Monthly income

<2,000 €

> 2,000 €

No answe

119 (55.6%)

87 (36.0%)

18 (8.4%)

Raised in (inhabitants)

<100,000

>100,000

146 (68.2%)

68 (31.8%)

Job area

Health & Social Sector

Others (Education, Finance,

R&D, …)

106 (49.5%)

108 (50.5%)

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Almost half of them (n=106; 49.5%) stated to

work in the health or social sector. More than half of

the participants were married or in a relationship

(n=129; 60.3%), while 81 participants (37.9%) stated

to be single. 4 out of 5 participants (n=173; 80.8%)

had a high school exam and almost one third of the

sample (n=75; 35.0%) had a monthly income below

1,000 €, while the second third of the sample had a

monthly income between 1,000€ and 3,000€ (n=73;

34.2%). One hundred forty-six participants

(68.2%)

grew up in a small or middle town (< 100,000 inhabitants)

(Table 2). A more detailed description of the sample is

given in Niewrzol and Ostermann (2024).

3.2 Principal Component Analysis

A KMO of .850 and a significant Bartlett’s test of

sphericity (χ2(171) = 1493.5 p < .001), confirmed that

the items were suitable for an exploratory factor

analysis. This was also confirmed by the

communality values which were located between

0.565 and 0.753 (Figure 1).

Figure 1: Communalities of the items in order of their

appearance in tables 2-6.

After six iterations of Varimax rotation, PCA

found five main components with Eigenvalues > 1

explaining 63.5% of the variance. Visual inspection

of the screeplot suggested at least 4 dimensions as

sufficiently meaningful, as after the fourth factor the

amount of the slope in the scree plot changes

significantly downwards (Figure 2).

Figure 2: Screeplot of the factors.

The first factor explained 31.6% of the variance

and included 5 items dealing with the improvement

of social life through digitalisation, i.e.

“Digitalisation improves my relationship with

people” or “Digitalisation creates connectedness”.

Factor loadings of the items ranged between 0.656

and 0.728 with only one side loadings >0.3 for the

fitht factor for the item “Digitalisation can make

relationships much more intense”. With Cronbach’s α

of 0.789 the internal consistency of this factor can be

considered as very good. Correlation of the items with

the factor ranged between 0.498 and 0.636. The scale

was named “Digitalisation and Social Life” (DSL).

Table 2: Factor 1 (DSL): Results of the PCA, reliability and

Items parameters (M= mean; SD = standard deviation; FL

= Factor loading; r(I-F) = Correlation of the items with the

factor). Scale range from 1= completely disagree to 6=

completely agree).

Items AD01 to AD05

Digitalization…

M ±

FL r(I-F)

…enables me to better commu-

nicate what is important to me

3.03 ±

1.27

.728 .636

…improves my relationship with

people.

2.91 ±

1.37

.718 .556

… creates connectedness. 3.46 ±

1.16

.696 .498

…enables me to get more socially

involved.

3.02 ±

1.37

.663 .566

…can make relationships much

more intense.

2.74 ±

1.36

.656 .586

The second factor explained 11.0% of the

variance and included 4 items dealing with the loss of

control through digitalisation, i.e. “Digitalization

means a loss of self-determination”.

The Development and Validation of the “Attitudes Towards Digitalization” (Att-Dig) Questionnaire

525

Table 3: Factor 2 (DLC): Results of the PCA, reliability and

Items parameters (M= mean; SD = standard deviation; FL

= Factor loading; r(I-F) = Correlation of the items with the

factor). Scale range from 1= completely disagree to 6=

completely agree).

Items AD06 to AD09

Digitalization…

M ±

FL r(I-F)

… leaves me at its mercy 2.96 ±

1.28

.817 .637

… means a loss of control 3.17 ±

1.50

.787 .716

… makes me helpless 2.35 ±

1.14

.750 .587

… means a loss of self-

determination

3.34 ±

1.34

.745 .628

Factor loadings of the items ranged between 0.745

and 0.817. With Cronbach’s α of 0.817 the internal

consistency of this factor can be considered as

excellent. Correlation of the items with the factor

ranged between 0.587 and 0.716. The scale was

named “Digitalisation and Loss of Control” (DLC).

The third factor explained 8.6% of the variance

also including 4 items dealing with the loss of control

through digitalisation, i.e. “Digitalization increases

the collective knowledge”. Factor loadings of the

items ranged between 0.656 and 0.821. With

Cronbach’s α of 0.791 the internal consistency of this

factor can be considered as very good. Correlation of

the items with the factor ranged between 0.578 and

0.627. The scale was named “Digitalisation,

Knowledge and Education” (DKE).

Table 4: Factor 3 (DKE): Results of the PCA, reliability and

Items parameters (M= mean; SD = standard deviation; FL

= Factor loading; r(I-F) = Correlation of the items with the

factor). Scale range from 1= completely disagree to 6=

completely agree).

Items AD10 to AD13

Digitalization…

M ±

FL r(I-F)

…increases the collective

knowledge

4.45 ±

1.209

.821 .617

… expands the collective

memory

3.99 ±

1.372

.756 .578

… creates education for all 3.91 ±

1.283

.695 .585

… promotes freedom of

expression.

3.93 ±

1.284

.656 .627

The fourth factor explained 6.4% of the variance

and includes 3 items dealing with the gain of freedom

through digitalisation, i.e. “Digitalisation takes work

out of my hands”. Factor loadings of the items ranged

between 0.623 and 0.831. With Cronbach’s α of 0.749

the internal consistency of this factor can be

considered as very good.

Table 5: Factor 4 (DGF): Results of the PCA, reliability and

Items parameters (M= mean; SD = standard deviation; FL

= Factor loading; r(I-F) = Correlation of the items with the

factor). Scale range from 1= completely disagree to 6=

completely agree).

Items AD14 to AD16

Digitalization…

M ±

FL r(I-F)

…takes work out of my hands 4.12 ±

1.20

.831 .537

… improves my daily life 4.19 ±

1.07

.731 .654

… means freedom. 3.55 ±

1.30

.623 .554

Correlation of the items with the factor ranged

between 0.537 and 0.654. The scale was named

“Digitalisation and Gain of freedom” (DGF).

The fifth and last factor explained 6.4% of the

variance and includes 3 items dealing with equity and

prosperity through digitalisation, i.e. “Digitalisation

will create prosperity for everyone”.

Table 6: Factor 5 (DEP): Results of the PCA, reliability and

Items parameters (M= mean; SD = standard deviation; FL

= Factor loading; r(I-F) = Correlation of the items with the

factor). Scale range from 1= completely disagree to 6=

completely agree).

Items AD17 to AD17

Digitalization…

M ±

FL r(I-F)

… solves our environmental

problems

2.34 ±

1.21

.791 .514

… creates equity and justice 2.15 ±

1.11

.698 .531

… will create prosperity for

everyone

2.52 ±

1.17

.687 .503

Factor loadings of the items ranged between 0.687

and 0.791. With Cronbach’s α of 0.699 the internal

consistency of this factor can be considered as good.

Correlation of the items with the factor ranged

between 0.503 and 0.531. The scale was named

“Digitalisation, Equity and Prosperity” (DEP).

Based on the results of the PCA, the

corresponding scale means were calculated and

examined for differences in relation to socio-

demographic aspects and for correlations with the

SWOP-K9 subscales.

Figures 3-8 show the values of the Att-Dig

subscales with respect to the sociodemographic

subgroups.

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Figure 3: Mean values of the Att-Dig subscales with respect

to gender (Error bars show the 95% confidence interval).

Figure 4: Mean values of the Att-Dig subscales with respect

to age (Error bars show the 95% confidence interval).

Figure 5: Mean values of the Att-Dig subscales with respect

to relationship status (Error bars show the 95% confidence

interval).

Figure 6: Mean values of the Att-Dig subscales with respect

to educational degree (Error bars show the 95% confidence

interval).

Figure 7: Mean values of the Att-Dig subscales with respect

to job sector (Error bars show the 95% confidence interval).

Figure 8: Mean values of the Att-Dig subscales with respect

to socialisation (Error bars show the 95% confidence

interval).

In particular significant differences were found in

the subscale DSL with respect to age (F=6.052;

p=0.015), DLC (F= 6.104; p= 0.014) and DGF

(F=6.252; p= 0.013) with respect to educational

degree, and DKE (F= 4.128; p= 0.043), DGF (F=

4.506; p=0.0035) and DEP (F= 6.283; p= 0.013) with

respect to gender.

The Development and Validation of the “Attitudes Towards Digitalization” (Att-Dig) Questionnaire

527

Table 8 shows the correlation with the SWOP-K9

subscales “self-efficacy”, “optimism”, and

“pessimism”.

Table 8: Correlations with the SWOP-K9 subscales “self-

efficacy” (SE), “optimism” (OP), and “pessimism” (PM).

Att-DIG/SWOP subscales SE OP PM

DSL 0.012 -0.047 -0.011

DLC -0.152 -0.041 -0.016

DKE 0.06 0.125 -0.093

DGF 0.086 0.13 -0.034

DEP 0.08 0.024 -0.022

All correlations were weak and only the

correlation of “Digitalisation and Loss of Control”

(DLC) with “Self efficacy” (SE) was significant

(p=0.026) and negative (r=-0.152) which due to the

nature of the scales and their meaning is rather

evident.

4 DISCUSSION

Digitalization is becoming increasingly important in

every society and in every area of society and is

increasingly determining people's everyday lives.

This applies in particular to the healthcare system,

where digitalization is playing an increasingly

important role in patient care such as in projects like

“Open notes” (O’Neill et al, 2021) or when

introducing the “Electronic Health Card” in the

German health care system (Jorzig et al., 2020)

Thus there is an urgent need for research on scales

and questionnaires quantifying attitudes towards

digitalisation. This article aims at contributing to this

field of research and summarizes first results of the

development and validation of the Attitudes towards

Digitalisation Questionnaire (Att-Dig). Our analysis

yielded a stable and convergent five-factor solution

that exhibited convincing validity with values of

Cronbach's alpha between 0.699 and 0.817).

Correlations with the SWOP-K9 were

neglectable, while differences especially with respect

to gender and education need further investigations.

Our study has some limitations. Firstly, the item

selection did not rely on conceptual theoretical

models as already been discussed in (Niewrzol &

Ostermann, 2024). This however does not directly

imply that the questionnaire is invalid, but futher

inspection of the results is recommended in particular

due to the fact that digitalisation has advanced in the

last 5 years. However, as with other constructs, it can

be assumed that the factorial structure remains the

same even after six years even if attitudes towards

digitalization may have changed. Thus this work

cleary focusses mainly on the validity of the scales

rather than discussing the outcomes of the survey. In

particular critical items, e.g. "Digitization increases

the collective knowledge" or "Digitization does/does

not create equity and justice" have not be discussed

based on this data set but may be subject to future

studies.

In addition, technical readiness and socio-

organizational factors should also be taken into

account in further studies when measuring the

attitudes towards digitization in a given context and

should be surveyed in studies using the Att-Dig. This

in particular is relevant in the health context, in which

previous surveys have shown very high approval

ratings for digitization (Veikkolainen et al., 2023)

Secondly the sample size in the present study is

borderline. Although communality values as given in

this study according to (Schreiber, 2021) justify a

sample size of around 200, Costello & Osborne

(2009) argue that the required sample size of a factor

analytical approach should at least have a subject to

item ratio of 10:1 but preferable a ratio of 20:1 to

avoid an unstable factor structure. Thus 214

participants can be regarded as adequate just at the

border of a sufficient sample size.

From a methodological point of view, the use of

an exploratory factor analysis can be criticized and in

the construction of psychological constructs it is often

suggested to do a confirmatory factor analysis instead

(Schreiber, 2019). Moreover, the use of a PCA

instead of e.g. a principal axis factoring (PAF)

approach is also still a matter of discussion (Niewrzol

& Ostermann, 2024). Here, a simulation study found

that PCA loadings might be better approximations of

the true factor loadings than the loadings produced by

PAF (de Winter et al. 2016). Thus, although this

discussion is still ongoing, we believe that our

approach has produced sufficiently reliable scales,

which was confirmed not least by Horn's parallel

analysis.

Nevertheless, not only for methodological reason

i.e. sample size, have we suggested replicating the

survey on other samples such as schoolchildren or

older people or in other health related contexts in

order to analyse measurement invariances which was

not possible in the present study.

5 CONCLUSION

In sum, although there is a need for further research,

the Att-Dig is a sound survey instrument to

economically assess the attitude towards

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digitalisation. It can be used in different areas of

public life and health care and is easy and quick to

answer.

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