Digital archetypes: a tool for understanding personality characteristics in the digital culture

The paper situates organizational culture as the shared beliefs, behaviors, values, and practices that align collaborators toward common goals, and frames digital culture as the manifestation of these elements under the influence of digital technologies. Individuals differ markedly in their readiness to adopt emerging technologies and to cope with uncertainty and rapid change. Building on personality theories and archetype models (e.g., Jung; DISC), the authors aim to construct a conceptual and psychometric foundation to identify digital archetypes that explain behavior toward digitization and change. The stated purpose is to enable better-balanced innovation and digitization teams, improve communication, and leverage distinct personality tendencies for project execution in hyper-digital contexts.

The paper critically reviews the DISC tradition and related archetype-based personality models, focusing on psychometric shortcomings in widely used reports and validations (e.g., Inscape Publishing, John Wiley & Sons, Price, Prochaska et al., Roodt). It details missing or insufficient practices for establishing internal structure validity, such as appropriate inter-item correlation matrices (tetrachoric/polychoric), KMO and Bartlett tests, item-level MSA, factor analytic choices and rival model comparisons, factor score computation methods, and justification for auxiliary techniques like cluster analysis or MDS. The authors argue that prior DISC validations often commit confirmatory bias, fail to minimize factor indeterminacy, and conflate scales with factors. They emphasize principles of orthogonality and univocity to ensure isomorphism between theory and measurement, critique the use of summed scores, and note that evidence presented via inter-scale correlations contradicts the orthogonal structure implied by DISC’s circumplex model. Table 1 summarizes relevant research spanning archetypes, DISC applications in organizations, and validity/psychometric guidance. The review concludes that a rigorous, item-level factor analytic approach with refined factor score computation is necessary, and that many organizational uses of DISC may lead to unfair or invalid decisions without such evidence.

Conceptual model: The authors adapt the DISC imaginary model to define a Digital Personality Matrix comprising two orthogonal axes: (1) attitude and openness to change (Explorer vs. Conservative) and (2) attitude toward the use of digital tools (Digital vs. Analog). This yields four digital archetypes: Innovative (Digital/Explorer), Visionary (Digital/Conservative), Cooperative (Analog/Explorer), and Traditional (Analog/Conservative). Profiles: Individuals often express multiple archetype tendencies; combining a primary and secondary archetype yields practical digital profiles such as Flexible, Pioneer, Versatile, Perseverant, Reliable, Cautious, Inspirational, Executor, Methodical, Strategist, Collaborative, and Perfectionist. Operationalization: The model is intended for psychometric formalization with orthogonal factors and refined factor score computation to preserve independence, enabling use as predictors in organizational contexts. Case study design: To explore practical application, the authors conducted an assessment within a corporate digital transition program. Steps (Fig. 5): (1) Creating the digital personality model; (2) Applying the assessment under uniform conditions; (3) Building a cultural radar to visualize team personality distribution; (4) Implementing tailored strategies per segment to improve communication, conflict resolution, and change readiness. Sample and instrument: A group of 200 employees from a specific project completed a digital personality questionnaire comprising 10 variables to position each member within the archetype space and assign a profile. Intervention: Based on the cultural radar, targeted change management, mobilization, communication, and training strategies were deployed. Examples: For a large Methodical segment, reframing the meaning of ‘error’ consistent with agile practices and establishing rigorous control processes reduced blame-seeking and increased solution-finding. For Pioneers, pairing with Methodicals grounded ideation in feasibility. For Perseverants, emphasizing human dignity and role reconfiguration reduced fears of displacement by automation. Data availability: Data are available from the corresponding author upon reasonable request.

Distribution of profiles in the 200-person case study indicated a concentration in a few profiles: Methodical accounted for 32% of the team; Persevering 42%; Pioneer 21%. Several other profiles appeared at low frequencies: Reliable 0.5%, Cautious 0.5%, Flexible 0.5%, Executor 0.5%, Strategist 0.5%, Versatile 0.5%, Inspiring 0.5%, Perfectionist 0.5%, and Collaborative 1% (Table 4). Targeted strategies aligned to dominant profiles yielded qualitative improvements: teams spent more time on corrective solutions rather than blame; pairing visionary/innovative thinkers (Pioneers) with Methodicals created ‘grounded’ innovation; reframing roles for Perseverants increased buy-in for automation and digitization. Overall, the model facilitated segmentation, communication tailoring, and improved collaboration and change readiness in a high-pressure digital initiative.

The findings suggest that identifying digital archetypes and profiles can meaningfully inform change management in technology projects by aligning strategies to the distribution of personality tendencies. Segment-specific interventions improved error-correction behaviors, communication, and cross-profile collaboration (e.g., pairing ideators with methodical executors). The orthogonal, two-axis framing provides a practical lens to balance innovation with stability and human-centric service with digitization. Conceptually, the model addresses the research aim of constructing a foundation to recognize digital personality patterns that enhance team design and performance in transformation contexts. The authors argue that robust psychometric validation (ensuring orthogonality, univocity, refined factor scoring) is necessary to generalize predictive use, but even the initial application demonstrates managerial utility for team dynamics, urgency alignment, and resistance mitigation.

This work proposes a conceptual digital archetype model grounded in two orthogonal axes—interaction with technology and openness to change—yielding four archetypes and multiple combinatory profiles. A real-world case study illustrates how diagnosing a team’s archetype distribution enables tailored strategies that improve collaboration, error management, and adoption of digital tools. Managerially, the model offers a practical framework to form balanced teams and design communications that respect diverse styles without privileging one over another. Future research will refine the psychometric model, execute item-level factor analyses with rival model comparisons, employ refined factor score computations, and validate norms to ensure diagnostic precision and fairness. Broader applications may include sociometric analysis for team configuration and standardized decision rules in recruitment and team assembly for digital initiatives.

The current study’s psychometric validation is incomplete: controls for factor indeterminacy, capitalization on chance, and confirmatory bias were not yet implemented; prior literature often relied on unrefined scoring methods. The case study used a single-organization sample with n=200 and qualitative outcomes, limiting generalizability. A fully specified specification table, item-level loadings, rival model testing, and adequate sample sizes for robust CFA/IFA and norm development remain pending. Data collection for comprehensive validation requires additional time and larger, representative samples to ensure statistical power and stable parameter estimates.