Abbreviated Technology Anxiety Scale (ATAS)

Abstract

The Abbreviated Technology Anxiety Scale (ATAS) is an 11-item self-report psychometric instrument developed by Matthew L. Wilson, Anne Corinne Huggins-Manley, Albert D. Ritzhaupt, and Krista Ruggles (2023) to assess an individual’s state and trait levels of technology anxiety (TA). Built upon contemporary conceptualizations of digital apprehension and human-computer interaction, the ATAS measures personal affective discomfort, apprehension, and negative emotional responses experienced when encountering, utilizing, or anticipating interaction with information and communication technology (ICT). Originating from an initial pool of 21 negatively worded candidate items derived from theoretical operationalizations and extensive literature synthesis, the instrument underwent rigorous iterative pilot testing, expert content validation, and psychometric pruning, culminating in a streamlined unidimensional 11-item scale.

Psychometric evaluation in adult collegiate populations demonstrates robust structural and internal validity. Confirmatory factor analysis (CFA) supports a unidimensional latent model with strong incremental fit indices ($\chi^2(44) = 195.70, p < 0.001$; Comparative Fit Index [CFI] = 0.97; Tucker-Lewis Index [TLI] = 0.97; Root Mean Square Error of Approximation [RMSEA] = 0.10), corroborated by exploratory factor analyses showing a single dominant eigenvalue. The scale exhibits exceptional composite internal consistency, evidenced by a McDonald’s coefficient omega ($\omega$) of 0.95. Criterion and construct validity are established through significant inverse correlations with attitudes toward technology ($r = -0.65, p < 0.001$), computer and digital self-efficacy ($r = -0.47, p < 0.001$), and self-reported frequency of technology engagement ($r = -0.28, p < 0.001$). Designed for administration in educational, occupational, and psychological research environments, the ATAS provides an efficient, reliable measurement tool that mitigates respondent fatigue while preserving nuanced diagnostic sensitivity.

Keywords

Technology Anxiety, Abbreviated Technology Anxiety Scale, ATAS, Computer Anxiety, Human-Computer Interaction, Information and Communication Technology, Negative Emotions, Technostress, Psychometrics, Factor Analysis, Self-Efficacy

Authors

The Abbreviated Technology Anxiety Scale was developed and validated by an interdisciplinary team of educational technology, psychometrics, and human-computer interaction researchers:

• Matthew L. Wilson, Ph.D. (Corresponding Author)
  ORCID: 0000-0003-4548-921X
  Affiliation: Bagwell College of Education, Kennesaw State University, Kennesaw, Georgia, United States
  Email: [email protected]
  Mailing Address: Kennesaw State University, 585 Cobb Avenue, Kennesaw Hall – MD Box #0127, Kennesaw, GA 30144-5591, USA
• Anne Corinne Huggins-Manley, Ph.D.
  Affiliation: Research and Evaluation Methodology Program, College of Education, University of Florida, Gainesville, Florida, United States
• Albert D. Ritzhaupt, Ph.D.
  ORCID: 0000-0002-0655-8633
  Affiliation: Educational Technology Program, School of Teaching and Learning, University of Florida, Gainesville, Florida, United States
• Krista Ruggles, Ed.D.
  Affiliation: School of Education, Utah Valley University, Orem, Utah, United States

Purpose

The primary purpose of the Abbreviated Technology Anxiety Scale (ATAS) is to provide an empirically sound, brief, and unidimensional psychometric instrument capable of reliably measuring an individual’s affective anxiety toward modern digital tools, hardware, software, and networked communication platforms. Over recent decades, the widespread integration of information and communication technology into higher education, workplace environments, and everyday life has fundamentally shifted the nature of computer interaction. Despite ubiquitous exposure, modern computer interfaces, cloud systems, and automated software ecosystems continue to elicit substantial psychological distress, apprehension, and avoidance among diverse user demographics.

Prior legacy instruments measuring technological or computer anxiety—such as the Computer Anxiety Rating Scale (CARS; Heinssen et al., 1987) or the Computer Anxiety Index (CAIN; Maurer & Simonson, 1984)—frequently suffered from methodological and contextual limitations. Many legacy scales contained extensive item inventories (frequently spanning 20 to 40 items), imposing heavy cognitive loads and severe survey fatigue in large-scale evaluative studies. Furthermore, legacy scales were developed during eras dominated by command-line interfaces, stand-alone microcomputers, and centralized mainframe architectures. Consequently, their item content often focused on obsolete hardware characteristics (e.g., fear of damaging diskettes or typing commands incorrectly) rather than contemporary, fluid, interconnected digital ecologies involving touch interfaces, collaborative software, algorithm-driven tools, and web-based educational platforms.

The ATAS was created to fill this methodological void by offering a refined 11-item instrument optimized for efficiency without sacrificing diagnostic fidelity. Specifically, the purpose of the scale encompasses:

• Research Applications in Higher Education: Providing university administrators, instructional designers, and educational researchers with a practical instrument to assess students’ affective readiness for digital coursework, learning management systems (LMS), and hybrid learning configurations.
• Organizational and Industrial Diagnostics: Equipping organizational psychologists and human resources specialists with a standardized metric to evaluate employee resistance, apprehension, and psychological strain during enterprise-wide technological migrations, software rollouts, or automation initiatives.
• Intervention Evaluation: Serving as a sensitive pre- and post-test measurement tool to monitor changes in emotional distress following targeted digital literacy training, technological mentoring programs, or user experience (UX) modifications.
• Theoretical Modeling: Facilitating structural equation modeling (SEM) within information systems research, specifically by capturing the affective dimension of technological aversion within extended formulations of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT).

Psychological Construct

The psychological construct evaluated by the ATAS is technology anxiety (TA), conceptualized as an affective, emotion-focused condition characterized by apprehension, psychological distress, fear, physiological arousal, and intuitive aversion toward interacting with, or anticipating interaction with, information and communication technology. Unlike cognitive constructs such as perceived ease of use or self-efficacy, technology anxiety is grounded in negative affectivity.

To fully understand the construct of technology anxiety as operationalized within the ATAS, it is necessary to contrast it with related psychological phenomena:

• Technology Anxiety vs. Computer Self-Efficacy: Whereas computer self-efficacy (Compeau & Higgins, 1995) represents an individual’s cognitive judgment of their technical capabilities to execute specific computing tasks, technology anxiety reflects an emotional and visceral fear reaction. Although self-efficacy and technology anxiety are inversely related, an individual may conceptually grasp the sequence of steps required to execute a technical task while simultaneously experiencing overwhelming autonomic anxiety, dread, or performance intimidation.
• Technology Anxiety vs. Technostress: Technostress (Ragu-Nathan et al., 2008) is a broad, overarching macro-construct encompassing organizational and transactional phenomena, including technological overload, techno-invasion of personal time, techno-complexity, and techno-insecurity. Technology anxiety functions as an emotional core within this system, capturing the user’s direct, acute affective threat appraisals.
• Technology Anxiety vs. Cyberphobia: While cyberphobia is categorized as a severe, often debilitating clinical phobia requiring clinical diagnosis, technology anxiety exists on a normative continuum. It is experienced to varying degrees across healthy, sub-clinical populations when coping with complex or unfamiliar digital devices.

The ATAS treats technology anxiety as a unidimensional construct focusing on the personal sentiment and negative emotionality induced by technology. The construct manifests along three interrelated experiential domains:

1. Anticipatory Apprehension: Negative affective responses experienced prior to technological interaction, characterized by dread, prospective performance anxiety, fear of failure, and fear of irreversible errors (e.g., accidentally deleting critical files or crashing an organizational infrastructure).
2. Interactional Discomfort and Frustration: Acute stress, impatience, irritability, and cognitive overload experienced during actual engagement with digital platforms, particularly when encountering software bugs, unintuitive user interfaces, error messages, or system latency.
3. Technological Intimidation and Avoidance: Psychological feelings of inadequacy, alienation, or intimidation when confronted with advanced technological paradigms, leading to avoidance behaviors, reliance on others, and resistance to adopting novel technological solutions.

Theoretical Framework

The conceptual and empirical foundation of the Abbreviated Technology Anxiety Scale is rooted in several prominent psychological theories of anxiety, behavioral regulation, and technological adoption:

1. Social Cognitive Theory and Self-Efficacy Theory

Albert Bandura‘s (1986, 1997) Social Cognitive Theory posits that human functioning is mediated by triadic reciprocal determinism among personal factors (cognitive, affective, and biological events), environmental influences, and behavioral patterns. Within this model, physiological and affective states directly inform self-efficacy judgments. When individuals experience heightened autonomic arousal, heart palpitations, or panic-like emotional responses in the presence of digital technology, they interpret these visceral signals as indicators of low capability and impending failure. The ATAS captures these affective states, which fundamentally undermine digital agency, induce behavioral avoidance, and diminish persistence when confronting novel computing tasks.

2. State-Trait Anxiety Theory

Drawing on Charles Spielberger‘s (1983) State-Trait Anxiety framework, anxiety manifests both as a transitory emotional state (A-State)—characterized by subjective feelings of tension, apprehension, and heightened autonomic nervous system activity—and as a relatively stable personality disposition (A-Trait), reflecting generalized vulnerability to perceiving environments as threatening. The ATAS evaluates domain-specific trait technology anxiety: a stable tendency to perceive interactions with information and communication technologies as threatening, which subsequently triggers acute situational state anxiety during computer-mediated activities.

3. The Transactional Model of Stress and Coping

Formulated by Richard Lazarus and Susan Folkman (1984), the Transactional Model of Stress and Coping describes psychological stress as a relationship between the person and the environment that is appraised as taxing or exceeding resources. Technology anxiety emerges during the primary appraisal phase, wherein the technological encounter is evaluated as a personal threat rather than a positive challenge. In the subsequent secondary appraisal, the user evaluates their internal coping resources and technical capabilities as inadequate. The items comprising the ATAS index this persistent negative cognitive-affective appraisal process.

4. The Technology Acceptance Model (TAM)

In Fred Davis’s (1989) Technology Acceptance Model and its extensions (Venkatesh, 2000), technology anxiety is conceptualized as an affective antecedent that exerts strong direct and indirect effects on perceived ease of use (PEOU) and perceived usefulness (PU). High levels of technology anxiety distort a user’s cognitive perception of system usability, increasing perceived mental effort and lowering behavioral intentions to adopt and utilize information systems.

Validity

The validity of the Abbreviated Technology Anxiety Scale was evaluated through a multi-phase validation framework focusing on content validity, construct validity, and criterion-related validity (Wilson et al., 2023).

Content Validity

The scale development began with an initial pool of 21 negatively-worded candidate items. These items were formulated following a comprehensive literature synthesis of historical computer anxiety inventories and theoretical operationalizations of digital apprehension (Wilson, 2018). Content validation was conducted across iterative cycles with subject-matter experts and undergraduate student panels. Problematic, ambiguous, double-barreled, or technologically obsolete items were systematically removed or rephrased. The final 11 items were selected for their clarity, construct coverage, and focus on personal sentiments and negative emotional responses toward modern information and communication technology.

Construct and Criterion-Related Validity

Criterion-related validity was established by examining bivariate correlations between the ATAS and several theoretically linked constructs:

• Attitudes Toward Technology: A strong, statistically significant negative correlation was observed ($r = -0.65, p < 0.001$). This confirms that elevated technological anxiety is closely paired with unfavorable general sentiments, cynicism, and negative attitudes toward digital systems.
• Technology Self-Efficacy: A moderate-to-strong negative correlation was established ($r = -0.47, p < 0.001$). This aligns with Bandura’s theoretical expectation that affective anxiety undermines self-efficacy appraisals regarding computer competence.
• Frequency of Technology Use: A statistically significant negative correlation was found ($r = -0.28, p < 0.001$). While modern academic and professional settings mandate daily digital usage—attenuating the magnitude of behavioral avoidance—higher technology anxiety remains linked to lower spontaneous engagement with technology.

These empirical findings confirm both convergent and discriminant validity, demonstrating that while the ATAS shares variance with related constructs (attitudes and self-efficacy), it captures distinct affective distress rather than merely measuring general digital competence.

Reliability

The internal consistency reliability of the Abbreviated Technology Anxiety Scale was evaluated using McDonald’s coefficient omega ($\omega$), a metric generally preferred over Cronbach’s coefficient alpha ($lpha$) for scales evaluated with factor analysis. McDonald’s omega relaxes the unrealistic assumption of essential tau-equivalence (equal factor loadings across all items) and provides a more accurate, unbiased estimate of scale reliability.

The 11-item ATAS yielded an internal consistency coefficient of:

McDonald’s $\omega = 0.95$

An omega value of 0.95 reflects excellent internal consistency, exceeding recommended thresholds for both group-level academic research ($\omega ge 0.70 – 0.80$) and individual-level diagnostic decisions ($\omega ge 0.90$). The high magnitude of this coefficient indicates minimal measurement error and suggests that the 11 items share strong common variance under a single underlying construct. The homogeneity of the items ensures that aggregate scores provide a stable and precise representation of an individual’s technology anxiety.

Factor Analysis

To investigate the internal latent structure of the ATAS, the scale developers conducted complementary exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) on independent undergraduate samples.

Exploratory Factor Analysis (EFA)

Initial exploratory factor analysis revealed a dominant single-factor solution. The first unrotated factor accounted for the vast majority of the shared variance, exhibiting a dominant eigenvalue relative to all subsequent factors. Examination of the scree plot showed a sharp elbow immediately following the first factor, satisfying classic psychometric criteria (e.g., Cattell’s scree test) for strict unidimensionality.

Confirmatory Factor Analysis (CFA)

To formally test the hypothesized unidimensional model, a CFA was estimated using maximum likelihood techniques. The 11 items were specified to load onto a single latent Technology Anxiety factor. The model demonstrated acceptable to excellent global fit to the empirical data:

• Chi-Square Statistic: $\chi^2(44) = 195.70, p < 0.001$
• Comparative Fit Index (CFI): $0.97$
• Tucker-Lewis Index (TLI): $0.97$
• Root Mean Square Error of Approximation (RMSEA): $0.10$

The incremental fit indices ($ ext{CFI} = 0.97$,$ ext{TLI} = 0.97$) comfortably exceed the standard$ge 0.95$ threshold proposed by Hu and Bentler (1999), demonstrating excellent model fit relative to a null baseline model. Although the RMSEA value of $0.10$ is at the upper acceptable boundary, psychometricians note that in shorter unidimensional scales with modest degrees of freedom and highly correlated item sets, RMSEA can be artificially inflated (Kenny et al., 2015). When viewed in conjunction with the exceptional incremental indices, dominant exploratory eigenvalues, and high internal consistency ($\omega = 0.95$), the unidimensional factor structure of the ATAS is strongly supported.

Instrument / Measurement Tool

The Abbreviated Technology Anxiety Scale is structured as an efficient, self-administered rating instrument designed for rapid completion in digital or paper formats.

• Test Type: Original Rating Scale / Psychological Self-Report Inventory
• Construct Assessed: Technology Anxiety (unidimensional negative affectivity toward ICT)
• Target Population: Adults (18 years and older); validated with undergraduate university students in the United States
• Number of Items: 11 items
• Administration Time: Approximately 2 to 4 minutes
• Response Format: Five-point Likert scale rated as follows:
  • 1 = Strongly disagree
  • 2 = Disagree
  • 3 = Neither agree nor disagree
  • 4 = Agree
  • 5 = Strongly agree
• Scoring Guidelines:
  • All 11 items are negatively phrased to reflect anxiety, discomfort, and apprehension. As a result, no reverse scoring is required.
  • A composite score can be calculated as a summed total score (ranging from 11 to 55) or as a mean item score (ranging from 1.00 to 5.00).
  • Higher scores indicate greater levels of technology anxiety, fear, and emotional distress regarding information and communication technology.

Permissions & Fee and Test Year

• Test Publication Year: 2023
• Original Publication Outlet: Behavior Research Methods (Springer Nature)
• Commercial Fee: No commercial fee is required for non-profit academic research and educational evaluation purposes.
• Permissions and Licensing: The instrument and its developmental findings were published in an academic journal. Researchers wishing to reproduce, adapt, or utilize the full ATAS scale for scholarly, clinical, or institutional assessments should review publisher copyright policies and contact the corresponding author:
  
  Dr. Matthew L. Wilson
  Kennesaw State University
  Email: [email protected]

References

• Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
• Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• Heinssen, R. K., Glass, C. R., & Knight, L. A. (1987). Assessing computer anxiety: Development and validation of the Computer Anxiety Rating Scale. Computers in Human Behavior, 3(1), 49–59. https://doi.org/10.1016/0747-5632(87)90010-0
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486–507. https://doi.org/10.1177/0049124114543236
• Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer Publishing Company.
• Maurer, M. M., & Simonson, M. R. (1984). Development and validation of a measure of computer anxiety. In Proceedings of Selected Research Paper Presentations at the 1984 Convention of the Association for Educational Communications and Technology (pp. 318–330). AECT.
• McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.
• Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Information Systems Research, 19(4), 417–433. https://doi.org/10.1287/isre.1070.0165
• Spielberger, C. D. (1983). State-Trait Anxiety Inventory for Adults: Sampler Set, Manual, Test, Scoring Key. Mind Garden.
• Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872
• Wilson, M. L. (2018). Examining the factor structure and psychometric properties of an instrument to measure technology anxiety (Doctoral dissertation, University of Florida).
• Wilson, M. L., Huggins-Manley, A. C., Ritzhaupt, A. D., & Ruggles, K. (2023). Development of the Abbreviated Technology Anxiety Scale (ATAS). Behavior Research Methods, 55(1), 185–199. https://doi.org/10.3758/s13428-022-01820-9

Items of the Scale