Assessing Personality Differences in Human-Technology Interaction: An Overview of Key Self-report Scales to Predict Successful Interaction

Abstract

For a comprehensive understanding of user diversity, a reliable and valid assessment of stable user characteristics is essential. In the field of human-technology interaction, a plethora of personality-related constructs linked to the experience of and interaction with technical systems has been discussed. A key question for researchers in the field is thus: Which are the key personality concepts and scales for characterizing inter-individual differences in user technology interaction? Based on a literature review and citation analysis, a structured overview of frequently used technology-related personality constructs and corresponding self-report scales is provided. Changes in the popularity and content of scales and concepts that occured over time as well as overlap between constructs and scales are discussed to facilitate scale selection.

Full text

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Assessing Personality Differences in Human-technology Interaction: An
Overview of Key Self-report Scales to Predict Successful Interaction
Christiane Attig1*, Daniel Wessel2, & Thomas Franke2
1Department of Psychology, Cognitive and Engineering Psychology, Chemnitz University of Technology, Chemnitz, Germany
christiane.attig@psychologie.tu-chemnitz.de
2Institute for Multimedia and Interactive Systems, Engineering Psychology and Cognitive Ergonomics, Universität zu Lübeck,
Lübeck, Germany
wessel@imis.uni-luebeck.de
franke@imis.uni-luebeck.de
*Corresponding author
ABSTRACT
For a comprehensive understanding of user diversity, a reliable and valid assessment of stable user
characteristics is essential. In the field of human-technology interaction, a plethora of personality-
related constructs linked to the experience of and interaction with technical systems has been
discussed. A key question for researchers in the field is thus: Which are the key personality concepts
and scales for characterizing inter-individual differences in user technology interaction? Based on a
literature review and citation analysis, a structured overview of frequently used technology-related
personality constructs and corresponding self-report scales is provided. Changes in the popularity and
content of scales and concepts that occur in the literature over time as well as overlap between
constructs and scales are discussed to facilitate scale selection.
Keywords: human-technology interaction, human-computer interaction, personality assessment
Note: This is the authors’ version of a work accepted for publication in the proceedings of the 14th International
Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2017, held in Vancouver, Canada, in July
2017. Changes resulting from the publishing process may not be reflected in this document. The final publication
is available at Springer via http://dx.doi.org/10.1007/978-3-319-58750-9_3
Cite as: 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

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1 INTRODUCTION
We are living in a world that is increasingly pervaded by technology. The first personal computers from
the late 1970s and early 1980s were limited in their fields of application (e.g., word processing,
programming, gaming). Today, computers are used for an enormous breadth of tasks in professional
and private activities, from designing, learning, knowledge sharing, to leisure activities, social
networking or videoconferencing. With the increasing number of these functions, users have more
degrees of freedom regarding use of devices. Computers, especially mobile devices like tablets, have
also tapped into user groups who would rarely use personal computers (e.g., seniors, small children),
resulting in a higher user diversity. At the same time, the speed of technological innovation is steadily
increasing. Thus, users need to learn to cope with new technology at a faster pace, and understanding
how to optimally utilize current technology becomes more and more relevant. Consequently, people do
not only differ in their technology usage but also in their success with utilizing new technology. Hence,
it becomes increasingly important to take the individual fit between persons and technical systems in
the focus of engineering psychology research.
Within human factors and engineering psychology, theory and research are typically focused on
what users have in common in terms of experience and behavior [52]. That is, there has been a lack of
addressing stable inter-individual psychological differences of human operators. However, in recent
years, the call for a more comprehensive integration of personality differences and theories into human
factors research has grown (e.g., [41, 52, 53]). Personality traits that reflect inter-individual differences
in human-technology interaction are increasingly specified in recent models, for instance within
research on technology acceptance and technology interaction (e.g., [1, 7, 51]), or regarding
motivational aspects of human-technology interaction (e.g., [53]). A more comprehensive examination
of such personality differences is essential for a deepened understanding of user diversity regarding
experience and preferences in human-technology interaction [52]. Moreover, certain personality facets
constitute resources for a successful interaction with technology. Knowledge about how to cultivate
and use these resources can increase users’ fit to technology.
Even though personality is hardly addressed in textbooks on engineering psychology (e.g., [55]),
investigating inter-individual differences in the field of human-technology interaction is not new [4, 18].
A plethora of personality-related constructs linked to the subjective experience of – and interaction with
– technical systems have been discussed. Unfortunately, many of these concepts are interconnected
and overlapping, which is reflected by similarities in assessment scales.
A key question for researchers in the field is thus: Which are the key personality dimensions for
characterizing inter-individual differences in successful user technology interaction?

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2 BACKGROUND
A single prevailing definition of personality has not been established so far [45] and definitions vary with
different theoretical perspectives (e.g., psychodynamic view, cognitive view; [11]). When talking about
personality, we refer to the inter-individually differing set of relatively stable attributes that affect one’s
behavior, cognitions and emotions [3]. Among these attributes are classic personality traits (e.g., the Big
Five personality dimensions, see below), cognitive patterns (e.g., attitudes, beliefs), motivational
patterns (e.g., interests), and affective patterns (e.g., domain-specific anxiety).
The goal of investigating personality differences in human-technology interaction can be
achieved on different levels. First, personality can be broadly assessed by using one of the established
Big Five questionnaires (e.g., [16]). Personality is then measured on the five fundamental dimensions
openness to experience, conscientiousness, extraversion, agreeableness and neuroticism [33]. While
some connections between Big 5 dimensions and technology use [7], acceptance [51], and human-
computer interaction (HCI; [28]) have been found, the effect sizes are usually small to medium-sized.
These factors are likely too broad to accurately predict specific difference in human-technology
interaction.
Second, personality variables that are closely related to Big Five sub-facets (e.g., need for
cognition [9], which is connected to openness to experience) as well as other psychological
characteristics that are seen as influencing behavior transsituationally (e.g., self-efficacy; [5]) have been
examined regarding human-technology interaction. For instance, locus of control [44] has been
identified as a significant predictor for the intention to use internet banking [1], and the stress
experienced during first-time interaction with limited range of electric vehicles [20].
Third, a large variety of technology-related personality constructs have been proposed to
characterize differences in human-technology interaction. These constructs are understood as relatively
stable personality traits that are situation-specific, that is, characterize an individual’s experience and
behavior while interacting with technology. Unfortunately, these constructs and the corresponding
scales are often overlapping and interconnected, making it difficult for researchers and practitioners to
choose the right construct and scale. For instance, regarding human-technology and human-computer
interaction the following constructs are discussed: computer attitudes, computer anxiety, computer
self-efficacy, control beliefs regarding technology usage, playfulness, personal innovativeness, affinity
to technology, technology commitment, technology readiness, computer-related motivations, and
geekism (see Table 2). Out of this richness of constructs reflecting individual differences in human-
technology interaction, an important task of engineering psychology research is to structure the
prevailing concepts and to provide an overview regarding assessment scales.

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As a first step in this research agenda, we aim to answer the following research questions: (Q1)
Which technology-related personality constructs and corresponding scales are frequently applied in
human factors/ergonomics research?, and (Q2) Which technology-related personality constructs and
scales have been proposed and applied in recent years?
3 METHOD
To identify key scales that characterize inter-individual differences in human-technology interaction, the
following procedure was used. In the first step (S1-identification), relevant scales were identified using
literature search. In the second step (S2-selection), the number of relevant scales was narrowed down
according to citation frequencies in major human factors/ergonomics and HCI journals in selected time
periods (see below).
In the identification step (S1) a literature search in Google Scholar for technology-related
personality scales using the search string (“human-technology interaction” OR “human
computer-interaction”) AND “personality” AND (“questionnaire” OR
“scale”) AND “reliability”
1
was conducted. Google Scholar was chosen as a database due
to its large breadth of covered academic sources. Further, we examined five review articles discussing
relevant scales [21, 29, 30, 40, 48]. Moreover, iterative forward and backward search processes revealed
additional scales. Within this first step 59 relevant scales were identified.
2
In the selection step (S2), we looked for the identified scales in nine key journals and two key
conference proceedings from the field of human factors/ergonomics and HCI (see Table 1). We selected
(1) scales that were cited more than ten times in the mentioned journals/proceedings, (2) scales that
were developed within the last ten years and were cited at least five times, and (3) selected scales
published within the last five years (i.e., without citation criterion). With selection criterion (1) we
detected established, frequently used scales over a longer time period. Because of fundamental
technology changes since the emergence of first technology-related personality scales, we were also
particularly interested in current developments, both somewhat established (2) and novel (3).
1
Parentheses inserted for better legibility. Google Scholar does not utilize parentheses for generating search
results.
2
Note that this number does not claim to be exhaustive as a distinctive search term for this research question
does not exist and many different scholars and research groups developed different scales. Thus, obtaining a
comprehensive picture of all developed scales in the field is a hardly achievable task.

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Table 1. Selected academic journals and conference proceedings. 2015 Impact factors according to
Journal Citation Reports (http://admin-apps.webofknowledge.com/JCR/JCR).
Title
IF
5-year IF
Selected academic journals
Human-Computer Interaction
3.70
4.03
Computers in Human Behavior
2.88
3.72
Applied Ergonomics
1.71
2.11
International Journal of Human-Computer Studies
1.48
2.10
Ergonomics
1.45
1.72
Human Factors
1.37
1.77
International Journal of Human-Computer Interaction
1.26
1.46
Behaviour & Information Technology
1.21
1.49
Interacting with Computers
0.89
1.64
Selected conference proceedings
CHI (Conference on Human Factors in Computing Systems)
HCI International
4 RESULTS AND DISCUSSION
4.1 (Q1) Established Personality Constructs and Corresponding Scales
Selected scales, journals and citation figures are depicted in Table 2.
With respect to (Q1) the most frequently applied constructs were computer attitude (nine
scales), computer anxiety (eight scales) and computer self-efficacy (three scales). Further, one scale for
each of the following constructs, computer playfulness, personal innovativeness, and technology
readiness was found.
Computer attitudes are usually regarded as a multidimensional construct reflecting users’
positive or and negative feelings towards computers [21, 29]. However, scales differ in their definition
of attitudes making them difficult to compare [21]. Out of the computer attitude measures, the CAS-L
[31] was the most frequently cited scale (90 citations overall). This scale consists of 30 items on three
subscales, namely computer liking, computer confidence, and computer anxiety. Interestingly, none of
the computer attitude scales was cited more than ten times within the last five years, leaving the

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impression that computer attitudes (or at least the corresponding scales) play an increasingly smaller
role in HCI research.
Computer anxiety can be viewed as a situation-specific form of anxiety characterized by feelings
of fear and apprehension when interacting with or thinking of computers [13]. The most frequently used
scale to assess computer anxiety is the CARS-H [22], which was cited 87 times overall and 17 times
within the last five years. The CARS-H assesses computer anxiety with 20 items, of which 11 reflect
anxiety-related cognitive, behavioral and affective responses to computers and 9 items reflect positive
attitudes towards computers. This scale correlates highly with the CAS-L, which reveals conceptual
overlaps [22]. The New Computer Anxiety and Self-Efficacy Scales [6], which were published 17 years
later, were cited 17 times within the last five years. All other established computer anxiety scales were
cited five times or less in the last five years.
Computer self-efficacy is defined as the “judgment of one’s capability to use a computer” ([15],
p. 192) and is based on the more general psychological concept of self-efficacy [5]. Computer self-
efficacy is negatively correlated with computer anxiety and positively with ease of use [23], thus, it
represents a personal resource for coping with computer demands. By far the most cited scale for
assessing computer self-efficacy is the CSEM [15], which was cited 180 times overall and 93 times within
the last five years, indicating a constant relevance.
Another construct that can be viewed as a personal resource regarding coping with technology
is computer playfulness, which reflects the cognitive spontaneity, curiosity and tendency to explore with
respect to computer interactions [54]. The 22-item CPS [54] was cited 70 times overall, of which 23
citations occurred within the last five years.
Closely connected to computer playfulness is personal innovativeness in information
technologies. This construct is defined as the “willingness of an individual to try out any new information
technology” ([2], p. 206) and can be measured with the 4-item PIIT scale. The scale was cited 57 times
since its publication, of which 38 citations appeared within the last five years.
Also, dealing with users’ tendency to react to technological innovations is the construct
technology readiness, “people’s propensity to embrace and use new technologies for accomplishing
goals in home life and at work” ([37], p. 308). The 36-item TRI uses four sub-scales (optimism,
innovativeness, discomfort, insecurity) to describe this tendency and is more thoroughly than the short
PIIT scale. The TRI and the shortened and revised TRI 2.0 [38] were cited 17 times overall and 13 times
within the last five years.

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Table 2. Selected scales and citation counts overall (Co) and within the last five years (C5)
Authors
Scale names and abbreviations
Co
C5
Scales with ten or more citations in key journals and conference proceedings
Loyd & Gressard (1984) [31]
Computer Attitude Scale (CAS-L)
90
4
Nickell & Pinto (1986) [37]
Computer Attitude Scale (CAS-N)
56
6
Kay (1993) [26]
Computer Attitude Measure (CAM)
39
0
Popovich et al. (1987) [39]
Attitudes-Toward-Computer Usage Scale
(ATCUS)
31
1
Dambrot et al. (1985) [17]
Computer Attitude Scale (CATT)
30
2
Zoltan-Ford & Chapanis (1987) [57]
Attitudes about Computers (AAC)
26
0
Reece & Gable (1982) [42]
Attitudes toward Computers (ATC-R)
11
0
Francis (1993) [19]
Attitudes toward Computers (ATC-F)
10
0
Shaft et al. (2004) [49]
Attitudes toward Computers Instrument (ATCI)
10
3
Heinssen et al. (1987) [22]
Computer Anxiety Rating Scale (CARS-H)
87
17
Rosen et al. (1987) [43]
Computer Anxiety Rating Scale (CARS-R)
65
2
Barbeite & Weiss (2004) [6]
New Computer Anxiety and Self-efficacy Scales
29
17
Simonson et al. (1987) [50]
Computer Anxiety Index (CAIN)
20
2
Marcoulides (1989) [32]
Computer Anxiety Scale (CAS-M)
16
4
Cohen & Waugh (1989) [14]
Computer Anxiety Scale (CAS-C)
14
1
Campbell & Dobson (1987) [10]
Computer Anxiety Scale – Short Form (CAS-SF)
10
1
Charlton & Birkett (1995) [12]
Computer Apathy and Anxiety Scale (CAAS)
10
5
Compeau & Higgins (1995) [15]
Computer Self-Efficacy Measure (CSEM)
180
93
Murphy et al. (1989) [34]
Computer Self-Efficacy Scale (CSE)
45
9
Webster & Martocchio (1992) [54]
Computer Playfulness Scale (CPS)
70
23
Argawal & Prasad (1998) [5]
Personal Innovativeness in Information
Technologies (PIIT)
57
38
Parasuraman (2000) [37]
Technology Readiness Index (TRI)
17
13
Scales developed in the last ten years and cited at least five times
Beier (2009) [8]
Control Beliefs while Dealing with Technology
(KUT)
8
Karrer et al. (2009) [25]
Affinity for Technology Questionnaire (TA-EG)
8
Schulenberg & Melton (2008)
[47]
Computer Aversion, Attitudes, and Familiarity
Index (CAAFI)
7
Joyce & Kirakowski (2015) [24]
General Internet Attitude Scale (GIAS)
6
Selected scales from the last five years without citation criteria
Neyer et al. (2012) [35]
Technology Commitment (TB)
1
Kim & Glassman (2013) [27]
Internet Self-Efficacy Scale (ISS)
3
Yildirim & Correira (2015) [58]
Nomophobia Questionnaire (NMP-Q)
3
Senkbeil & Ihme (2016) [48]
Short Scale for Computer-Related Motivations
in Adults (FECAF)
0
Schmettow & Drees (2014) [46]
Gex (Geekism, explicit)
0

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4.2 (Q2) Recently Discussed Personality Constructs and Corresponding Scales
Selected recent developments (also depicted in Table 2) represent novel scales we view as particularly
promising to predict successful human-technology interaction.
Control beliefs while dealing with technology is based on the more general construct of control
beliefs [44], which describes an individual’s belief about the relationship between their behavior and
the behavioral outcome. The corresponding scale, the 24-item KUT [8], assesses control beliefs on three
dimensions: internality (behavioral outcomes depend on factors within the person), technical
externality (behavioral outcomes depend on factors within the technical device) and fatalistic
externality (behavioral outcomes depend on coincidence). A unidimensional 8-item short form is also
available [8].
According to [25], affinity to technology consists of four sub-facets, namely enthusiasm for
technology, competence in dealing with technology, positive and negative attitudes towards
technology. These facets are measured by the 19-item TA-EG. A similar multidimensional approach is
followed with the TB [35], which measures technology commitment on three subscales: technology
acceptance, technology competence (i.e., self-efficacy), and technology control beliefs. Another multi-
dimensional measure, albeit restricted to computer use, is the CAAFI [47]. This 30-item questionnaire
assesses computer familiarity, attitudes towards computers and computer aversion (i.e., discomfort and
fear).
Two recent scales deal with internet usage: an internet attitude scale [24] and an internet self-
efficacy scale [27]. The 21-item GIAS measures internet attitudes on four dimensions: internet affect,
internet exhilaration, social benefit of the internet, and internet detriment [24]. With the 25-item ISS,
internet self-efficacy is measured in five dimensions according to five groups of internet activities: self-
efficacy regarding information search, communication, information organization, information
differentiation, and information generation [27].
The NMP-Q assesses nomophobia, defined as the fear of having no mobile phone contact [56].
The 20-item questionnaire measures nomophobia on four dimensions reflecting different perceived
consequences of being out of mobile phone contact: not being able to communicate, losing
connectedness, not being able to access information, and giving up convenience.
Computer-related motivations are seen as relatively stable and situation-independent
dispositions that determine the purposes of an individual’s computer use [48]. The 14-item FECAF
measures computer-related motivations on six subscales subsumed under two factors: utilitarian
motivation (usage of computers as a learning tool, for information search and for higher efficiency of

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everyday tasks), and hedonistic motivation (usage of computers for entertainment, escapism, and social
communication).
Finally, the construct of geekism reflects the “need to explore, to understand and to tinker with
computing devices” ([46], p. 235). While users low in geekism use technology solely as a tool, users high
in geekism are intrinsically motivated to use and think about technological devices and explore them. A
recently developed self-report measure to assess geekism is the 15-item Gex [46].
5 CONCLUSION
Our overview of constructs and scales emphasizes the aforementioned problem that constructs – as
well as the corresponding scales – are conceptually overlapping. For instance, the CAS-L [31] is supposed
to measure computer attitude but comprises subscales assessing computer anxiety and computer
confidence (i.e., self-efficacy). On the other hand, the CARS-H [22], a scale for assessing for computer
anxiety, contains items regarding positive attitudes towards the computer. Hence, the differentiation
between scales and constructs is unclear.
The finding that the established computer attitude scales are rarely cited anymore gives rise to
the assumption that measuring HCI on the attitude level might no longer be relevant (see also [21]). In
fact, computers have become ubiquitous while the classic attitude scales [17, 19, 26, 31, 36, 39, 42, 57]
were developed at times when the digital society was in its infancy. Moreover, a shift from computer
anxiety and technophobia to nomophobia, the fear to be without a digital device, is occurring. In
contrast to the 80s and 90s, when the aforementioned scales were developed, computers can hardly
be avoided today. Thus, the need for new scales emerges.
A limitation of our research that has to be kept in mind when interpreting the results is the
heuristic solution of inferring actual scale usage from citation counts. First, a cited scale does not
automatically mean that the scale was actually employed by the citing source. Thus, based on our data,
the absolute usage frequencies might be overestimated. Second, due to the selection of journals and
conference proceedings, the absolute usage frequencies might also be underestimated. However, the
relative citation frequencies should remain mostly constant.
The next step in the research agenda to structure personality constructs and scales regarding
human-technology interaction is to clarify interrelationships between concepts. Thereby, personality
facets unique to certain scales – as well as blind spots – should be revealed. This knowledge will further
facilitate scale selection and stimulate future scale construction.

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