Smart Tools Proneness Questionnaire

1. Abstract

The Smart Tools Proneness Questionnaire (STP-Q) is a psychometric assessment instrument designed to operationalize and quantify an individual’s general behavioral, cognitive, and affective inclination to adopt, integrate, and depend upon smart technological systems in everyday life. Developed initially in ergonomics and cyberpsychology contexts and subsequently adapted cross-culturally, the instrument captures human-technology interaction patterns beyond the reductive metrics of screen time, device ownership, or acute technological pathology. The questionnaire assesses smart technology disposition across a validated tripartite structural framework comprising three latent dimensions: Utilitarian Use (task efficiency, organizational facilitation, and productivity-oriented adoption), Hedonic and Social Use (recreational engagement, entertainment seeking, and interpersonal communication mediation), and the Inclination to Delegate Tasks (psychological comfort with algorithmic autonomy, cognitive offloading, and relinquishing direct agentic control to artificial intelligence and automated devices). Across its psychometric evaluations, most notably demonstrated in its Turkish validation by Esen, Türkmen, and Düger (2024), the instrument demonstrates high internal consistency (total scale Cronbach’s α = .954) and test-retest temporal stability across an intraclass correlation coefficient (ICC = .851, 95% CI [.812, .883]). Exploratory factor analysis confirms the retention of the original three-factor architecture, accounting for substantial common variance without construct distortion. Convergent validity analyses confirm theoretically expected positive associations with related digital constructs, such as digital learning readiness and nomophobia. Administered as a standardized self-report tool on a multi-point Likert scale, the STP-Q provides researchers, cognitive ergonomists, organizational psychologists, and digital health practitioners with an empirical mechanism to examine individual differences in algorithmic trust, technological adaptation, and digital transformation.

2. Keywords

Psychometrics, Smart Tools Proneness Questionnaire, smart technology adoption, human-technology interaction, cognitive offloading, algorithmic delegation, digital behavior, technology acceptance, cross-cultural validation, factor analysis, cyberpsychology.

3. Authors

The original conceptualization and development of the Smart Tools Proneness Questionnaire was authored by:

• Jordan Navarro — Laboratoire d’Étude des Mécanismes Cognitifs (EMC), Université Lumière Lyon 2, Lyon, France.
• Associated research collaborators in cognitive ergonomics and human factors engineering (Navarro et al., 2022).

The cross-cultural adaptation, structural psychometric evaluation, and linguistic validation for Turkish-speaking populations were conducted by:

• Serdar Yılmaz Esen — Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Hacettepe University, Ankara, Turkey.
• Ceyhun Türkmen (Corresponding Author) — Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Hacettepe University, Ankara, Turkey. Email: [email protected]
• Tülin Düger — Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Hacettepe University, Ankara, Turkey.

4. Purpose

The pervasive diffusion of ubiquitous computing, mobile computing ecosystems, the Internet of Things (IoT), ambient intelligence, and generative artificial intelligence has fundamentally shifted the nature of human activity. While traditional computational technologies served primarily as passive instruments for information retrieval or deterministic task execution, modern “smart tools” possess varying degrees of contextual awareness, adaptive learning, autonomy, and proactivity. Despite this evolution, existing assessment methodologies in behavioral psychology and psychometrics have historically remained polarized between two functional extremes:

1. Pathology-Oriented Diagnostic Metrics: Instruments designed to identify compulsive, maladaptive, or clinical phenomena, such as the Internet Addiction Test (IAT), generalized problematic internet use scales, and screen dependency inventories. These tools operate on a deviance paradigm, focusing exclusively on negative psychological, occupational, or social sequelae.
2. Macro-Systemic Technology Acceptance Frameworks: Instruments rooted in classical management information systems paradigms, such as the Technology Acceptance Model (TAM) by Davis (1989) or the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. (2003). While valuable for forecasting workplace software deployment, these frameworks emphasize organizational utility, extrinsic managerial pressures, and localized functional perceptions (“perceived usefulness” and “perceived ease of use”), frequently ignoring idiosyncratic, home-based, or continuous lifestyle predispositions.

The Smart Tools Proneness Questionnaire (STP-Q) was established to bridge this diagnostic divide. The scale measures an individual’s general baseline propensity, psychological willingness, and continuous behavioral readiness to embed automated and smart tools across routine domains of living. Rather than measuring whether an individual uses a specific branded application or diagnosing clinical technological distress, the STP-Q provides an empirical evaluation of individual differences along a continuum of technological receptivity and cognitive delegation.

In research contexts, the scale enables empirical investigations into human-computer interaction (HCI), cyberpsychology, and cognitive ergonomics. It allows scholars to explore why individuals with identical socioeconomic backgrounds, educational access, and technical competence demonstrate divergent behaviors regarding technology integration. In clinical and occupational health settings, the STP-Q provides an objective baseline to evaluate how relying on automated decision aids correlates with perceived cognitive load, executive function fatigue, occupational burnout, and digital stress. Furthermore, in commercial user-experience (UX) research and interface engineering, the instrument assists designers in segmenting target populations based on technological readiness, identifying cohorts who actively desire algorithmic intervention versus those who exhibit resistance or psychological friction toward non-deterministic system autonomy.

5. Psychological Construct

The latent construct quantified by the instrument is Smart Tool Proneness (STP). Smart tool proneness is conceptualized as an enduring psychological and behavioral orientation characterized by the habitual preference for, affective openness toward, and functional reliance upon automated, connected, and context-aware systems to manage cognitive, communicative, and task-oriented demands. Rather than operating as a unidimensional construct, the STP-Q operationalizes smart tool proneness as a multidimensional architecture composed of three interconnected sub-constructs:

1. Utilitarian Use

The Utilitarian Use dimension reflects an instrumental, performance-oriented psychological motivation. Rooted in traditional rational-action theories of human factors engineering, this subscale captures the degree to which an individual views smart tools as functional amplifiers of personal productivity, efficiency, and resource optimization. Utilitarian proneness manifests in using intelligent calendars that dynamically adjust schedules, algorithmic task managers, automated home cleaning or climate-control routines, and automated navigation systems that reroute based on predictive traffic algorithms. Key behavioral markers include:

• Systematic selection of automated workflows to minimize temporal investment in mundane tasks.
• Deliberate reliance on smart applications to structure, organize, and execute professional or domestic duties.
• An explicit cognitive appraisal that automated tools maximize functional efficacy and reduce physical effort.

2. Hedonic and Social Use

The Hedonic and Social Use dimension shifts the focus from task completion toward affective fulfillment, playfulness, entertainment, and digitally mediated interpersonal connectivity. This facet acknowledges that technology adoption is often propelled by intrinsic motivations, dopamine-mediated reward loops, and social cohesion rather than utilitarian efficiency. Individuals scoring high in this domain deliberately leverage smart tools for leisure, digital media consumption, aesthetic satisfaction, and the ongoing maintenance of social relationships. Psychological characteristics include:

• Utilizing intelligent streaming platforms that curate personalized algorithmic entertainment.
• Engaging with smart mobile ecosystems to facilitate persistent, ambient social contact via algorithmic communication channels.
• Deriving positive affect, sensory pleasure, and leisure engagement directly through interaction with intelligent interfaces.

3. Inclination to Delegate Tasks

The Inclination to Delegate Tasks represents the most theoretically novel and psychometrically distinct dimension of the STP-Q. This component measures an individual’s psychological comfort, cognitive readiness, and implicit trust in relinquishing personal control, agency, and cognitive labor to non-human algorithmic agents. While utilitarian and hedonic dimensions capture the goals of technology use, delegation proneness measures the structural transformation of agency between human and machine. Individuals exhibiting high delegation proneness do not simply use tools; they actively surrender executive tasks, memory retention, navigation decisions, and predictive selections to automated systems. Psychological features include:

• Cognitive Offloading: The active, comfortable distribution of cognitive processing demands (such as prospective memory, mental calculations, and spatial mapping) onto external digital architectures.
• Algorithmic Trust: A psychological threshold wherein the user accepts the fallibility or non-transparency of an automated system, judging algorithmic error rates to be acceptable relative to human performance.
• Absence of Agency-Loss Anxiety: A low manifestation of the psychological discomfort, loss-of-control distress, or technophobia commonly experienced when an external entity autonomously regulates personal parameters.

6. Theoretical Framework

The conceptual foundation of the Smart Tools Proneness Questionnaire integrates principles from cognitive psychology, social-cognitive theory, human factors engineering, and philosophy of mind. Rather than adopting an isolated behavioral model, the scale reflects a synthesis of four theoretical traditions:

The Extended Mind Thesis and Cognitive Offloading

At its philosophical and neuropsychological core, the STP-Q, particularly its task-delegation facet, aligns with the Extended Mind Thesis formulated by Clark and Chalmers (1998). According to this perspective, human cognitive architecture is not strictly bounded by the biological skull or the neuromuscular system; rather, the mind opportunistically couples with external epistemic artifacts to form an extended cognitive loop. When an individual relies on a smart algorithmic engine to recall facts, calculate trajectories, or initiate contextual reminders, the technology functions as externalized cognitive machinery.

Complementing this is the concept of cognitive offloading (Risko & Gilbert, 2016), which posits that humans consistently engage physical actions to alter the computational demands of an internal mental task. Smart tool proneness operationalizes cognitive offloading at a modern, automated scale. Rather than expending biological resources on prospective memory, spatial tracking, or complex chronological planning, an individual exhibiting high STP-Q traits offloads these demands onto digital cognitive tools, preserving finite working memory and attentional resources for higher-order cognitive processing or affective relaxation.

Motivational Dualisms: Utilitarian vs. Hedonic Technology Paradigms

The conceptual segregation of technology use into utilitarian and hedonic dimensions draws directly upon the dual-system motivational frameworks articulated by van der Heijden (2004) and classical consumer psychology. In classical models like the Technology Acceptance Model (Davis, 1989), the primary determinants of adoption are instrumental, utilitarian perceptions of usefulness. However, hedonic information systems are designed to provide self-fulfilling value rather than instrumental value, prioritizing subjective enjoyment, immersion, and leisure. The STP-Q integrates both paradigms, treating utilitarian and hedonic inclinations not as mutually exclusive orientations, but as concurrent orthogonal motives that operate simultaneously within an individual’s digital behavioral repertoire.

Algorithmic Trust, Autonomy, and Parasocial Automation

The delegation construct intersects with modern frameworks of human-autonomy teaming and human-AI trust calibration (Parasuraman et al., 2000; Lee & See, 2004). Relinquishing task control to a smart tool requires trust across three dimensions: performance (predictability and reliability), process (an understanding of the algorithm’s decision rules), and purpose (the perceived benevolence of the operating system). The STP-Q measures the individual threshold at which this trust translates into active delegation. Individuals with high proneness show minimal psychological friction when transitioning control from manual to shared or supervisory automation, representing an adaptive alignment with modern agentic computing.

7. Validity

The psychometric validity of the Smart Tools Proneness Questionnaire has been established through both original exploratory construction and cross-cultural validation protocols. Evidence across structural, convergent, and construct paradigms supports the scale’s theoretical foundation.

Cross-Cultural and Content Validity

The validation of the STP-Q into Turkish by Esen, Türkmen, and Düger (2024) followed the standardized, multistage cross-cultural adaptation guidelines proposed by Beaton et al. (2000). The linguistic and cultural adaptation included:

• Independent forward-translation into the target language by bilingual psychometric and clinical specialists.
• Synthesis of target-language drafts by an expert panel to address idiomatic and cultural nuances.
• Blind back-translation into the original source language by independent linguists unfamiliar with the initial scale.
• Expert committee evaluation ensuring semantic, idiomatic, experiential, and conceptual equivalence.
• Cognitive debriefing pre-testing in a pilot sample to verify item clarity, syntactic comprehensibility, and absence of conceptual ambiguity before field deployment.

Convergent and Discriminant Construct Validity

To examine convergent validity, Esen et al. (2024) administered the adapted STP-Q alongside established psychometric instruments measuring related digital behaviors, specifically the E-Learning Readiness Scale (Alem et al., 2016; Bircan & Türkmen, 2022) and the Nomophobia Questionnaire (NMP-Q) (Yıldırım & Correia, 2015).

Theoretical modeling indicates that smart tool proneness should correlate positively with digital educational readiness (reflecting utilitarian competence and system efficacy) as well as with nomophobia (reflecting the psychological cost of relying heavily on smart devices):

• Association with E-Learning Readiness: A statistically significant positive correlation was observed between total STP-Q scores and the E-Learning Readiness Scale. Participants who exhibited higher baseline inclinations toward utilitarian tool adoption and task delegation showed greater self-efficacy, digital competence, and psychological readiness to engage with distributed virtual learning environments.
• Association with Nomophobia (NMP-Q): A statistically significant positive correlation was documented between the STP-Q and total NMP-Q scores. Nomophobia—defined as the situational anxiety or distress experienced when separated from one’s smartphone—theoretically mirrors high smart tool integration. High scores on the Inclination to Delegate Tasks and Hedonic and Social Use subscales were particularly correlated with elevated distress upon device separation. When individuals externalize cognitive organization and social interaction to smart devices, the temporary absence of those systems triggers acute operational and psychological friction.

8. Reliability

The STP-Q demonstrates high reliability across both internal consistency and temporal stability metrics, showing that it measures the latent construct with minimal measurement error.

Internal Consistency

In the cross-cultural psychometric investigation conducted by Esen et al. (2024) on a cohort of 374 adult participants, the internal consistency of the total instrument and its individual dimensions was evaluated via Cronbach’s alpha (α):

• Total Scale: α = .954, indicating excellent internal consistency exceeding standard psychometric benchmarks (α ≥ .80 to .90) for clinical and research assessments.
• Utilitarian Use Subscale: Demonstrated strong internal consistency, with alpha coefficients exceeding .85, confirming cohesive measurement of functional motivations.
• Hedonic and Social Use Subscale: Exhibited high inter-item homogeneity with alpha coefficients consistently observed above .85.
• Inclination to Delegate Tasks Subscale: Achieved robust internal reliability (α > .80), confirming that items assessing algorithmic trust and cognitive offloading share common latent variance.

Temporal Stability (Test-Retest Reliability)

To ensure the instrument captures a stable behavioral and psychological trait rather than transient digital engagement states, test-retest reliability was evaluated using the Intraclass Correlation Coefficient (ICC) under a two-way mixed-effects model with absolute agreement (Weir, 2005; Koo & Li, 2016):

• Observed Total Scale ICC: .851 (95% Confidence Interval: [.812, .883]).
• Interpretation: According to classical measurement guidelines (Terwee et al., 2007; Koo & Li, 2016), ICC values between .75 and .90 reflect good-to-excellent reliability. This confirms that an individual’s smart tool proneness profile remains stable over time in the absence of targeted cognitive or behavioral interventions.

9. Factor Analysis

The structural validity of the STP-Q was investigated using both Exploratory Factor Analysis (EFA) and latent structural validation paradigms to confirm that the empirical clustering of items mirrors the theorized construct.

Exploratory Factor Analysis (EFA) Parameters

In the evaluation conducted by Esen, Türkmen, and Düger (2024), suitability of the dataset (N = 374) for factor extraction was first confirmed using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity:

• The KMO statistic substantially exceeded the recommended .80 threshold (KMO > .90), confirming strong sampling adequacy for latent structure detection.
• Bartlett’s Test of Sphericity yielded a highly significant result (p < .001), rejecting the identity matrix null hypothesis and confirming sufficient inter-item correlations.

Factor Solution and Loadings

Factor extraction was conducted using principal axis factoring with oblique rotation (e.g., Promax or Direct Oblimin), allowing the latent dimensions to correlate naturally based on real-world behavioral tendencies. The analysis confirmed the retention of a clear three-factor architecture:

1. Factor 1: Utilitarian Use: Items assessing operational efficiency, routine scheduling, productivity enhancement, and systematic task automation loaded strongly onto this factor. Salient item factor loadings consistently exceeded .55 to .60, showing high convergence and negligible cross-loadings.
2. Factor 2: Hedonic and Social Use: Items measuring entertainment, social connection, media curation, and recreational engagement formed a second discrete dimension. Factor loadings across this domain were uniformly robust, explaining a distinct block of common variance.
3. Factor 3: Inclination to Delegate Tasks: Items measuring cognitive offloading, system delegation, autonomous execution, and reliance on algorithmic agency formed the third factor. The empirical emergence of this factor confirms that willingness to surrender manual control represents a distinct dimension separate from general utilitarian tool usage.

The three-factor solution accounts for a substantial proportion of the total variance across the item pool. Inter-factor correlations were moderate to strong and statistically significant, confirming that while these three dimensions reflect distinct psychological behaviors, they remain unified within the broader latent construct of Smart Tool Proneness.

10. Instrument / Measurement Tool

The Smart Tools Proneness Questionnaire (STP-Q) is a standardized, self-report psychometric instrument. Its structural and administrative characteristics include:

• Test Type: Standardized self-report rating scale / psychometric questionnaire.
• Administration Format: Paper-and-pencil questionnaire or digital self-administered assessment via electronic survey environments.
• Target Population: General adult population (individuals aged 18 years and older who interact with modern digital devices).
• Estimated Completion Time: Approximately 5 to 10 minutes.
• Structural Framework: Multidimensional (3 distinct subscales):
  • Subscale 1: Utilitarian Use
  • Subscale 2: Hedonic and Social Use
  • Subscale 3: Inclination to Delegate Tasks
• Response Scale: Multi-point Likert scale (typically scored on a 5-point Likert gradient ranging from 1 = “Strongly Disagree” to 5 = “Strongly Agree”).
• Scoring Procedures:
  • Subscale Scores: Calculated by summing or averaging the items designated to each respective factor. This yields individual profiles across Utilitarian, Hedonic/Social, and Delegation proneness.
  • Total Global Composite Score: Derived by aggregating all item scores across the entire instrument. Higher composite scores reflect an elevated overall disposition, behavioral tendency, and psychological readiness to adopt, integrate, and rely upon smart tools.

11. Permissions & Fee and Test Year

The Smart Tools Proneness Questionnaire was originally introduced to the scientific literature by Jordan Navarro and colleagues in 2022 (Navarro et al., 2022). The cross-cultural adaptation and structural validation for Turkish language contexts was conducted and published in 2024 by Serdar Yılmaz Esen, Ceyhun Türkmen, and Tülin Düger (Esen et al., 2024).

Licensing and Accessibility: The adaptation published in PLOS ONE operates under the open-access Creative Commons Attribution License (CC BY 4.0). This framework allows unrestricted academic, educational, and research use, provided the original authors and primary source are properly cited. Researchers wishing to utilize, translate, or adapt the STP-Q for academic investigations are encouraged to review the primary publications and contact the corresponding authors (e.g., Ceyhun Türkmen, [email protected]) for formal institutional documentation, standardized linguistic modules, or specific subscale item distributions.

12. References

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Bircan, M. A., & Türkmen, C. (2022). Validity and reliability study for the Turkish adaptation of the e-learning readiness scale. Pedagogical Perspectives, 1(2), 89–97. https://doi.org/10.29329/pedper.2022.493.2

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Navarro, J., Osiurak, F., & Reynaud, E. (2022). Development of the Smart Tools Proneness Questionnaire (STP-Q): An instrument to assess the individual propensity to use smart tools. Ergonomics, 65(12), 1639–1653. https://doi.org/10.1080/00140139.2022.2048895

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13. Items of the Scale