ATI Scale
Affinity for Technology Interaction Scale
Questionnaire Scale to Access Affinity for Technology Interaction
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What does affinity for technology interaction mean?
The 9-item affinity for technology interaction (ATI) scale is designed
to assess a person’s tendency to actively engage in intensive
technology interaction — or to avoid it. ATI can be seen as a core
personal resource for users’ successful coping with technology.
ATI is a key facet of user personality and therefore essential when assessing user characteristics in research on technology interaction. Use cases include the identification of sample biases in usability tests or adapting interfaces or technology-related trainings to ATI values in the user group.
Do you have any questions regarding the scale? Just drop us a line at ati@imis.uni-luebeck.de. We are very interested to get in contact and look forward to answer your questions!
ATI is a key facet of user personality and therefore essential when assessing user characteristics in research on technology interaction. Use cases include the identification of sample biases in usability tests or adapting interfaces or technology-related trainings to ATI values in the user group.
Do you have any questions regarding the scale? Just drop us a line at ati@imis.uni-luebeck.de. We are very interested to get in contact and look forward to answer your questions!
Download the Scale
Try out the ATI Scale
Background
Users differ regarding their Affinity for Technology Interaction
People differ in how much they like to interact with technology. On
the one extreme, some people really like to explore technical
systems and even love to deal with problems. On the other extreme,
some people just dislike it.
This is not surprising, but affinity for technology interaction (ATI) has important consequences for general research models of user-technology interaction, user experience, technology acceptance, and practical development of technical systems.
This is not surprising, but affinity for technology interaction (ATI) has important consequences for general research models of user-technology interaction, user experience, technology acceptance, and practical development of technical systems.
Differences in ATI have Consequences for Research and Development
For example, if you want to understand why users differ in their
successful adaptation to a new system, assessing ATI can provide
insights and help in model and system optimization. Likewise, user
evaluation can be skewed by participants who like to interact with
technology and can thus easily overcome usability problems which
would pose stumbling blocks for other users.
Thus, assessing ATI is as relevant as assessing age and gender if you want to characterize your user sample in technology interaction research. But how can you assess affinity for technology interaction?
Thus, assessing ATI is as relevant as assessing age and gender if you want to characterize your user sample in technology interaction research. But how can you assess affinity for technology interaction?
The ATI-Scale
People differ in how much they like to interact with technology. On
the one extreme, some people really like to explore technical systems
and even love to deal with problems. On the other extreme, some people
just dislike it.
This is not surprising, but affinity for technology interaction (ATI) has important consequences for general research models of user-technology interaction, user experience, technology acceptance, and practical development of technical systems.
This is not surprising, but affinity for technology interaction (ATI) has important consequences for general research models of user-technology interaction, user experience, technology acceptance, and practical development of technical systems.
Presentation
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Download the scale for use in your project. Please contact us if you
want to translate the scale into another language. We’re always happy to
add another language here and
created an Excel sheet
that should assist in the translation process.
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English
Version
Version
German
Version
Version
Short Scale Version
There is also a short version available (ATI-S), consisting of 4 items
(subset of the full 9-item ATI scale).
Use it only if you absolutely, positively have to reduce the number of
items.
In all other cases, the 9-item version should be used, as it has
superior psychometric quality.
More on the construction of the short scale
is available in this paper.
The
data set and the R scripts used for its construction are also
available.
If you have the full scale in another language, use items Items 1, 2, 6,
and 8 of the full version (note that items 6 and 8 have to be reversed
for analysis).
Use the scale
Downloading the Scale
You can download the scale
above
in different languages.
Conditions of Use
The scale is free to use. However, we appreciate it if you cite the
scale as:
Franke, T., Attig, C., & Wessel, D. (2019). A Personal Resource for Technology Interaction: Development and Validation of the Affinity for Technology Interaction (ATI) Scale. International Journal of Human–Computer Interaction, 35(6), 456-467, DOI: 10.1080/10447318.2018.1456150
and send us a short message. We are always happy to hear how it is used and whether it was useful.
Franke, T., Attig, C., & Wessel, D. (2019). A Personal Resource for Technology Interaction: Development and Validation of the Affinity for Technology Interaction (ATI) Scale. International Journal of Human–Computer Interaction, 35(6), 456-467, DOI: 10.1080/10447318.2018.1456150
and send us a short message. We are always happy to hear how it is used and whether it was useful.
Calculating Scale Means
When analyzing participants’ responses, code the answers to the items
with 1 to 6, but reverse code the responses to items 3, 6 and 8 (see
analysis information in the Word/PDF document of the scale). The
user’s ATI score is the mean of the 9 ATI item values (6 original
values and 3 reverse coded items).
Assessing Cronbach’s alpha is recommended prior to calculating the means.
Assessing Cronbach’s alpha is recommended prior to calculating the means.
Analysis and Interpretation
For analysis/interpretation, we strongly recommend using, e.g.,
correlations/regression analyses or including ATI as a covariate, and
avoid creating groups (e.g., doing a split at 3.5 or a median split,
followed e.g., by t-Tests). Reasons being:
- ATI was developed to cover the whole range of affinity for technology in a population, without floor or ceiling effects. It’s a continuous variable that does not lend itself to artificial grouping. There are no sudden changes which would indicate specific subgroups of affinity for technology interaction.
- You’d lose a lot of information if you split the sample, e.g., a person whose ATI score is slightly above/below the «magical» line would be treated the same as someone in the extremes.
- «Average» ATI varies between populations. Groups which are self-selected for their interest in technology (e.g., computer scientists) will have higher ATI values, so a person might be below average in the sample but above average in the population.
Data for reanalysis
If you are interested in reanalyzing the data, we have made the data
from the MTurk study (reported in Franke, Attig, & Wessel, 2019)
available for reanalysis. The
zipped file contains the data as .csv file and the Codebook via
PDF.
The data was also analyzed by Lezhnina and Kismihók. See their the
paper: Lezhnina, O., & Kismihók, G. (2020). A multi-method psychometric
assessment of the affinity for technology interaction (ATI) scale.
Computers in Human Behavior Reports, 1, 100004.
https://doi.org/10.1016/j.chbr.2020.100004
If you have any questions,
please contact us.
Download the MTurk data.zip file
Who we are
Prof. Dr. Thomas Franke
Thomas Franke is a professor of Engineering Psychology and
Cognitive Ergonomics at the University of Lübeck. He received his
PhD in 2014 from Chemnitz University of Technology. He is
particularly interested in user diversity and a resource
perspective on user-technology interaction.
Dr. Christiane Attig
Christiane Attig is an engineering and cognitive psychologist at
Chemnitz University of Technology, where she received her Master
of Science in Psychology in 2016. Besides research focusing on
user state detection in human-computer interaction, she is also
particularly interested in user diversity and user interaction
with activity trackers.
Dr. Daniel Wessel
Daniel Wessel is a researcher at the Institute for Multimedia and
Interactive Systems at the University of Lübeck. His research
interests include mobile media, research methods and evaluation,
and especially the interaction between psychology and computer
technology.
Get in touch
We are very interested in continuing to evaluate and improve the
scale. If you plan to use the scale, we would be very happy to learn
more about your research topics. If you are looking for a localized
version, we are also very happy to provide some support for
translations (e.g., regarding the procedure, the translation itself
would need to be done by your organization). You can reach us at
ati@imis.uni-luebeck.de.
Please also contact us if you have any questions.
Contact us