Customer Data Falsification Intentions Scale (CDFI)

1. Abstract

The Customer Data Falsification Intentions Scale (CDFI) is a psychometric instrument designed to quantify a consumer’s deliberate inclination to provide inaccurate, fabricated, or altered personal information when interacting with commercial entities. Originally adapted by Martin, Borah, and Palmatier (2017) from foundational research on online consumer privacy protection behaviors (Lwin, Wirtz, & Williams, 2007), the scale captures a distinct form of proactive consumer self-defense in digital transactions. Comprising three unidimensional items administered via a 7-point Likert scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”), the instrument exhibits robust psychometric properties across diverse empirical consumer research domains. Psychometric evaluations routinely demonstrate high internal consistency (composite reliability and Cronbach’s alpha values typically exceeding .90), strong convergent validity with related constructs such as perceived data vulnerability and privacy concern, and distinct discriminant validity separating intentional falsification from passive privacy management strategies, such as transaction abandonment or ad-blocking adoption. Structural equation modeling and confirmatory factor analyses confirm an unambiguous single-factor structure with standardized factor loadings exceeding .85. By positioning data fabrication as an active coping mechanism triggered by perceived institutional vulnerability and emotional violation, the CDFI provides marketers, behavioral scientists, and psychometricians with an empirically rigorous tool for evaluating the hidden costs of aggressive consumer data harvesting practices.

2. Keywords

Customer Data Falsification Intentions Scale, CDFI, consumer privacy, data vulnerability, information fabrication, data poisoning, privacy calculus, coping mechanism, psychometrics, marketing ethics

3. Authors

The scale adaptation and validation in contemporary customer relationship management (CRM) and data privacy literature was authored by:

• Kelly D. Martin — Professor of Marketing and University Board of Governors Professor, College of Business, Colorado State University, Fort Collins, Colorado, USA.
• Abhishek Borah — Associate Professor of Marketing, INSEAD, Fontainebleau, France (formerly at the Foster School of Business, University of Washington).
• Robert W. Palmatier — Professor of Marketing and John C. Narver Chair in Business Administration, Foster School of Business, University of Washington, Seattle, Washington, USA.

The original conceptual foundation and item pool were derived from research conducted by Michael O. Lwin (Nanyang Technological University), Jochen Wirtz (National University of Singapore), and Jerome D. Williams (Rutgers University).

4. Purpose

The primary purpose of the Customer Data Falsification Intentions Scale (CDFI) is to evaluate the extent to which consumers intentionally distort the personal data they disclose to an enterprise. In an era dominated by ubiquitous tracking, algorithmic profiling, and big data monetization, commercial organizations consistently demand proprietary user information—including legal names, demographic characteristics, physical addresses, telephone contacts, and psychographic preferences—as a prerequisite for service access, discount redemption, or account creation. When individuals perceive that their informational privacy is threatened or that corporate surveillance practices exceed normative boundaries, they experience psychological distress and perceived vulnerability. The CDFI measures the actionable behavioral manifestation of this distress: proactive data obfuscation or “data poisoning.”

From an applied research perspective, the CDFI serves as a vital diagnostic instrument for empirical marketing, data governance, and consumer psychology. Rather than assuming that consumers merely act as passive participants in exchange networks who either comply completely or opt out entirely, the CDFI operationalizes a nuanced, subversive behavioral response. Consumers who cannot easily exit an exchange ecosystem due to monopolistic service provision or network lock-in frequently resort to furnishing synthetic or altered personal data. Measuring these falsification intentions is critical because synthetic and corrupted data undermines corporate analytical infrastructure, distorts machine learning model training, biases customer segmentation algorithms, and impairs predictive customer lifetime value calculations.

Theoretically, the scale enables investigators to test structural models examining how data breach announcements, opaque terms-of-service agreements, and unauthorized secondary data sharing degrade consumer cognitive trust and elicit feelings of emotional violation. In behavioral settings, researchers employ the CDFI to model how psychological reactance and privacy cynicism directly induce transactional friction, empowering scholars to quantify the precise point at which data acquisition policies backfire against firm performance.

5. Psychological Construct

The psychological construct assessed by the Customer Data Falsification Intentions Scale is a conscious, goal-directed behavioral intention characterized by the deliberate misrepresentation, fabrication, or modification of personal identifying details during consumer-firm interactions. Constructually, data falsification is categorized as an active, covert self-protective coping mechanism within the broader taxonomy of privacy-preserving behaviors.

To fully delineate the construct, it is necessary to contrast it with conceptually adjacent privacy management behaviors:

• Passive Non-Disclosure (Refusal or Abandonment): This entails immediately terminating a transaction, closing a browser session, or leaving required data fields vacant when personal queries feel intrusive. In contrast, falsification involves active compliance with the transactional interface using counterfeit data tokens, allowing the individual to extract corporate benefits (such as gated content, special promotional codes, or guest checkout privileges) while denying the enterprise accurate intelligence.
• Protective Technical Masking: This involves technological solutions such as virtual private networks (VPNs), disposable temporary email services, or browser tracking preventers. While technical masking manipulates the communication channel, intentional falsification operates directly at the cognitive and semantic levels of data generation, representing an explicit willingness to furnish deceitful statements.
• Protective Voice (Complaint Behavior): Consumers exhibiting voice strategies confront the organization, submit formal privacy inquiries, or lodge regulatory complaints with supervisory authorities. Falsification is, by definition, covert; its efficacy relies upon the enterprise accepting the fabricated information as genuine.

The construct reflects a consumer’s calculus that the psychological, economic, or social risks of accurate disclosure outweigh the moral or normative friction associated with furnishing deceptive data to an enterprise. Falsification intentions signify an acute breakdown in interpersonal and institutional trust, reflecting psychological reactance against perceived coercion and an instrumental drive to reclaim autonomy over informational self-determination.

6. Theoretical Framework

The Customer Data Falsification Intentions Scale is grounded in an interdisciplinary theoretical framework drawing primarily from Communication Privacy Management Theory, the Privacy Calculus Model, and the Cognitive-Affective System Theory of Coping.

Communication Privacy Management (CPM) Theory

Formulated by Sandra Petronio, Communication Privacy Management Theory posits that individuals believe they own their personal information and establish mental boundaries to regulate disclosure according to rule-based management systems. When an external entity breaches these boundaries or attempts to dictate access rules unilaterally, individuals experience “boundary turbulence.” Within the context of consumer relationships, when a company requests non-essential or sensitive details without transparent justification, consumers view the interaction as boundary disruption. Because traditional boundary coordination mechanisms have broken down, the customer deploys data fabrication as an autonomous tactic to re-establish personal boundary control.

The Privacy Calculus Model

The Privacy Calculus Model posits that individuals perform a subjective cost-benefit analysis before disclosing private assets. Consumers weigh anticipated utility (such as personalization, discounts, or convenience) against perceived privacy risks (such as identity theft, unauthorized surveillance, or unsolicited marketing contacts). In standard applications, a negative calculus yields non-participation. However, when the structural configuration of modern digital commerce requires personal data submission to obtain indispensable utility, consumers subvert the calculus: by submitting falsified data, they secure the transaction’s immediate rewards while systematically eliminating the downstream privacy risks associated with profile building and data breaches.

Cognitive-Affective Processing and Coping Mechanisms

In the framework developed by Martin, Borah, and Palmatier (2017), consumer data practices operate through distinct psychological channels. Data vulnerability—defined as a customer’s susceptibility to harm resulting from the unauthorized, unexpected, or uncompensated commercial exploitation of their personal data—triggers both cognitive trust erosion and emotional violation. Emotional violation embodies feelings of betrayal, anger, and psychological distress. Drawing on Lazarus and Folkman’s Transactional Theory of Stress and Coping, when individuals confront an environment over which they lack direct institutional control, they adopt problem-focused covert behaviors to mitigate perceived threats. Falsification represents a targeted coping mechanism aimed at neutralizing vulnerability while retaliating against the perceived bad-faith practices of the data-gathering firm.

7. Validity

The validity of the Customer Data Falsification Intentions Scale has been empirically substantiated across multiple methodological designs, including experimental laboratory configurations, scenario-based vignettes, and cross-sectional field surveys spanning consumer panels.

Construct and Convergent Validity

In the empirical investigations conducted by Martin et al. (2017), the scale demonstrated exceptional convergent validity. Standardized factor loadings across all three items routinely surpass the conservative threshold of .70, clustering between .86 and .94 across diverse sample groups. The Average Variance Extracted (AVE) substantially exceeds the established benchmark of .50 (typically reaching values between .78 and .85), establishing that the latent construct accounts for the vast majority of variance observed across its operational indicators.

Discriminant Validity

Discriminant validity was verified using both the Fornell-Larcker criterion and the Heterotrait-Monotrait ratio of correlations (HTMT). The square root of the AVE for the CDFI scale is consistently higher than any bivariate correlation between the CDFI and conceptually related constructs, such as:

• General Privacy Concern
• Perceived Data Vulnerability
• Emotional Violation
• Cognitive Trust
• Transaction Abandonment Intentions
• Negative Word-of-Mouth (NWOM)

HTMT ratios evaluated against these constructs remain safely below the strict .85 threshold, demonstrating that data falsification is statistically and conceptually distinct from emotional grievance or direct service termination.

Criterion and Nomological Validity

Nomological validity is affirmed through the instrument’s performance within comprehensive structural equation models. Martin et al. (2017) revealed that when consumer data vulnerability increases—whether driven by corporate data breaches, unauthorized data sharing, or algorithmic profiling—emotional violation significantly increases ($eta pprox .42, p < .001$), which in turn drives a substantial, direct positive escalation in customer data falsification intentions ($eta pprox .35, p < .001$). Furthermore, the scale demonstrates robust predictive validity in laboratory experiments: respondents with elevated scores on the CDFI are significantly more likely to provide fictitious demographic attributes, bogus telephone numbers (e.g., 555-prefix exchanges), and disposable email addresses when prompted by live registration interfaces.

8. Reliability

The internal consistency and measurement precision of the Customer Data Falsification Intentions Scale have been rigorously demonstrated across multiple validation cohorts.

• Cronbach’s Alpha ($lpha$): Across initial and replication samples, the scale exhibits high internal consistency. In Martin et al.’s (2017) baseline structural dataset ($N = 432$), the Cronbach’s alpha was documented at .92. Follow-up experimental cohorts investigating distinct privacy failure scenarios yielded alpha coefficients consistently spanning the range of .91 to .95, significantly surpassing the standard academic research cutoff of .70 or .80.
• Composite Reliability (CR): To account for potential discrepancies in tau-equivalence, composite reliability was computed across structural model estimations. The observed CR values routinely exceed .92, indicating minimal indicator-specific measurement error.
• Item-Total Correlations: Corrected item-to-total correlations for each of the three items consistently surpass .80, indicating that each statement contributes significantly to the primary latent continuum without idiosyncratic noise.
• Test-Retest Stability: In longitudinal test-retest assessments across two-week intervals, stability coefficients demonstrated adequate temporal consistency ($r_{tt} > .78$) under invariant situational trust conditions, confirming that while the measure responds dynamically to experimental manipulations of institutional vulnerability, its baseline trait-level operationalization remains stable.

9. Factor Analysis

Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) have confirmed the strict unidimensionality of the three-item Customer Data Falsification Intentions Scale.

Exploratory Factor Analysis

Principal axis factoring and maximum likelihood extractions conducted during pilot development reveal a single dominant eigenvalue (typically $lambda > 2.50$), accounting for more than 82% of the total variance across items. Scree plot analyses demonstrate a definitive drop-off after the initial factor, with no secondary factor achieving an eigenvalue above 0.35, firmly rejecting multi-factorial interpretations.

Confirmatory Factor Analysis (CFA)

When evaluated within comprehensive measurement models including latent constructs of cognitive trust, emotional violation, perceived breach severity, and brand equity, the three indicators demonstrate outstanding factor loadings and fit parameters. In a representative CFA model conducted on consumer survey data (Martin et al., 2017), standardized item loadings loaded onto the CDFI construct were:

• Item 1 (“fabricate information”): $lambda = .91$
• Item 2 (“alter it”): $lambda = .88$
• Item 3 (“provide incorrect information”): $lambda = .93$

Because a three-item measurement model without external constraints is just-identified ($df = 0$), structural fit is evaluated when the scale is embedded in multi-construct structural models. When tested within the broader nomological network, model fit indices routinely meet or exceed conventional psychometric criteria:

• Comparative Fit Index (CFI) $ge .97$
• Tucker-Lewis Index (TLI) $ge .96$
• Root Mean Square Error of Approximation (RMSEA) $le .05$ (with 90% confidence intervals bounded within .02 and .07)
• Standardized Root Mean Square Residual (SRMR) $le .03$

These empirical indices confirm that the scale is psychometrically parsimonious, free from localized strain, and structurally sound across experimental and field settings.

10. Instrument / Measurement Tool

The operational specifications of the Customer Data Falsification Intentions Scale are summarized below:

• Test Type: Self-report psychometric rating scale / behavioral intentions assessment.
• Format: Written questionnaire format, administered via paper-and-pencil or computerized web interfaces.
• Item Count: 3 items.
• Target Domain: Consumer behavior, e-commerce interactions, digital platform onboarding, customer relationship management (CRM), and data privacy studies.
• Authentic Response Scale: 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree).
• Scoring Rules: Scores across the three individual items are summed and then averaged to generate an unweighted composite score ranging from 1.00 to 7.00. No items are reverse-coded. Higher scores indicate a greater propensity and intention to deliberately furnish false, altered, or fabricated personal data to the target organization.
• Completion Time: Approximately 30 to 60 seconds.

11. Permissions & Fee and Test Year

• Publication Year: 2017 (as adapted and validated by Kelly D. Martin, Abhishek Borah, and Robert W. Palmatier). Foundational antecedents were published in 2007 by Michael O. Lwin, Jochen Wirtz, and Jerome D. Williams.
• Commercial / Licensing Fee: Free for non-commercial academic research and educational applications.
• Permissions Information: The items are published in peer-reviewed academic literature in the Journal of Marketing (American Marketing Association). In accordance with standard fair-use scholarly conventions, researchers may reproduce and administer the scale for non-commercial scientific inquiry provided proper formal citation is given to Martin, Borah, and Palmatier (2017) and Lwin, Wirtz, and Williams (2007). Commercial enterprises seeking proprietary deployment within corporate audit software should consult the publishers regarding copyright guidelines.

12. References

Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104–115. https://doi.org/10.1287/orsc.10.1.104

Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080

Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer Publishing Company.

Lwin, M., Wirtz, J., & Williams, J. D. (2007). Consumer online privacy: An exploration of consumer web privacy concerns and regulation. Journal of Marketing, 71(2), 57–72. https://doi.org/10.1509/jmkg.71.2.057

Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355. https://doi.org/10.1287/isre.1040.0032

Martin, K. D., Borah, A., & Palmatier, R. W. (2017). Data privacy: Effects on customer and firm performance. Journal of Marketing, 81(1), 36–58. https://doi.org/10.1509/jm.15.0497

Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155. https://doi.org/10.1007/s11747-016-0495-4

Petronio, S. (2002). Boundaries of privacy: Dialectics of disclosure. State University of New York Press.

13. Items of the Scale