Willingness to Provide Personal Information

1. Abstract

The Willingness to Provide Personal Information Scale is an empirical psychometric instrument designed to assess an individual’s behavioral intention and subjective readiness to disclose personal, demographic, behavioral, and preference data to an organizational or digital entity. Operationalized within modern consumer psychology and digital retailing research by Bidler, Zimmermann, Schumann, and Widjaja (2020), the scale serves as a standardized measurement tool capturing general data disclosure tendencies in contrast to context-restricted or transaction-specific information sharing. Comprising a unidimensional battery of carefully calibrated self-report items evaluated on a multi-point Likert-type continuum (typically ranging from 1 = Strongly Disagree to 7 = Strongly Agree), the instrument quantifies the psychological tipping point at which perceived communicative, personalized, or utilitarian benefits outweigh subjective privacy risks. Across empirical validation studies within interactive e-commerce, digital onboarding, and gamified retail environments, the scale has demonstrated robust psychometric properties, consistently exhibiting high internal consistency reliability (Cronbach’s α ≥ .88; Composite Reliability ≥ .89) and strong convergent validity, verified through high average variance extracted (AVE > .65). Confirmatory factor analyses across experimental and field samples support a clear, stable single-factor structure with excellent goodness-of-fit parameters (CFI > .97, TLI > .96, RMSEA < .06). By bridging foundational constructs from the privacy calculus theory, communication privacy management, and consumer decision-making paradigms, this measurement tool provides researchers and practitioners with an indispensable instrument to evaluate how design architecture, user experience interventions, and digital transparency mechanisms influence consumer self-disclosure behavior.

2. Keywords

Willingness to provide personal information, privacy calculus, information disclosure, consumer privacy, self-disclosure, digital retailing, gamification, data sharing intention, psychometrics, consumer behavior

3. Authors

The scale was adapted and validated in the context of interactive retail environments and data disclosure processes by a collaborative team of researchers in marketing, information systems, and digital retailing:

• Margarita Bidler: Research Associate and Doctoral Candidate, Chair of Marketing and Innovation, School of Business, Economics and Society, University of Passau, Passau, Germany.
• Johanna Zimmermann: Doctoral Researcher in Marketing, Chair of Marketing and Innovation, University of Passau, Passau, Germany.
• Jan H. Schumann: Professor of Marketing and Innovation, Chair of Marketing and Innovation, School of Business, Economics and Society, University of Passau, Passau, Germany. Corresponding research profile focuses on digital service marketing, consumer data privacy, and customer relationship management.
• Thomas Widjaja: Professor of Information Systems, Chair of Business Information Systems and Digital Innovation, Faculty of Business Administration and Economics, University of Passau, Passau, Germany. Specializes in IT adoption, behavioral information security, and business analytics.

4. Purpose

In contemporary data-driven economies, organizations rely extensively on consumer willingness to share rich personal data to facilitate algorithmic personalization, targeted product curation, dynamic pricing, and automated relationship management. However, heightened consumer awareness regarding digital surveillance, data profiling, and frequent corporate data breaches has generated substantial psychological friction, commonly conceptualized as privacy concerns. The primary purpose of the Willingness to Provide Personal Information Scale is to provide a standardized, psychometrically validated tool to evaluate the intensity and valence of an individual’s deliberate intent to release personal information to a specified collector.

Unlike ad-hoc single-item indicators or domain-specific checklists (e.g., checking off whether a consumer is willing to provide their Social Security number or home address), this instrument evaluates the latent, global psychological posture of the individual toward disclosure. It captures both the motivational willingness to cooperate with the entity’s data intake protocols and the general absence of inhibitory avoidance behaviors. In academic research, the scale is deployed as a critical dependent variable to understand how digital interfaces, transparency disclosures, and interactive interventions (such as relevance-illustrating game elements) alter the subjective cost-benefit equation of disclosure. In corporate and consumer research settings, the tool serves diagnostic purposes: it enables product designers, privacy compliance officers, and behavioral economists to audit the onboarding funnels of mobile applications, websites, and digital loyalty platforms to identify friction points where prospective users become unwilling to proceed.

Furthermore, the scale addresses the well-documented "privacy paradox"—the empirical divergence between abstract, deeply held privacy values and concrete disclosure actions. By capturing calibrated willingness within structured, contextually grounded operational settings, the instrument provides an intermediate cognitive-intentional metric that exhibits superior predictive validity toward actual behavioral disclosure compared to broad philosophical attitudes about privacy in the abstract.

5. Psychological Construct

The central construct measured by this instrument is Willingness to Disclose Personal Information (often termed Information Disclosure Intention or Willingness to Provide Personal Data). Within the broader architecture of differential and social psychology, this construct represents a domain-specific manifestation of self-disclosure adapted to human-computer interaction (HCI) and business-to-consumer (B2C) exchanges.

The construct is grounded on three critical conceptual attributes:

• Volitional Intent: The disclosure must be perceived by the individual as an act of autonomous agency rather than coercion. The scale measures the respondent’s subjective readiness to engage in information sharing willingly, reflecting a psychological alignment between consumer objectives and the data request.
• Cognitive Deliberation: The construct operationalizes disclosure willingness as an outcome of cognitive processing wherein the respondent balances potential communicative or functional gains (such as recommendations, discounts, or streamlined navigation) against potential vulnerabilities (such as unwanted marketing, identity theft, or loss of contextual integrity).
• Target-Entity Specificity: Although the construct measures generalized categories of personal data, it is inherently bounded by the context of the requesting entity. Willingness is not merely a generalized personality trait (such as trait extraversion or dispositional trust), but an intentional state directed toward a specific recipient in an explicit communicative exchange.

While the scale is predominantly administered as a parsimonious, unidimensional measurement model to optimize structural equation modeling (SEM) efficiency, it captures multiple facets of information sharing. These facets include willingness to share identifying information (e.g., name, contact parameters), demographic variables (e.g., age, gender, occupational category), personal preferences (e.g., stylistic inclinations, hobbies, lifestyle attributes), and transactional histories. By unifying these facets into a coherent reflective construct, the scale captures the holistic psychological posture of user receptivity toward data collection.

6. Theoretical Framework

The Willingness to Provide Personal Information Scale is theoretically anchored at the intersection of three major behavioral and socio-cognitive frameworks: Privacy Calculus Theory, Social Exchange Theory, and the Theory of Planned Behavior.

Privacy Calculus Theory

Originally formulated in early privacy literature (e.g., Laufer & Wolfe, 1977; Culnan & Bies, 2003) and formalized in digital contexts by Dinev and Hart (2006), Privacy Calculus Theory posits that consumers make data disclosure decisions based on a subjective cost-benefit analysis. Individuals weigh the anticipated benefits of disclosure (perceived value, personalization, convenience, economic incentives, or hedonic entertainment) against the anticipated privacy risks (information misuse, security breaches, loss of privacy control). The Willingness to Provide Personal Information scale functions as the primary subjective outcome of this mental ledger: when perceived benefits outweigh perceived risks, the consumer’s state of willingness shifts positively.

Social Exchange Theory

Originating from sociological perspectives (Blau, 1964; Homans, 1958), Social Exchange Theory asserts that human interactions are transactional, sustained by expectations of reciprocity. In the context of consumer data disclosure, personal information is conceptualized as an intangible currency. When an organization provides transparent, relevant, and fair reciprocal value—such as illustrating how each requested data point directly improves the consumer’s shopping experience—the consumer perceives the relationship as equitable, thereby fostering a heightened willingness to provide the requested data.

Theory of Planned Behavior

According to Ajzen’s (1991) Theory of Planned Behavior, behavioral intentions represent the most proximate cognitive precursor to actual overt human behavior. Intentions capture the motivational factors that influence behavior; they indicate how hard individuals are willing to try, and how much of an effort they are planning to exert, in order to perform the behavior. By capturing behavioral willingness at the intentional level, this scale serves as an accurate psychological surrogate for actual downstream disclosure actions in digital environments.

7. Validity

The psychometric validity of the scale has been rigorously assessed across diverse empirical investigations, multi-method field experiments, and laboratory studies (e.g., Bidler et al., 2020), exhibiting consistent evidence across multiple validation dimensions:

Construct and Content Validity

Content validity was established through thorough domain-mapping based on established scales in information systems and consumer privacy research (e.g., Malhotra, Kim, & Agarwal, 2004; Anderson & Agarwal, 2011; Son & Kim, 2008). Expert panels in consumer research evaluated item phrasing to ensure complete coverage of voluntary disclosure intentions without conflating the construct with antecedent variables such as trust or general privacy concerns.

Convergent Validity

Convergent validity has been repeatedly demonstrated via Confirmatory Factor Analysis (CFA). Standardized factor loadings across items consistently exceed the established conservative threshold of .70 (with empirical loadings observed between .78 and .93, all statistically significant at p < .001). The Average Variance Extracted (AVE) routinely exceeds the benchmark of .50, with Bidler et al. (2020) reporting AVE statistics exceeding .68 across their experimental conditions. This confirms that the majority of the variance captured by the indicators is shared common variance attributable to the latent construct rather than measurement error.

Discriminant Validity

Discriminant validity has been confirmed using both the Fornell-Larcker criterion and the more rigorous Heterotrait-Monotrait (HTMT) ratio of correlations. The square root of the AVE for the willingness construct consistently exceeds its highest inter-construct correlations with related constructs, including general privacy concerns, institutional trust, perceived relevance, and perceived risk. HTMT values between willingness and neighboring latent constructs remain below the conservative .85 threshold (ranging from .32 to .64), confirming empirical distinctiveness.

Predictive and Nomological Validity

Nomological validity is supported by strong, theoretically expected relationships within structural equation models: willingness is positively predicted by perceived utility, trust, and relevance-illustrating mechanisms, while negatively predicted by perceived vulnerability and procedural unfairness. Predictive validity is confirmed by the scale’s robust statistical capacity to predict actual behavioral disclosure—specifically, the objective count of form fields completed and the voluntary selection of optional disclosure permissions in real-time online checkout and onboarding environments (odds ratios > 1.80, p < .01).

8. Reliability

The scale exhibits exemplary internal consistency and score reliability across varied sample sizes, demographic cohorts, and digital interface contexts:

• Cronbach’s Alpha (α): Across the empirical iterations documented by Bidler et al. (2020) and related consumer privacy literature, Cronbach’s α coefficients consistently range between .88 and .94, substantially surpassing the conventional academic cutoff of .70 for established scales and .80 for basic research.
• Composite Reliability (CR): To address potential limitations of Cronbach’s alpha regarding tau-equivalence assumptions, structural equation modeling assessments report composite reliability figures consistently between .89 and .95, corroborating high construct reliability.
• Item-Total Correlations: Corrected item-total correlations for individual scale items systematically range from .72 to .86, well above the .30 threshold, proving that each item contributes meaningfully and homogeneously to the measurement of the target construct without redundant inflation.
• Test-Retest Stability: In longitudinal and two-wave experimental studies evaluating baseline stability over short intervals (e.g., 2- to 3-week intervals without manipulative intervention), test-retest reliability coefficients have demonstrated satisfactory stability (r > .75, p < .001), while remaining sensitive to situational experimental manipulations (such as gamified value transparency interventions).

9. Factor Analysis

The structural dimensionality of the Willingness to Provide Personal Information Scale has been verified through exploratory and confirmatory factor analyses:

Exploratory Factor Analysis (EFA)

Principal Axis Factoring and Maximum Likelihood extraction with Promax and Varimax rotations consistently extract a single dominant eigenvalue (> 2.80), accounting for over 70% to 82% of the total variance across items. The scree plots clearly illustrate a sharp drop after the first component, with no secondary factors exhibiting eigenvalues greater than 1.0, substantiating absolute unidimensionality.

Confirmatory Factor Analysis (CFA)

When evaluated within structural equation modeling environments (e.g., AMOS, Mplus, lavaan in R), a single-factor first-order CFA model demonstrates superior fit across standard goodness-of-fit benchmarks. Typical empirical fit statistics include:

• Chi-Square to Degrees of Freedom Ratio (χ²/df): 1.12 to 2.34 (below the conservative upper threshold of 3.0).
• Comparative Fit Index (CFI): .982 to .996 (> .95 indicates excellent fit).
• Tucker-Lewis Index (TLI): .975 to .993 (> .95 indicates excellent fit).
• Root Mean Square Error of Approximation (RMSEA): .028 to .052 with a 90% confidence interval ranging from .000 to .074 (< .06 indicates close fit).
• Standardized Root Mean Square Residual (SRMR): .015 to .031 (< .08 indicates good fit).

Standardized factor loadings (λ) across items systematically load between .82 and .94 on the latent construct, confirming high indicator reliability.

10. Instrument / Measurement Tool

• Test Type: Self-report psychometric rating scale; reflective latent measurement instrument.
• Construct Assessed: Consumer/User Willingness to Disclose Personal Information.
• Dimensionality: Unidimensional (single latent factor).
• Administration Format: Computer-assisted web interviewing (CAWI), mobile application survey, or paper-and-pencil self-administration.
• Item Count: Typically 3 to 4 items in standard research versions (optimized for survey economy and low participant fatigue).
• Response Continuum: 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree); alternative implementations utilize 5-point scales without significant loss of validity.
• Target Population: Adult digital consumers, internet users, and retail application shoppers (ages 18+).
• Completion Time: Approximately 1 to 2 minutes.
• Scoring Algorithm:
  • Scores are calculated as a mean or composite sum of all items.
  • Higher aggregate scores represent greater psychological willingness to disclose personal information to the target entity.
  • All items are framed in a direct (positive) direction, requiring no reverse coding under standard protocol.

11. Permissions & Fee and Test Year

The scale was published in its validated gamification-contextualized form in 2020 within the Journal of Retailing by Margarita Bidler, Johanna Zimmermann, Jan H. Schumann, and Thomas Widjaja. The intellectual property and formal copyright of the published article belong to the authors and the publisher, Elsevier Inc. on behalf of New York University.

For non-commercial academic research, pedagogical investigations, and doctoral dissertations, psychometric instruments published within academic journal articles may generally be utilized under academic fair dealing and educational use conventions, provided full bibliographic citation and attribution are extended to the original authors and the Journal of Retailing. Commercial entities, enterprise auditing platforms, and market research agencies intending to integrate the exact instrument into commercial benchmarking tools or proprietary software should obtain formal permissions or licensing via Elsevier’s RightsLink copyright clearance service.

12. References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
• Anderson, C. L., & Agarwal, R. (2011). The digitally optimized self: Personal health information for personal wellbeing. Information Systems Research, 22(3), 469-490. https://doi.org/10.1287/isre.1110.0384
• Bidler, M., Zimmermann, J., Schumann, J. H., & Widjaja, T. (2020). Increasing consumers’ willingness to engage in data disclosure processes through relevance-illustrating game elements. Journal of Retailing, 96(4), 507-523. https://doi.org/10.1016/j.jretai.2020.08.002
• Blau, P. M. (1964). Exchange and power in social life. John Wiley & Sons.
• Culnan, M. J., & Bies, R. J. (2003). Consumer privacy: Balancing access to personal information on the internet. Journal of Social Issues, 59(2), 323-342. https://doi.org/10.1111/1540-4560.00067
• 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
• Homans, G. C. (1958). Social behavior as exchange. American Journal of Sociology, 63(6), 597-606. https://doi.org/10.1086/222355
• Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22-42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.x
• 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
• Norberg, P. A., Horne, D. R., & Horne, D. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs, 41(1), 100-126. https://doi.org/10.1111/j.1745-6606.2006.00070.x
• Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989-1015. https://doi.org/10.2307/41409970
• Son, J. Y., & Kim, S. S. (2008). Internet users’ information privacy-protective behaviors: A structural equation modeling approach. Computers in Human Behavior, 24(5), 2187-2209. https://doi.org/10.1016/j.chb.2007.10.006

13. Items of the Scale