Conference PaperPDF Available

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



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.
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
2Institute for Multimedia and Interactive Systems, Engineering Psychology and Cognitive Ergonomics, Universität zu Lübeck,
Lübeck, Germany
*Corresponding author
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
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. 1929). Cham, Switzerland: Springer
International Publishing AG. doi:10.1007/978-3-319-58750-9_3
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?
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
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.
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?
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”
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.
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).
Parentheses inserted for better legibility. Google Scholar does not utilize parentheses for generating search
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.
Table 1. Selected academic journals and conference proceedings. 2015 Impact factors according to
Journal Citation Reports (
5-year IF
Selected academic journals
Human-Computer Interaction
Computers in Human Behavior
Applied Ergonomics
International Journal of Human-Computer Studies
Human Factors
International Journal of Human-Computer Interaction
Behaviour & Information Technology
Interacting with Computers
Selected conference proceedings
CHI (Conference on Human Factors in Computing Systems)
HCI International
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
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.
Table 2. Selected scales and citation counts overall (Co) and within the last five years (C5)
Scales with ten or more citations in key journals and conference proceedings
Loyd & Gressard (1984) [31]
Nickell & Pinto (1986) [37]
Kay (1993) [26]
Popovich et al. (1987) [39]
Dambrot et al. (1985) [17]
Zoltan-Ford & Chapanis (1987) [57]
Reece & Gable (1982) [42]
Francis (1993) [19]
Shaft et al. (2004) [49]
Heinssen et al. (1987) [22]
Rosen et al. (1987) [43]
Barbeite & Weiss (2004) [6]
Simonson et al. (1987) [50]
Marcoulides (1989) [32]
Cohen & Waugh (1989) [14]
Campbell & Dobson (1987) [10]
Charlton & Birkett (1995) [12]
Compeau & Higgins (1995) [15]
Murphy et al. (1989) [34]
Webster & Martocchio (1992) [54]
Argawal & Prasad (1998) [5]
Parasuraman (2000) [37]
Scales developed in the last ten years and cited at least five times
Beier (2009) [8]
Karrer et al. (2009) [25]
Schulenberg & Melton (2008)
Joyce & Kirakowski (2015) [24]
Selected scales from the last five years without citation criteria
Neyer et al. (2012) [35]
Kim & Glassman (2013) [27]
Yildirim & Correira (2015) [58]
Senkbeil & Ihme (2016) [48]
Schmettow & Drees (2014) [46]
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
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 individuals 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
everyday tasks), and hedonistic motivation (usage of computers for entertainment, escapism, and social
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].
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.
[1] Abu-Shanab E, Pearson JM, Setterstrom AJ (2010) Internet banking and customers’ acceptance in
Jordan: The unified model’s perspective. Commun Assoc Inform Syst 26:493-524.
[2] Argawal R, Prasad J (1998). A conceptual and operational definition of personal innovativeness in
the domain of information technology. Inform Syst Res 9:204-215. doi:10.1287/isre.9.2.204
[3] Ashton MC (2013) Individual differences and personality. Elsevier, Amsterdam
[4] Aykin NM, Aykin T (1991) Individual differences in human-computer interaction: a survey.
Computers Ind Engng 20:373-379. doi:10.1016/0360-8352(91)90009-U
[5] Bandura A (1977) Self-efficacy: Toward a unifying theory of behavioral change. Psychol Rev 84:191-
215. doi:10.1037/0033-295X.84.2.191
[6] Barbeite FG, Weiss EM (2004) Computer self-efficacy and anxiety scales for an internet sample:
Testing measurement equivalence of existing measures and development of new scales. Comput
Hum Behav 20:1-15. doi:10.1016/S0747-5632(03)00049-9
[7] Barnett T, Pearson AW, Pearson R, Kellermanns FW (2015) Five-factor model personality traits as
predictors of perceived and actual usage of technology. Eur J Inform Syst 24:374-390.
[8] Beier G (1999) Kontrollüberzeugungen im Umgang mit Technik [Control beliefs in dealing with
technology]. Rep Psychol 9:684-93
[9] Cacioppo JT, Petty RE (1982) The need for cognition. J Pers Soc Psychol 42:116-131.
[10] Campbell NJ, Dobson JE (1987) An inventory of student computer anxiety. Elem School Guidance
Counsel 22:149-156
[11] Carducci BJ (2009) The psychology of personality. Wiley Blackwell, Chichester
[12] Charlton JP, Birkett PE (1995) The development and validation of the computer apathy and anxiety
scale. J Educ Comput Res 13:41-59. doi:10.2190/5UPE-80NP-W9WN-BE6W
[13] Chua SL, Chen D, Wong AFL (1999) Computer anxiety and its correlates: A meta-analysis. Comput
Hum Behav 15:609-623. doi:/10.1016/S0747-5632(99)00039-4
[14] Cohen BA, Waugh GW (1989) Assessing computer anxiety. Psychol Rep 65:735-738.
[15] Compeau DR, Higgings CA (1995) Computer self-efficacy: Development of a measure and initial
test. MIS Quart 19:189-211. doi:10.2307/249688
[16] Costa PT, McCrae RR (1992) Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor
Inventory (NEO-FFI): Professional manual. Psychological Assessment Resources, Odessa
[17] Dambrot FH, Watkins-Malek MA, Silling SM, Marshall RS, Garver JA (1985) Correlates of sex
differences in attitudes toward and involvement with computers. Journal Vocat Behav 27:71-86.
[18] Dillon A, Watson C (1996) User analysis in HCI the historical lessons from individual differences
research. Int J Hum-Comput St 45:619-637. doi:10.1006/ijhc.1996.0071
[19] Francis LJ (1993) Measuring attitude toward computers among undergraduate college students:
The affective domain. Comput Educ 20:251-255. doi:10.1016/0360-1315(93)90024-D
[20] Franke T, Rauh N, Krems JF (2016) Individual differences in BEV drivers’ range stress during first
encounter of a critical range situation. Appl Ergon 57:28-35. doi:10.1016/j.apergo.2015.09.010
[21] Garland KJ, Noyes JM (2008) Computer attitude scales: How relevant today? Comput Hum Behav
24:563-575. doi:10.1016/j.chb.2007.02.005
[22] Heinssen RK, Glass CR, Knight LA (1987) Assessing computer anxiety: Development and validation
of the computer anxiety rating scale. Comput Hum Behav 3:49-59. doi:10.1016/0747-
[23] Igbaria M, Iivari J (1995) The effects of self-efficacy on computer usage. Omega-Int J Manage S
23:587-605. doi:10.1016/0305-0483(95)00035-6
[24] Joyce M, Kirakowski J (2015) Measuring attitudes towards the internet: The general internet
attitude scale. Int J Hum-Comput Int 31:506-517. doi:10.1080/10447318.2015.1064657
[25] Karrer K, Glaser C, Clemens C, Bruder C (2009). Technikaffinität erfassen der Fragebogen TA-EG
[Measuring affinity to technology the questionnaire TA-EG]. In: Lichtenstein A, Stößel C, Clemens
C (eds) Der Mensch im Mittelpunkt technischer Systeme. 8. Berliner Werkstatt Mensch-Maschine-
Systeme 7. bis 9. Oktober 2009 VDI, Düsseldorf, pp.196-201
[26] Kay RH (1993) An exploration of theoretical and practical foundations for assessing attitudes
toward computers: The computer attitude measure (CAM). Comput Hum Behav 9:371-386.
[27] Kim Y, Glassman M (2013) Beyond search and communication: Development and validation of the
Internet Self-efficacy Scale (ISS). Comput Hum Behav 29:1421-1429.
[28] Kuurstra J (2015) Individual differences in human-computer interaction: A review of empirical
studies. Master’s thesis, University of Twente
[29] LaLomia MJ, Sidowski JB (1991). Measurements of computer attitudes: A review. Int J Hum-Comput
Int 3:171-197. doi:10.1080/10447319109526003
[30] LaLomia MJ, Sidowski JB (1993) Measurements of computer anxiety: A review. Int J Hum-Comput
Int 5:239-266. doi:10.1080/10447319309526067
[31] Loyd BH, Gressard C (1984) Reliability and factorial validity of computer attitude scales. Educ
Psychol Meas 44:501-505. doi:10.1177/0013164484442033
[32] Marcoulides GA (1989) Measuring computer anxiety: The Computer Anxiety Scale. Educ Psychol
Meas 49:733-739. doi:10.1177/001316448904900328
[33] McCrae RR, John OP (1992). An introduction to the five-factor model and its applications. J Pers
60:175-215. doi:10.1111/j.1467-6494.1992.tb00970.x
[34] Murphy CA, Coover D, Owen SV (1989) Development and validation of the computer self-efficacy
scale. Educ Psychol Meas 49, 893-899. doi:10.1177/001316448904900412
[35] Neyer FJ, Felber J, Gebhardt C (2012) Entwicklung und Validierung einer Kurzskala zur Erfassung
von Technikbereitschaft [Development and validation of a brief measure of technology
commitment]. Diagnostica 58:87-99. doi:10.1026/0012-1924/a000067
[36] Nickell GS, Pinto JN (1986) The Computer Attitude Scale. Comput Hum Behav 2:301-306.
[37] Parasuraman A (2000). Technology Readiness Index (TRI). J Serv Res-US 2:307-320.
[38] Parasuraman A, Colby CL (2015). An updated and streamlined Technology Readiness Index: TRI 2.0.
J Serv Res-US 18:59-74. doi:10.1177/1094670514539730
[39] Popovich PM, Hyde KR, Zakrajsek T, Blumer C (1987) The development of the attitudes toward
computer usage scale. Educ Psychol Meas 47:267-269. doi:10.1177/0013164487471035
[40] Powell AL (2013). Computer anxiety: Comparison of research from the 1990s and 2000s. Comput
in Hum Behav 29:2337-2381. doi:10.1016/j.chb.2013.05.012
[41] Pozzi S, Bagnara S (2013) Individuation and diversity: The need for idiographic HCI. Theor Issues in
Ergon S 14:1-21. doi:10.1080/1464536X.2011.562564
[42] Reece MJ, Gable RK (1982) The development and validation of a measure of general attitudes
toward computers. Educ Psychol Meas 42:913-916. doi:10.1177/001316448204200327
[43] Rosen LD, Sears DC, Weil MM (1987) Computerphobia. Behav Res Meth Ins C 19:167-179.
[44] Rotter JB (1966) Generalized expectancies for internal versus external control of reinforcement.
Psychol Monogr-Gen A 80:1-28. doi:10.1037/h0092976
[45] Saucier G (2008). Measures of the personality factors found recurrently in human lexicons. In:
Boyle GJ, Matthews G, Saklofske DH (eds) The SAGE handbook of personality theory and
assessment, Vol 2: Personality measurement and testing. London, SAGE Publications, pp 29-54
[46] Schmettow M, Drees M (2014) What drives the geeks? Linking computer enthusiasm to
achievement goals. In: Proceedings of HCI 2014, Southport, UK. BCS Learning and Development
Ltd., Swindon, pp. 234-239
[47] Schulenberg SE, Melton AMA (2008) The Computer Aversion, Attitudes, and Familiarity Index
(CAAFI): A validity study. Comput Hum Behav 24:2620-2638. doi:10.1016/j.chb.2008.03.002
[48] Senkbeil M, Ihme, JM (2016) Entwicklung und Validierung eines Kurzfragebogens zur Erfassung
computerbezogener Anreizfaktoren bei Erwachsenen [Development and Validation of a Short
Scale for Computer-Related Motivations in Adults]. Diagnostica. doi:10.1026/0012-1924/a000170
[49] Shaft TM, Sharfman MP, Wu WW (2004). Reliability assessment of the attitude towards computers
instrument (ATCI). Comput Hum Behav 20:661-689. doi:10.1016/j.chb.2003.10.021
[50] Simonson MR, Maurer M, Montag-Torardi, M, Whitaker M (1987) Development of a standardized
test of computer literacy and a computer anxiety index. J Educ Comp Res 3:231-247.
[51] Svendsen GB, Johnsen JK, Almås-Sørensen L, Vittersø J (2013) Personality and technology
acceptance: The influence of personality factors on the core constructs of the Technology
Acceptance Model. Behav Inform Technol 32:323-334. doi:10.1080/0144929X.2011.553740
[52] Szalma JL (2009) Individual differences in human-technology interaction: Incorporating variation in
human characteristics into human factors and ergonomics research and design. Theor Issues in
Ergon S 10:381-397. doi:10.1080/14639220902893613
[53] Szalma JL (2014). On the application of motivation theory to human factors/ergonomics:
Motivational design principles for human-technology interaction. Hum Factors 56:1453-1471.
[54] Webster J, Martocchio, JT (1992) Microcomputer playfulness: Development of a measure with
workplace implications. MIS Quart 16:201-226. doi:10.2307/249576
[55] Wickens CD, Hollands J, Banbury S, Parasuraman R (2013). Engineering psychology and human
performance. Pearson, London
[56] Yildirim C, Correira A (2015) Exploring the dimensions of nomophobia: Development and validation
of a self-reported questionnaire. Comput Hum Behav 49:130-137. doi:10.1016/j.chb.2015.02.059
[57] Zoltan-Ford E, Chapanis A (1987). What do professional persons think about computers? In:
Anderson JG, Jay SJ (eds) Use and impact of computers in clinical medicine. Springer, New York, pp
51-67. doi:10.1007/978-1-4613-8674-2_5
... The study adopted the model of the five major factors of personality, and the results revealed that openness is associated with the ability to effectively employ technology. While the study by Attig;Attig et al: [2017] investigated the interrelationships between personality traits and the use of technological systems. It answered the following question: What are the basic personality traits for diagnosing internal individual differences in the use of technology and the interaction between them? ...
... The study adopted the model of the five major factors of personality, and the results revealed that openness is associated with the ability to effectively employ technology. While the study by Attig;Attig et al: [2017] investigated the interrelationships between personality traits and the use of technological systems. It answered the following question: What are the basic personality traits for diagnosing internal individual differences in the use of technology and the interaction between them? ...
Full-text available
Employing computer technology in different learning situations leads to the rapid absorption of various scientific concepts, and contributes to providing learners withmany facts and knowledge in succession. However, the use of these technologies needs human groups capable of dealing with technological software and effectively employing them. It is clear from the foregoing that the use of computer technology is related to the personal characteristics of the individual, and in light of the interest of some of the literature in identifying the most important characteristics of personality and its dimensions and its various effects on the processes of using computer technology, and the interest of other studies in the possibility of identifying personality characteristics and judging them through the digital performance of computer users. The current study aimed to identify the degree of achieving psycho-technological compatibility between both personality traits and technological skills through the application of the five factors of the personality scale and the technological competency test on a sample of preparatory year students at King Faisal University. The descriptive, correlative, and comparative approach was used. Two tools were developed, a scale for personal traits and a test for basic competencies for using computer technologies. The results revealed that there is a statistically significant correlation between the personality traits and their dimensions, and the students’ technological skills. Further, there was an effect of extraversion, openness to experience, acceptability, and conscientiousness on the total scores of technological skills. In addition, it is possible to predict some of the personality traits with the technological skills common to students, and there is a large degree of compatibility and compatibility between the personality traits and the technological skills of the individual.
... For the pre/post evaluation of [P8] questionnaires were the primary method of data collection. We used several validated scales throughout our studies, including the Affinity of Technology Interaction scale (ATI) [8,51], the User Experience Questionnaire (UEQ) [76], the System Usability Scale (SUS) [15], and blockchain specific items adapted from Abramova et al. [1]. ...
... At the same time, cryptocurrencies users still face major unsolved challenges: user interfaces suffer from usability issues [8,22,27,37], there remain fundamental trust challenges [6,26,34,44,45], cryptocurrencies are complex to understand [21,22] and have a high entry-barrier for people with less technical knowledge [31]. With more blockchain-based services emerging, it is important to understand which challenges people face -to ultimately design solutions around them and facilitate the development of more inclusive systems that allow users without deep technical knowledge to participate in the crypto economy of tomorrow. ...
Full-text available
This dissertation investigates the usability challenges faced by cryptocurrency users and explores how human-computer interaction (HCI) can help address these challenges. The research is conducted through three approaches: (1) a systematic review of HCI research on cryptocurrency, which finds that existing research has focused largely on Bitcoin and Ethereum, (2) a qualitative interview study and Delphi study to understand user behavior and the challenges faced by first-time users, and (3) an evaluation of different approaches to improving the usability of cryptocurrency applications. The findings of the empirical studies are translated into artifacts and tested, with the results showing that onboarding in mobile apps can improve perceived usability for first-time users, Bitcoin Lightning can serve as a usable settlement layer for everyday transactions, education can support the next generation of developers in building more useful applications, and rapid interface prototyping systems may speed up development efforts. The work concludes by considering the future role of HCI research in the cryptocurrency and blockchain space. However, the introduction of new technologies is often accompanied by the emergence of equally new design challenges. Despite the technological progress over the past years, cryptocurrencies have earned a reputation of being hard to get started with and overall difficult to use. But what exactly are the aspects that make them difficult to use? How do users manage their cryptocurrency in practice? Which challenges do they need to overcome? And how can Human-Computer Interaction help overcome these challenges? In several studies, this dissertation addresses these questions and explores them through three different approaches: (1) Cryptocurrency in Human-Computer Interaction: By systematically reviewing published Human-Computer Interaction research since the inception of Bitcoin, we organize the existing research effort and juxtapose it with the changing landscape of emerging technologies from practice to identify avenues for future research. Our results show that existing research has overwhelmingly focused on Bitcoin and Ethereum, while not addressing novel cryptocurrencies. (2) Understanding User Behavior: By exploring user behavior through multiple lenses we shed light on real-world practices of users and the challenges they face. We explore security and privacy practices through a qualitative interview study and triangulate the results in a delphi-study with 25 experts. We conducted an interview study to understand a particularly relevant point for the adoption of cryptocurrency – we investigate challenges first-time users face. Our results show that many usability issues are not rooted in the technical aspects of blockchain technology and can be addressed through Human-Computer Interaction research. (3) Improving Application Usability: By evaluating different approaches on how to aid the development of cryptocurrency applications we translate the findings of our empirical work into artifacts and put them to the test. Our results show that onboarding in mobile apps can improve perceived usability for first-time users under the right conditions, that Bitcoin Lightning can serve as a usable settlement layer for everyday transactions, that education can support the next generation of developers in building more useful applications, and that systems for rapid interface prototyping may speed up development efforts. Collectively, the contribution of this dissertation centers around the ongoing discussion on how to build usable cryptocurrency systems. More precisely, this dissertation contributes (a) empirical studies that show how users manage their cryptocurrency in practice and which challenges they face in doing so and (b) constructive approaches attempting to support the development of cryptocurrency systems in the future. The work concludes by reflecting on the future role of Human-Computer Interaction research in the cryptocurrency and blockchain space.
... This can be inferred from the steadily growing adoption of respective IVA-driven products illustrated in Figure 1. With respect to this increase in adoption, one may even argue that the IVA field has started experiencing what Attig et al. describe as "a shift from computer anxiety and technophobia to nomophobia, the fear to be without a digital device" ( [2], p. 26). To this end, we have already entered an era of "ubiquitous listening" where we are surrounded by devices that are capable of constantly listening to their environment, and where the technological advancements in speech recognition and natural language processing allow for complete device control, without the need for pressing buttons or shifting levers. ...
Full-text available
Throughout the last years, Intelligent Virtual Assistants (IVAs), such as Alexa and Siri, have increasingly gained in popularity. Yet, privacy advocates raise great concerns regarding the amount and type of data these systems collect and consequently process. Among many other things, it is technology trust which seems to be of high significance here, particularly when it comes to the adoption of IVAs, for they usually provide little transparency as to how they function and use personal and potentially sensitive data. While technology trust is influenced by many different socio-technical parameters, this article focuses on human personality and its connection to respective trust perceptions, which in turn may further impact the actual adoption of IVA products. To this end, we report on the results of an online survey (n=367). Findings show that on a scale from 0 to 100%, people trust IVAs 51.59% on average. Furthermore, the data point to a significant positive correlation between people’s propensity to trust in general technology and their trust in IVAs. Yet, they also show that those who exhibit a higher propensity to trust in technology tend to also have a higher affinity for technology interaction and are consequently more likely to adopt IVAs.
... The object of this study was to construct logistic regression models using statistically significant sociodemographic, well-being, health, benefit, and lifestyle variables. A diverse range of independent variables was selected, since the increasing use of digital devices gives rise to new phenomena, which make earlier established scales concentrating on for example (computer) attitude insufficient [35]. ...
Full-text available
The use of wearable technology, which is often acquired to support well-being and a healthy lifestyle, has become popular in Western countries. At the same time, healthcare is gradually taking the first steps to introduce wearable technology into patient care, even though on a large scale the evidence of its' effectiveness is still lacking. The objective of this study was to identify the factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the Finnish adult population (20-99) and among older adults (65-99). The study utilized a cross-sectional population survey of Finnish adults aged 20 and older (n = 6,034) to analyse non-causal relationships between wearable technology use and the users' characteristics. Logistic regression models of wearable technology use were constructed using statistically significant sociodemographic, well-being, health, benefit, and lifestyle variables. Both in the general adult population and among older adults, wearable technology use was associated with getting aerobic physical activity weekly according to national guidelines and with marital status. In the general adult population, wearable technology use was also associated with not sleeping enough and agreeing with the statement that social welfare and healthcare e-services help in taking an active role in looking after one's own health and well-being. Younger age was associated with wearable technology use in the general adult population but for older adults age was not a statistically significant factor. Among older adults, non-use of wearable technology went hand in hand with needing guidance in e-service use, using a proxy, or not using e-services at all. The results support exploration of the effects of wearable technology use on maintaining an active lifestyle among adults of all ages.
... Since the introduction of personal computers in the 1970s and 1980s, personality-related constructs on people's attitudes towards technology (e.g., 'computer attitudes', 'computer anxiety', 'computer aversion', 'computer self-efficacy', 'technology readiness', see Attig et al. 2017 for an overview) have been found to be associated with various other characteristics in adults (e.g., Anthony et al. 2000;dos Santos and Santana 2018;Horstmann et al. 2018;Korukonda 2005Korukonda , 2007Nitsch and Glassen 2015;Powell 2013;Saleem et al. 2011) as well as children (e.g., Baloğlu and Çevik 2008;Chou 2001;Cooper 2006;King et al. 2002;Rees and Noyes 2007;Todman and Lawrenson 1992;Todman and Monaghan 1994). Therefore, the main reason for including the TAQ was to control for parallel associations between children's technological affinity and their DVA-exposure (e.g., higher technological affinity associated with higher DVA-exposure), or between children's technological affinity and their ontological perceptions of technology (e.g., more (items 5, 11, 13, 14, 15) or adjusted in their wording (item 7: '[…] that can add numbers together' → '[…] that can calculate something'). ...
Full-text available
Digital Voice Assistants (DVAs) have become a ubiquitous technology in today’s home and childhood environments. Inspired by (Bernstein and Crowley, J Learn Sci 17:225–247, 2008) original study (n = 60, age 4–7 years) on how children’s ontological conceptualizations of life and technology were systematically associated with their real-world exposure to robotic entities, the current study explored this association for children in their middle childhood (n = 143, age 7–11 years) and with different levels of DVA-exposure. We analyzed correlational survey data from 143 parent–child dyads who were recruited on ‘Amazon Mechanical Turk’ (MTurk). Children’s ontological conceptualization patterns of life and technology were measured by asking them to conceptualize nine prototypical organically living and technological entities (e.g., humans, cats, smartphones, DVAs) with respect to their biology, intelligence, and psychology. Their ontological conceptualization patterns were then associated with their DVA-exposure and additional control variables (e.g., children’s technological affinity, demographic/individual characteristics). Compared to biology and psychology, intelligence was a less differentiating factor for children to differentiate between organically living and technological entities. This differentiation pattern became more pronounced with technological affinity. There was some evidence that children with higher DVA-exposure differentiated more rigorously between organically living and technological entities on the basis of psychology. To the best of our knowledge, this is the first study exploring children’s real-world exposure to DVAs and how it is associated with their conceptual understandings of life and technology. Findings suggest although psychological conceptualizations of technology may become more pronounced with DVA-exposure, it is far from clear such tendencies blur ontological boundaries between life and technology from children’s perspective.
... There is a lot of literature that deals with technical affinity in various forms including literature that specifically mentions and uses the ATI scale by Attig et al. (2017). ...
Full-text available
Originally, the term "Deepfake" was thought to be the replacement or substitution of faces, facial features or facial expressions by artificial intelligence (AI). Currently, it is possible to change entire bodies and voices in audiovisual media content. The paper deals with the problem of how deepfakes are perceived by recipients. It examines to what extent perception depends on the viewer's self-assessed affinity for technology. First, an overview of the technical basics and the possible potential of deepfake technology is given. Then, after a detailed literature review on deepfakes, the research question is derived and seven hypotheses are established. For the investigation, a questionnaire was designed including the ATI scale for measuring the affinity for technology. A within-subject design with an information text made it possible to measure the perception of authenticity towards the deepfake. An advertising video served as a deepfake example. The online survey, in which 199 German-speaking participants took part, was statistically evaluated and interpreted. Summarising the study's outcome, it was found that the participants' affinity for technology had little influence on the perception of deepfakes. Male test persons considered themselves to be more tech-savvy than females. The participants with a higher affinity for technology had more background knowledge of the topic, were more impressed by the deepfake technology and tended to perceive the term "Deepfake" less negatively. Of the alternative terms mentioned in the questionnaire, the term "AI-generated media" was selected by the majority of the participants to be the term that best described the new technology and was perceived as the least negatively. Keywords: Deepfake, Perception, Synthetic Media, AI-generated Media, Artificial Media, Advertisement, Artificial Intelligence, Machine Learning, Artificial Neural Networks, Deep Learning, Deep Neural Networks, Affinity for Technology, ATI Scale
When interacting with artificial intelligence (AI) in the medical domain, users frequently face automated information processing, which can remain opaque to them. For example, users with diabetes may interact daily with automated insulin delivery (AID). However, effective AID therapy requires traceability of automated decisions for diverse users. Grounded in research on human-automation interaction, we study Subjective Information Processing Awareness (SIPA) as a key construct to research users’ experience of explainable AI. The objective of the present research was to examine how users experience differing levels of traceability of an AI algorithm. We developed a basic AID simulation to create realistic scenarios for an experiment with N = 80, where we examined the effect of three levels of information disclosure on SIPA and performance. Attributes serving as the basis for insulin needs calculation were shown to users, who predicted the AID system’s calculation after over 60 observations. Results showed a difference in SIPA after repeated observations, associated with a general decline of SIPA ratings over time. Supporting scale validity, SIPA was strongly correlated with trust and satisfaction with explanations. The present research indicates that the effect of different levels of information disclosure may need several repetitions before it manifests. Additionally, high levels of information disclosure may lead to a miscalibration between SIPA and performance in predicting the system’s results. The results indicate that for a responsible design of XAI, system designers could utilize prediction tasks in order to calibrate experienced traceability.
Full-text available
The recruitment of disabled participants for conducting usability evaluation of accessible information and communication technologies (ICT) is a challenge that current research faces. To overcome these challenges, researchers have been calling upon able-bodied participants to undergo disability simulations. However, this practice has been criticized due to the different experiences and expectations that disabled and able-bodied participants may have with ICT. This paper presents the methodology and lessons learned from ongoing mixed method-based usability evaluation of a suboptimal conventional computer mouse and an assistive gesture-based interface (i.e., the Leap Motion Controller) by stroke patients with upper-limb impairment and able-bodied participants experiencing a motor dysfunction simulation. The paper concludes with recommendations for future multidisciplinary research on ICT accessibility by people with disabilities.KeywordsAccessibilityUsability EvaluationDisability SimulationGesture-Based InterfaceAssistive Technology
Mind wandering (MW) is a mental activity in which our thoughts drift away and turn into internal notions and feelings. Research suggests that individuals spend up to one half of their waking hours thinking about task-unrelated things. Being the opposite of goal-directed thinking, empirical evidence suggests that MW can forester creativity and problem solving. However, and despite growing efforts to understand the role of MW in technology-related settings, the role of individual differences remains unclear. We address this gap by proposing a research model that seeks to shed further light on age-related differences in MW while using different types of technology (i.e., hedonic and utilitarian systems). Thereby, we provide a point of departure for further research on how individual characteristics influence MW while using technology.KeywordsMind wanderingTechnology useAgeHedonic and utilitarian systems
Full-text available
The article reveals the conditions for the science fiction and fantasy fiction to affect formation of technooptimistic and technopessimistic attitude of young readers. Primarily, the work assumes the way fiction heroes understand artefacts with reservations can be considered as the basis to reveal the attitude to engineering among those generations for whom these works were notional points. Technooptimism is defined as a worldview attitude within which nature and the humanbeing cause problems. The article shows that technopessimism identifies the human activity as the problem source. Harry Potter series proved to shape technopessimistic worldview and excuse technology inequality. Through the example of T. Pratchett books the article demonstrates that literature can formalize readers responsible attitude towards equipment if it shows not only the threat that pose technologies (the Promethan mentality), but also remedies to manage effects of machinery use, including those resulting from improper decisions (the Epimethian mentality).
Presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from 4 principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. Factors influencing the cognitive processing of efficacy information arise from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. (21/2 p ref)
Zusammenfassung. Im vorliegenden Beitrag wird die Konstruktion und erste Validierung eines Kurzfragebogens vorgestellt, der auf der Grundlage der sozial-kognitiven Theorie der Internetnutzung computerbezogene Anreizfaktoren bei Erwachsenen erfasst. Der Fragebogen ist fur den Einsatz in Large-Scale-Untersuchungen als Outcome-Variable sowie zur Vorhersage der Computernutzung und computerbezogener Fertigkeiten konzipiert. Die Ergebnisse einer Studie im Rahmen des Nationalen Bildungspanels (N = 462) zeigen, dass das vorgeschlagene Modell zur Erfassung computerbezogener Anreizfaktoren empirisch gestutzt werden kann und der Fragebogen gute psychometrische Eigenschaften besitzt. Uberdies konnte partielle Messinvarianz uber Geschlecht und Alter belegt werden. Aspekte der Konstruktvaliditat wurden uber Zusammenhange mit computerbezogenen Personenmerkmalen (z. B. Fertigkeiten) und Personlichkeitsmerkmalen (z. B. Need for Cognition) uberpruft.
Individual Differences and Personality describes how and why personality varies between one person and the next. Unlike books that focus on individual theorists, this textbook focuses on current research and theory on the nature of personality and related individual variations. In addition to covering the Big Five and HEXACO models of personality structure, the book also includes topics often left out of other personality books, including individual differences in mental abilities, religion, politics, and sexuality. It describes the biological bases of personality, including neurotransmitters, brain structures, and hormones. and explains genetic and environmental influences Its discussion of evolutionary function is unique among personality texts. The book is a valuable guide for students in courses on personality, discussing personality measurement, personality traits, and the basic dimensions of personality. This leads to a discussion of the origins of personality, with descriptions of its developmental course, its biological causes, its genetic and environmental influences, and its evolutionary function. Personality disorders are then described, followed by a discussion of the influence of personality on life outcomes in relationships, work, and health. Finally, the book examines in detail the important differences between individuals in the realms of mental abilities, of beliefs and attitudes, and of sexuality. Integrates research findings with real-life outcomes, explaining why personality leads to successes or failures in life Discusses healthy personalities as well as personality disorders New chapter on vocational interests and personality 35% revised content Unique coverage of the evolutionary basis for personality Strong coverage of biology, genetics, neuroscience of personality Testbank for professors.
It is commonly held that range anxiety, in the form of experienced range stress, constitutes a usage barrier, particularly during the early period of battery electric vehicle (BEV) usage. To better understand factors that play a role in range stress during this critical period of adaptation to limited-range mobility, we examined individual differences in experienced range stress in the context of a critical range situation. In a field experiment, 74 participants drove a BEV on a 94-km round trip, which was tailored to lead to a critical range situation (i.e., small available range safety buffer). Higher route familiarity, trust in the range estimation system, system knowledge, subjective range competence, and internal control beliefs in dealing with technology were clearly related to lower experienced range stress; emotional stability (i.e., low neuroticism) was partly related to lower range stress. These results can inform strategies aimed at reducing range stress during early BEV usage, as well as contribute to a better understanding of factors that drive user experience in low-resource systems, which is a key topic in the field of green ergonomics.
The General Internet Attitude Scale (GIAS) is a questionnaire designed to explore the underlying components of the attitudes of individuals to the Internet, and to measure individuals on these attitude components. Previous Internet attitude research is critiqued for its lack of a clear definition of constructs. GIAS was developed starting from the well-established three-component psychological model of attitude (affect, behavior, cognition) into which applicable statements found in previous Internet attitude measures were fitted. GIAS was developed using an iterative psychometric process with four independent samples (N = 2,200). During iterations, the wordings of the items were refined, and exploratory and confirmatory factor analyses identified four underlying factors in the scale: Internet Affect, Internet Exhilaration, Social Benefit of the Internet, and Internet Detriment, all of which had acceptable internal reliabilities. The final instrument contains 21 items and demonstrates strong reliability achieving an overall Cronbach’s alpha value of 0.85. The behavioral component of the three-factor attitude model could not be replicated, although there was a medium, positive correlation between GIAS and a measure of Internet self-efficacy. Attitude and self-efficacy are important personal constructs and may well contribute to the large variance that usability metrics are known to exhibit.