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!
Background
Users differ regarding their
Affinity for Technology Interaction
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.
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?
The ATI-Scale
The 9-item ATI scale is a unidimensional, short and reliable scale for assessing affinity for technology interaction. It is grounded in the established psychological construct need for cognition and is supported by multiple studies with over 1500 participants. For more information on the scale and its construction, see Franke, Attig, & Wessel (2018; empirical validation) or Attig, Wessel, & Franke (2017; development, references below). A presentation of the scale at HFES Europe 2017 can also be watched below.
Presentation
References
- Attig, C., Wessel, D., & Franke, T. (2017). Assessing personality differences in human–technology interaction: An overview of key self-report scales to predict successful interaction. In C. Stephanidis (Ed.), HCI International 2017 – posters’ extended abstracts, part I, CCIS 713 (pp. 19–29). Cham, Switzerland: Springer International Publishing AG. doi:10.1007/978-3-319-58750-9_3 [get free preprint]
- 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 [get free preprint]
Download
Short Scale Version
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
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.
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.
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.
Get in Touch
Data for Reanalysis
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
Who we are
Prof. Dr. Thomas Franke
Thomas Franke is a professor of Engineering Psychology and Cognitive Ergonomics at 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.
Christiane Attig, M.Sc.
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 University of Lübeck. His research interests include mobile media, research methods and evaluation, and especially the interaction between psychology and computer technology.