Information Systems Trust–Measurement Model

Abstract

The Information Systems Trust–Measurement Model (Müller & Hertel, 2025) is a psychometric framework designed to capture the dynamic interplay between human operators and contemporary digital technologies within routine workplace environments. Specifically, the instrument assesses how situational, event-specific trust in information systems (IS) influences employees’ cognitive resources, task performance, and psychological well-being. Comprising 37 items operationalized across 12 distinct subscales, the measurement model evaluates personal dispositions (e.g., dispositional trust, technology competence, need for cognition, conscientiousness, need for control), situational system perceptions (event-specific trust, accountability), cognitive load dynamics (distraction, forgetting), and critical occupational outcomes (workplace well-being, strain, self-rated task performance). Most items utilize a 7-point Likert scale ranging from 1 (fully disagree) to 7 (fully agree). Psychometric evaluation using confirmatory factor analysis and structural equation modeling demonstrated good global fit, indicated by a Standardized Root Mean Square Residual (SRMR) of 0.075, alongside unweighted least squares (dULS) and geodesic (dG) discrepancies falling below the 95% quantile bootstrap thresholds. Reliability estimates across the subscales range from adequate to excellent, with Cronbach’s alpha and Spearman-Brown coefficients spanning .55 to .93. Convergent validity was supported by average variance extracted (AVE) metrics exceeding .50 for nearly all constructs, while discriminant validity was confirmed via heterotrait-monotrait ratio (HTMT) values consistently below the stringent 0.85 cutoff. The model provides an empirically sound diagnostic tool for researchers and organizational practitioners examining human-automation interaction, digital stress, and cognitive ergonomics.

Keywords

Information Systems Trust, Cognitive Resources, Human-Computer Interaction, Technological Competence, Occupational Strain, Workplace Well-Being, Job Performance, Need for Control, Need for Cognition, Distraction, Psychometric Evaluation, Cognitive Ergonomics

Authors

The Information Systems Trust–Measurement Model was developed by researchers at the Institute of Psychology, University of Münster, Germany:

• Lea S. Müller, M.Sc. — Institute of Psychology, University of Münster, Fliednerstrasse 21, 48149 Münster, Germany. Email: [email protected].
• Guido Hertel, Dr. rer. nat. — Professor of Organizational and Business Psychology, Institute of Psychology, University of Münster, Fliednerstrasse 21, 48149 Münster, Germany. ORCID: 0000-0002-7754-2786.

Purpose

Modern workplaces require knowledge workers to interact constantly with enterprise software, algorithmic decision-support platforms, automated databases, and artificial intelligence interfaces. While traditional models of human-computer interaction (HCI)—such as the Technology Acceptance Model (TAM)—have emphasized generalized attitudes toward system adoption and perceived usefulness, they often overlook the fine-grained, micro-level fluctuations in cognitive processing and psychological strain that occur during routine work events. The primary purpose of the Information Systems Trust–Measurement Model is to address this gap by assessing event-specific trust in digital systems alongside its proximal cognitive correlates and distal occupational outcomes.

From an applied organizational perspective, digital systems are designed to alleviate human workload through automation and offloading. However, when users do not trust an information system, they frequently engage in continuous verification, parallel tracking, and redundant mental calculations. This vigilance depletes finite executive functioning capacities, leading to workplace burnout, subjective strain, and diminished performance. Conversely, uncritical overreliance (over-trust) without adequate situational awareness can cause missed system errors and catastrophic failures. The Information Systems Trust–Measurement Model serves as an empirical instrument to quantify the delicate equilibrium between trust, cognitive offloading, and user vigilance during specific technological encounters.

The scale was developed to meet rigorous clinical, ergonomic, and occupational research needs. In research settings, it allows investigators to capture episodic, day-level, or event-level interactions between human traits and system behaviors. In industrial and organizational applications, practitioners can deploy the tool to diagnose digital friction, identify sources of technological strain, evaluate human factors during software rollouts, and optimize interface designs to support cognitive recovery and employee well-being.

Psychological Construct

The Information Systems Trust–Measurement Model is a multidimensional architecture composed of 12 distinct latent constructs. These dimensions capture baseline individual differences, situational cognitive appraisals, executive functioning disruptions, and worker well-being:

1. Trust Disposition

Trust disposition reflects a generalized propensity to expect dependability, honesty, and benevolence in others and complex socio-technical environments. Within this instrument, it captures an individual’s baseline readiness to rely on unfamiliar entities before specific technological evidence is established.

2. Technology Competence

This construct represents the user’s subjective sense of mastery, self-efficacy, and operational fluency regarding technical artifacts and digital interfaces. High technology competence functions as a psychological buffer against digital stress and frustration.

3. Need for Cognition (NFC)

Adapted from foundational work by Cacioppo and Petty (1982), need for cognition describes a stable intrinsic motivation to engage in and enjoy effortful cognitive activity. Individuals high in NFC systematically scrutinize information system outputs rather than relying passively on automated heuristics.

4. Conscientiousness

Drawing from the Five-Factor Model of personality, conscientiousness assesses an employee’s tendency toward goal-directed behavior, diligence, thoroughness, and systematic order. In technological contexts, highly conscientious individuals are more likely to double-check automated outputs to maintain high task quality.

5. Need for Control

Adapted from occupational control frameworks (e.g., de Rijk et al., 1998), this subscale measures the psychological desire of the employee to maintain personal agency and autonomous oversight over work processes, resisting automated usurpation of decision-making authority.

6. Event-Specific Trust

The focal psychological construct of the model, event-specific trust refers to the situational psychological state comprising the intention to accept vulnerability based upon positive expectations of the information system’s capability, reliability, and functional integrity during a specific work task.

7. Accountability

Accountability captures the perceived social and organizational pressure to justify one’s decisions, process steps, and work outcomes to supervisors or peers. High accountability increases the perceived risk of unverified technology reliance.

8. Distraction

This cognitive resource indicator assesses the frequency and intensity of attentional fragmentation, intrusive thoughts, and off-task shifts triggered by technological malfunctions, notification overload, or continuous system monitoring.

9. Forgetting

Measuring memory lapses and retrieval failures within the work process, this subscale captures breakdowns in working memory and prospective memory caused by cognitive resource depletion or inappropriate cognitive offloading onto fallible digital tools.

10. Workplace Well-Being

Reflecting the hedonic and eudaimonic states of the employee, this construct measures positive affective experiences, feelings of competence, and psychological flourishing during technology-mediated work events.

11. Strain

Strain captures the adverse psychological and physical costs experienced by the individual, including tension, exhaustion, mental fatigue, and acute techno-stress stemming from demanding system interactions.

12. Task Performance

This subscale evaluates self-rated operational efficiency, output quality, accuracy, and timeliness in completing work tasks assisted by the information system.

Theoretical Framework

The Information Systems Trust–Measurement Model is anchored in several prominent theories spanning cognitive psychology, ergonomics, and organizational behavior:

Human-Automation Trust Theory

The model draws heavily upon Lee and See’s (2004) seminal framework of trust in automation, alongside Mayer, Davis, and Schoorman’s (1995) integrative model of organizational trust. Lee and See defined trust as an attitude that an agent will help achieve an individual’s goals in a situation characterized by uncertainty and vulnerability. Müller and Hertel (2025) contextualize this framework within information systems by distinguishing between stable individual antecedents (trust disposition, need for control) and dynamic, event-specific evaluations of system trustworthiness. When calibration between trust and actual system capability is optimal, users experience efficient cognitive offloading without entering states of disuse or misuse.

Conservation of Resources (COR) Theory

Under Hobfoll’s (1989) Conservation of Resources theory, individuals strive to obtain, retain, and protect valued personal resources, including time, energy, and attentional capacity. In technology-mediated work, reliable information systems serve as structural resources that preserve cognitive energy. However, when trust in a system breaks down, employees must invest compensatory cognitive resources to monitor, cross-check, and correct digital outputs. This ongoing expenditure leads to resource depletion, heightened psychological strain, and cognitive failures such as distraction and forgetting.

Cognitive Load Theory and Distributed Cognition

The measurement model incorporates principles from Cognitive Load Theory (Sweller, 1988) and Distributed Cognition (Hutchins, 1995). Distributed cognition posits that thinking does not occur solely within an individual’s mind but is shared across external tools, artifacts, and social systems. When an employee delegates computation, tracking, or scheduling to an information system, working memory capacity is freed for complex problem-solving. If the system is distrusted or poorly integrated, extraneous cognitive load rises rapidly, precipitating executive control lapses, attentional bottlenecks, and degraded objective performance.

Validity

Müller and Hertel (2025) subjected the Information Systems Trust–Measurement Model to empirical validation using a sample of employed information system users across diverse organizational sectors in Germany. Construct validity was evaluated using both convergent and discriminant criteria within a structural equation modeling framework.

Convergent Validity

Convergent validity was examined using the Average Variance Extracted (AVE) for each latent factor. According to the widely recognized psychometric benchmarks established by Fornell and Larcker (1981), an AVE exceeding 0.50 demonstrates that a latent construct accounts for more than half of the variance in its observed indicators. The empirical findings demonstrated that AVE values exceeded this recommended threshold of 0.50 across all subscales, with only a single minor exception: the Need for Control subscale registered an AVE of 0.47. In psychometric practice, an AVE marginally below 0.50 remains acceptable when the composite reliability of the construct is solid, confirming adequate convergent validity across the measurement model.

Discriminant Validity

Discriminant validity was established through the rigorous Heterotrait-Monotrait Ratio of Correlations (HTMT) method (Henseler et al., 2015). Methodologists recommend that HTMT ratios remain strictly below 0.85 to confirm that adjacent latent constructs represent statistically unique concepts. Across all inter-construct pairings in the measurement model, all HTMT values fell comfortably below the 0.85 cutoff, indicating that event-specific trust is statistically distinct from generalized dispositional trust, technology competence, and broader personality dimensions.

Criterion-Related and Predictive Validity

The predictive validity of the scale was corroborated through structural regression pathways. Event-specific trust significantly predicted lower cognitive distraction and lower memory lapses (forgetting), which in turn mediated the relationship between trust and workplace strain. Furthermore, the model successfully predicted variations in employee well-being and self-reported performance, demonstrating high ecological and criterion validity in live operational settings.

Reliability

The reliability of the Information Systems Trust–Measurement Model was assessed across its 12 subscales using standard internal consistency indices. For subscales consisting of three or more indicators, Cronbach’s alpha ($lpha$) was calculated. For shorter two-item scales, the Spearman-Brown split-half reliability coefficient was computed, consistent with psychometric best practices for small indicator sets (Eisinga et al., 2013).

• Range of Internal Consistency: Reliability coefficients across the 12 subscales ranged from .55 to .93.
• High-Reliability Subscales: Core outcome measures, including well-being, strain, and event-specific trust, demonstrated high internal consistency ($lpha ge .85$).
• Moderate/Lower-Reliability Subscales: Certain brief subscales (such as those measuring need for control or specific two-item individual difference dimensions) exhibited lower internal consistency coefficients approaching .55 to .65. In exploratory and multi-construct structural modeling, lower values in two-item scales can be psychometrically defensible provided their factor loadings and discriminant validity remain robust.

Overall, the reliability profile indicates that the measurement model reliably captures both stable personal dispositions and fluctuating cognitive-affective states in organizational environments.

Factor Analysis

To examine the dimensional integrity and structural architecture of the 37-item instrument, Müller and Hertel (2025) conducted a comprehensive Confirmatory Factor Analysis (CFA). Rather than relying solely on traditional covariance-based fit indices, the authors evaluated the goodness of fit of the saturated structural model using composite modeling fit metrics, including the Standardized Root Mean Square Residual (SRMR), the unweighted least squares discrepancy ($d_{ ext{ULS}}$), and the geodesic discrepancy ($d_{ ext{G}}$).

Model Fit Metrics and Empirical Cutoffs

• SRMR = 0.075: The model yielded an SRMR of 0.075, satisfying the conventional benchmark of $le 0.080$ proposed by Hu and Bentler (1999). This indicates that the average discrepancy between observed and model-implied correlation matrices is acceptably small.
• Discrepancy Indices ($d_{ ext{ULS}}$ and $d_{ ext{G}}$): To rigorously test model specification, bootstrap-based exact fit testing was conducted. Both the unweighted least squares discrepancy ($d_{ ext{ULS}}$) and the geodesic discrepancy ($d_{ ext{G}}$) remained strictly below their corresponding 95% quantile reference distributions ($p > .05$), indicating that the specified 12-factor latent structure adequately reproduces the empirical data without significant structural misspecification.

The confirmatory factor model confirmed that items loaded significantly onto their designated latent constructs, providing clear statistical justification for treating event-specific trust, cognitive load parameters (distraction, forgetting), personal traits, and occupational outcomes as discrete structural factors.

Instrument / Measurement Tool

The operational specifications of the Information Systems Trust–Measurement Model are outlined below:

• Test Type: Standardized self-report psychometric inventory / psychological questionnaire.
• Target Population: Employed adults (18 years and older) who interact with information systems as part of their routine occupational duties.
• Total Item Count: 37 items.
• Subscale Architecture (12 Dimensions):
  • Trust Disposition: General tendency to trust external agents.
  • Technology Competence: Subjective mastery and comfort with digital tools.
  • Need for Cognition: Preference for effortful, analytical thinking.
  • Conscientiousness: Workplace thoroughness, order, and precision.
  • Need for Control: Desire for operational autonomy and process oversight.
  • Event-Specific Trust: Situational trust placed in the active information system.
  • Accountability: Perceived obligation to explain or justify task outcomes.
  • Distraction: Attentional fragmentation and intrusive off-task thoughts.
  • Forgetting: Memory lapses, working memory overload, and prospective omission.
  • Workplace Well-Being: Hedonic and psychological thriving at work.
  • Occupational Strain: Tension, mental exhaustion, and somatic fatigue.
  • Task Performance: Self-evaluated accuracy, speed, and overall output quality.
• Response Format: Most items are administered on a 7-point Likert scale:
  • 1 = Fully disagree
  • 2 = Disagree
  • 3 = Somewhat disagree
  • 4 = Neutral / Neither agree nor disagree
  • 5 = Somewhat agree
  • 6 = Agree
  • 7 = Fully agree
• Scoring and Aggregation Rules:
  • Negatively keyed items must be reverse-coded prior to composite index calculation ($x_{ ext{recoded}} = 8 – x$).
  • Individual dimension scores are computed by calculating the arithmetic mean of the items comprising each subscale.
  • Higher subscale scores denote higher levels of the respective psychological construct (e.g., higher event trust, greater cognitive distraction, or elevated strain).

Permissions & Fee and Test Year

• Test Year: 2025 (Published in Ergonomics; initial online advance release 2023).
• Commercial Status: Non-commercial instrument. No licensing fee is required for non-profit academic research, scientific investigations, or educational instruction.
• Permissions and Access: The complete 37-item battery was published in the supplementary appendix of the original publication (Müller & Hertel, 2025, Appendix, p. 36). Researchers seeking to reproduce, translate, or adapt the inventory for clinical, industrial, or organizational research should contact the corresponding author:

  Lea S. Müller, M.Sc.
  University of Münster, Institute of Psychology
  Fliednerstrasse 21, 48149 Münster, Germany
  Email: [email protected]

References

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• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Items of the Scale