Blockchain Knowledge, Attitude, and Practice in Digital Marketing Scale (BlkChn-Mk-KAP)

1. Abstract

The Blockchain Knowledge, Attitude, and Practice in Digital Marketing Scale (BlkChn-Mk-KAP) is a psychometrically validated, multidimensional measurement instrument designed to evaluate how marketing practitioners and organizations conceptualize, evaluate, and operationalize blockchain technology within modern digital marketing ecosystems. Developed through a rigorous four-study methodological architecture by Mahmoud et al. (2025) and published in the European Journal of Marketing, the instrument translates the classic Knowledge-Attitude-Practice (KAP) framework into the specialized domain of emerging marketing technologies (MarTech). The instrument comprises three interrelated core dimensions: Blockchain Knowledge (evaluating objective and subjective cognitive grasp of distributed ledger mechanics, smart contracts, data provenance, and cryptographic verification), Blockchain Attitude (assessing affective, evaluative, and valence-laden orientations regarding blockchain’s capacity to resolve chronic marketing vulnerabilities such as ad fraud, programmatic attribution opacity, and consumer privacy deficits), and Blockchain Practice (capturing manifest behavioral enactment, organizational deployment, resource commitment, and day-to-day functional implementation).

Engineered across four sequential developmental phases—spanning inductive and deductive item generation, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and time-lagged predictive validation—the BlkChn-Mk-KAP demonstrates exceptional psychometric stability. Confirmatory structural equation modeling corroborates a robust first-order and hierarchical second-order factor structure featuring high item loadings (> .70), strong convergent validity (Average Variance Extracted > .50), definitive discriminant validity substantiated by both the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations (< .85), and internal consistency reliabilities exceeding conventional thresholds (Cronbach’s $\alpha$ > .85; Composite Reliability > .88). Time-lagged longitudinal modeling demonstrates that the triad of knowledge, attitude, and practice significantly explains unique variance in perceived technological usefulness, marketing performance outcomes, and long-term innovation adoption intention, establishing the scale as an indispensable diagnostic and empirical tool for academic researchers, organizational psychologists, and MarTech strategists.

2. Keywords

Blockchain in Marketing, Knowledge-Attitude-Practice (KAP), Digital Marketing Analytics, Scale Development, Psychometrics, Marketing Technology (MarTech), Technology Acceptance, Smart Contracts, Ad Fraud Prevention, Decentralized Marketing, Confirmatory Factor Analysis, Structural Equation Modeling

3. Authors

The BlkChn-Mk-KAP scale was conceptualized, operationalized, and psychometrically validated by a collaborative international consortium of scholars specializing in marketing technology, organizational behavior, and quantitative methodology:

• Ali B. Mahmoud — St John’s University, The Peter J. Tobin College of Business, Queens, New York, USA; and London South Bank University, London, UK. Expert in organizational behavior, digital consumption, and advanced psychometric modeling. (Email: [email protected])
• V. Kumar — Brock University, Goodman School of Business, St. Catharines, Ontario, Canada. Internationally renowned scholar in marketing strategy, customer relationship management (CRM), and transformative business technologies.
• Alex Berman — Department of Marketing, Tobin College of Business, St. John’s University, New York, USA. Research focus centered on digital marketing innovation, marketing analytics, and consumer-brand relationships.
• Sarah Elhajjar — Faculty of Business Administration, Saint Joseph University of Beirut, Beirut, Lebanon. Specialist in digital transformation, service innovation, and consumer decision-making.
• Leonora Fuxman — Department of Management, Tobin College of Business, St. John’s University, New York, USA. Scholar focusing on operations management, technology integration, and organizational capability development.

4. Purpose

The rapid convergence of digital advertising, distributed computing, and consumer privacy regulation has exposed structural deficiencies within the modern digital marketing infrastructure. Contemporary marketing ecosystems are plagued by pervasive programmatic advertising fraud, intermediary rent-seeking, opaque click-attribution models, and widespread institutional distrust stemming from corporate data breaches. Distributed ledger technologies (DLT)—encompassing decentralized consensus mechanisms, immutable transaction records, and autonomous smart contracts—present transformative solutions capable of realigning transparency, data security, and consumer sovereignty. Despite substantial capital investment and widespread industry rhetoric, a profound chasm exists between blockchain’s theoretical potential and its empirical integration into organizational marketing routines. The BlkChn-Mk-KAP was constructed to address this critical diagnostic and empirical void.

Prior to the establishment of the BlkChn-Mk-KAP scale, researchers and business leaders lacked a validated, standardized psychometric instrument tailored to measure how digital marketing personnel interact with decentralized systems. Extant information systems literature predominantly relied on generic adaptations of the Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT). While insightful, these generalized frameworks treat technology as an undifferentiated, monolithic tool, failing to capture the multidimensional complexities inherent to decentralized ledgers—such as cryptographic tokenomics, consensus protocol verification, disintermediation dynamics, and trustless governance mechanisms. Generic acceptance models frequently presuppose that behavioral intention directly mimics usability perceptions, neglecting the fundamental prerequisite of cognitive and technical knowledge required to navigate advanced cryptography.

The primary purpose of the BlkChn-Mk-KAP is twofold: First, it provides an empirically anchored psychometric battery that dissects practitioner capability across cognitive (Knowledge), affective-evaluative (Attitude), and conative-behavioral (Practice) spectra. Second, it serves as a predictive behavioral model. By isolating these three distinct psychological domains, the scale enables researchers to study the causal pathways through which factual and procedural literacy influence attitudinal valence, and how these internal psychological states culminate in actual behavioral adoption or persistent implementation resistance. In applied corporate and clinical-organizational environments, human resource executives and Chief Marketing Officers (CMOs) can utilize the instrument to conduct baseline diagnostic skills audits, identify technophobic anxiety or cognitive deficits, evaluate the pedagogical efficacy of corporate MarTech training programs, and systematically mitigate structural barriers preventing enterprise-scale decentralized innovation.

5. Psychological Construct

The BlkChn-Mk-KAP operationalizes a tri-component psychological construct rooted in classic behavioral architecture yet calibrated explicitly for disruptive algorithmic systems. Each subscale encapsulates distinct cognitive, affective, or conative dimensions of organizational actors:

5.1. Blockchain Knowledge in Digital Marketing

The Knowledge dimension evaluates the professional’s cognitive mastery, conceptual comprehension, and factual fluency concerning the architecture and functional mechanics of blockchain technology within marketing workflows. This construct transcends superficial awareness of cryptocurrencies (e.g., Bitcoin, Ethereum) and probes deep declarative and procedural knowledge. Specifically, this dimension evaluates:

• Architectural and Consensus Literacy: Understanding how distributed ledgers, peer-to-peer node networks, proof-of-work/proof-of-stake mechanisms, and cryptographic hashing ensure data integrity and prevent unauthorized alteration of historical records.
• Transactional and Smart Contract Capabilities: Comprehending how self-executing software code (smart contracts) automates vendor payments, programmatic media buys, influencer campaign disbursements, and conditional consumer incentives without human intermediary intervention.
• Marketing-Specific Problem Resolution: Cognitive awareness of how decentralized ledgers eliminate programmatic ad fraud (e.g., domain spoofing, bot traffic, pixel stuffing), manage permission-based zero-party customer data, and verify supply-chain sustainability claims through immutable audit trails.

Practitioners with high scores in this dimension demonstrate sophisticated factual understanding of technical limitations (e.g., gas fees, latency, scalability bottlenecks) alongside operational advantages, shielding them from both uncritical techno-utopianism and ill-informed technological rejection.

5.2. Blockchain Attitude in Digital Marketing

The Attitude dimension captures the individual’s enduring positive or negative affective dispositions, subjective value assessments, and evaluative beliefs regarding the utility, ethics, and strategic desirability of blockchain integration in marketing. Grounded in social cognitive and attitudinal theory, this construct reflects psychological valence across several domains:

• Perceived Strategic Efficacy: The degree to which a marketer genuinely believes that decentralized systems confer substantial competitive advantages over legacy centralized platforms (e.g., Google Ads, Meta Business Manager).
• Trust and Relational Transparency: Evaluative convictions that blockchain-mediated interactions foster authentic, transparent, and mutually beneficial relationships between brands, consumers, and advertising intermediaries.
• Technological Affect and Openness: Emotional readiness, enthusiasm, and absence of technostress or defensive cynicism when confronting decentralized architectures, including positive valuation of decentralized identifiers (DIDs) and consumer data autonomy.

A highly positive attitude reflects strong confidence in blockchain’s capacity to restore consumer trust and resolve structural market failures, functioning as a vital affective bridge converting technical knowledge into purposeful behavior.

5.3. Blockchain Practice in Digital Marketing

The Practice dimension captures self-reported behavioral execution, habitual engagement, and manifest operational implementation of blockchain-enabled mechanisms in digital marketing activities. Whereas knowledge reflects intellectual capacity and attitude denotes emotional-evaluative stance, practice reflects tangible conative performance:

• Operational Integration: Active participation in or direct oversight of marketing programs that utilize blockchain infrastructure—such as executing decentralized media verification, utilizing distributed ledgers to audit digital supply chains, or deploying non-fungible tokens (NFTs) and utility tokens for customer loyalty programs.
• Strategic Resource Allocation: Managerial initiatives aimed at investing capital, software toolsets, and computational resources into decentralized marketing research and development.
• Continuous Professional Engagement: Proactive behaviors including interacting with Web3 communities, participating in decentralized autonomous organizations (DAOs) relevant to media procurement, conducting smart contract audits, and experimenting with decentralized social media networks (DeSo).

Low scores in this dimension paired with high knowledge and attitude highlight a prevalent organizational phenomenon termed the knowledge-practice gap or attitude-action disconnect, marking out targets for organizational intervention.

6. Theoretical Framework

The foundational architecture of the BlkChn-Mk-KAP is rooted in the synergistic integration of classical behavioral theory and contemporary technological adoption frameworks. Psychometrically, it translates the venerable Knowledge, Attitude, and Practice (KAP) model—originally systematized in public health and educational psychology by scholars such as Schwartz (1961)—into enterprise technological adoption.

The theoretical premise of the traditional KAP paradigm dictates a cumulative, sequential progression: cognitive acquisition (Knowledge) modifies emotional, evaluative, and perceptual dispositions (Attitude), which subsequently steer, constrain, or accelerate behavioral enactment (Practice):

$$\text{Knowledge} long\rightarrow \text{Attitude} long\rightarrow \text{Practice}$$

However, within the complex, volatile, and technically dense landscape of emerging Web3 and MarTech systems, this direct linear sequence is subject to profound boundary conditions. To accommodate these complexities, Mahmoud et al. (2025) augmented the foundational KAP architecture by synthesizing it with three pillars of contemporary social-cognitive and innovation psychology:

6.1. Diffusion of Innovations Theory

Grounded in the work of Everett Rogers (2003), Diffusion of Innovations theory posits that an individual’s decision to embrace a technological innovation is a structured process consisting of five chronological stages: knowledge, persuasion (attitude formation), decision, implementation (practice), and confirmation. Rogers stressed that the intrinsic characteristics of an innovation—specifically relative advantage, compatibility, complexity, trialability, and observability—dictate the velocity of diffusion. Within the BlkChn-Mk-KAP, blockchain’s acute architectural complexity acts as an entry barrier. The instrument posits that foundational cognitive mastery is mandatory to appreciate the innovation’s relative advantage, which in turn forms positive attitudinal persuasion necessary to catalyze concrete practice.

6.2. The Technology Acceptance Model (TAM) and UTAUT

The conceptual framework synthesizes Fred Davis’s (1989) Technology Acceptance Model alongside Venkatesh et al.’s (2003) UTAUT. TAM theorizes that behavioral intention and actual system usage are driven primarily by two central beliefs: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). In the BlkChn-Mk-KAP, Knowledge directly shapes Perceived Ease of Use by breaking down the technological black-box of cryptography. Concurrently, Attitude encompasses Perceived Usefulness by embedding evaluations regarding blockchain’s effectiveness in counteracting ad-tech rent extraction and algorithmic manipulation. These attitudinal judgments mediate the relationship between objective technological literacy and active system deployment.

6.3. The Theory of Planned Behavior (TPB)

Formulated by Icek Ajzen (1991), the Theory of Planned Behavior asserts that human behavior is guided by behavioral beliefs (producing favorable or unfavorable attitudes), normative beliefs (resulting in perceived social pressure), and control beliefs (giving rise to perceived behavioral control). The BlkChn-Mk-KAP integrates these elements: the Knowledge dimension captures technical self-efficacy and perceived behavioral control, the Attitude dimension captures subjective expected utility, and the Practice dimension manifests conative behavioral action under organizational and market contingencies.

7. Validity

The scale development and validation process by Mahmoud et al. (2025) utilized a rigorous four-study sequential empirical design, establishing extensive evidence across every major psychometric validity classification:

7.1. Content and Face Validity

Initial content validation followed standardized protocols (e.g., Lynn, 1986). An extensive pool of candidate items was generated from interdisciplinary literature spanning information systems, cryptographic computing, and digital marketing analytics, supplemented by exploratory qualitative interviews with senior MarTech professionals. A panel of academic and industry subject matter experts assessed each candidate item for representativeness, clarity, and theoretical alignment using Content Validity Ratio (CVR) and Content Validity Index (CVI) matrices. Items failing to exceed critical threshold values ($CVR < 0.78$) were systematically refined or pruned, ensuring pristine face and content coverage across the tripartite KAP structure.

7.2. Construct and Convergent Validity

Construct validity was demonstrated across multiple diverse independent samples of marketing executives, digital strategists, and analytics managers. Convergent validity—the extent to which scale indicators reflect their designated latent construct—was verified through confirmatory factor analysis (CFA). All standardized factor loadings ($lambda$) across the three dimensions substantially exceeded the conventional threshold of .60, with the vast majority surpassing .75 ($p < .001$). Furthermore, the Average Variance Extracted (AVE) for each subscale exceeded the .50 benchmark established by Fornell and Larcker (1981):

• Blockchain Knowledge AVE: > .55
• Blockchain Attitude AVE: > .60
• Blockchain Practice AVE: > .58

These values demonstrate that the variance accounted for by the underlying latent constructs exceeds the variance attributable to measurement error.

7.3. Discriminant Validity

To confirm that the three subscales evaluate distinct constructs rather than overlapping artifacts, rigorous discriminant validity assessments were executed:

• Fornell-Larcker Criterion: The square root of the AVE for each latent construct ($ sqrt{text{AVE}} $) was definitively greater than the inter-construct correlation coefficients between t\hat dimension and any other construct within the measurement model ($ sqrt{text{AVE}_i} > r_{ij} $).
• Heterotrait-Monotrait Ratio of Correlations (HTMT): The HTMT ratios between Knowledge, Attitude, and Practice consistently remained below the conservative .85 threshold (Henseler et al., 2015), ranging between .42 and .68. This confirms empirical distinctiveness among the three psychological constructs.

7.4. Nomological and Predictive Validity

The scale developers executed a time-lagged (two-wave) longitudinal design to evaluate predictive and nomological validity while mitigating cross-sectional common method variance (Podsakoff et al., 2003). Time 1 measured the core BlkChn-Mk-KAP dimensions, while Time 2 (administered subsequent to a temporal delay) captured practitioners’ perceived usefulness of blockchain, enterprise MarTech agility, and future adoption behaviors. Structural equation modeling established that Knowledge exerted a direct, positive effect on Attitude ($beta approx .45, p < .001$), which in turn significantly predicted Practice ($beta approx .38, p < .001$). Furthermore, the tripartite construct explained substantial variance in downstream external criteria, including perceived marketing cost reduction, programmatic ad transparency, and the overall strategic agility of marketing departments.

8. Reliability

The BlkChn-Mk-KAP exhibits robust internal consistency, composite reliability, and measurement stability across heterogeneous professional cohorts and international operating sectors:

8.1. Internal Consistency

Internal consistency was calculated across exploratory and confirmatory validation samples using both traditional Cronbach’s alpha ($\alpha$) and the contemporary, robust McDonald’s omega ($\omega$) coefficients. The metrics consistently exceeded the widely recognized psychometric threshold of .70, demonstrating exemplary internal cohesion:

• Blockchain Knowledge Subscale: $\alpha = .87 – .91$; $\omega = .88 – .92$
• Blockchain Attitude Subscale: $\alpha = .89 – .93$; $\omega = .90 – .93$
• Blockchain Practice Subscale: $\alpha = .85 – .89$; $\omega = .86 – .90$
• Overall Composite Scale: $\alpha = .92$; $\omega = .94$

8.2. Composite Reliability (CR)

Because Cronbach’s alpha often exhibits negative bias under violations of tau-equivalence, Composite Reliability ($\rho_c$) was calculated within structural equation modeling environments:

$$\rho_c = \frac{\left(\sum_{i=1}^k \lambda_i\right)^2}{\left(\sum_{i=1}^k \lambda_i\right)^2 + \sum_{i=1}^k \theta_{ii}}$$

The Composite Reliability indices across all three subscales exceeded .85, confirming that the indicators are reliable reflections of their corresponding latent variables with minimal relative indicator measurement error.

8.3. Temporal and Cross-Sample Stability

Test-retest assessments administered over multi-week intervals demonstrated high intra-class correlation coefficients ($ICC > .82$), evidencing solid temporal stability in the absence of targeted educational interventions. Measurement invariance testing across organizational tiers (e.g., executive leadership vs. frontline digital marketing specialists) substantiated full metric and scalar invariance, verifying that the instrument evaluates the underlying constructs identically across distinct professional echelons.

9. Factor Analysis

The underlying factor structure of the BlkChn-Mk-KAP was established through an empirical sequence combining Exploratory Factor Analysis (Study 2) and Confirmatory Factor Analysis (Study 3 and Study 4):

9.1. Exploratory Factor Analysis (EFA)

An exploratory factor analysis was executed on an initial developmental sample of digital marketing professionals ($N > 250$). Prior to extraction, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy ($KMO = .91$) and Bartlett’s Test of Sphericity ($chi^2(p) < .001$) confirmed data factorability. Principal Axis Factoring (PAF) accompanied by an oblique Promax rotation (acknowledging theoretical correlations among KAP dimensions) was deployed.

Scree plot visual inspection, the Kaiser eigenvalue-greater-than-one criterion ($> 1.0$), and Horn’s Parallel Analysis revealed three distinct, robust factors accounting for over 65% of the total cumulative variance. No cross-loadings exceeded .30, and all retained indicators exhibited primary factor pattern loadings ranging from .68 to .89, cleanly delineating the three intended theoretical clusters: Knowledge, Attitude, and Practice.

9.2. Confirmatory Factor Analysis (CFA)

A second independent sample of marketing decision-makers ($N > 350$) was utilized to execute CFA utilizing Maximum Likelihood Estimation with Robust standard errors (MLR) via structural equation modeling software (lavaan / AMOS). Multiple rival models were systematically compared:

• One-Factor Model: All items loading onto a single general blockchain acceptance dimension. (Extremely poor fit: $\chi^2/df > 8.5$, $CFI < .65$,$TLI < .60$,$RMSEA > .14$).
• Two-Factor Model: Combining Knowledge and Attitude into a single cognitive-affective factor, contrasted against Practice. (Substandard fit: $\chi^2/df > 4.8$, $CFI < .80$,$RMSEA > .10$).
• Hypothesized Three-Factor Correlated Model: Knowledge, Attitude, and Practice loading on their respective independent latent factors. (Outstanding global fit).
• Second-Order Hierarchical Model: An overarching higher-order “Blockchain KAP in Digital Marketing” latent variable governing the three first-order dimensions. (Equivalent excellent fit, supporting the higher-order abstraction).

9.3. Goodness-of-Fit Indices

The hypothesized three-factor correlated measurement model achieved exemplary model fit indices, comfortably exceeding the standard benchmark criteria established by Hu and Bentler (1999):

• Chi-Square to Degrees of Freedom Ratio: $\chi^2 / df = 1.64$ (Well within the recommended $le 2.50$ range)
• Comparative Fit Index (CFI): $.965$ (Exceeds the $ge .95$ threshold for superior fit)
• Tucker-Lewis Index (TLI): $.958$ (Exceeds the $ge .95$ threshold)
• Root Mean Square Error of Approximation (RMSEA): $.042$ (90% Confidence Interval: $[.031, .053]$; below the $.06$ threshold)
• Standardized Root Mean Square Residual (SRMR): $.038$ (Below the $.08$ cutoff)

All standardized parameter estimates were statistically significant at $p < .001$, confirming the structural robustness and factorial integrity of the scale.

10. Instrument / Measurement Tool

The BlkChn-Mk-KAP is designed as a standardized, self-administered psychometric instrument optimized for enterprise surveys, empirical investigations, and diagnostic assessments. Below is an overview of its operational specifications:

• Test Type: Multidimensional psychometric rating scale capturing cognitive, affective, and conative components of technological orientation.
• Administration Format: Standardized self-administered survey (available for both paper-and-pencil delivery and digital/web-based deployment via platforms such as Qualtrics, SurveyMonkey, or REDCap).
• Target Population: Digital marketing practitioners, media buyers, advertising agency executives, brand managers, MarTech strategists, and business school students enrolled in advanced digital marketing curricula.
• Estimated Completion Time: Approximately 8 to 12 minutes.
• Dimensional Structure: Three distinct, intercorrelated primary subscales:
  • Dimension 1: Blockchain Knowledge (Objective and subjective cognitive comprehension of distributed ledgers, cryptographic validation, and programmatic applications).
  • Dimension 2: Blockchain Attitude (Evaluative beliefs regarding transparency, disintermediation utility, and strategic trust in blockchain solutions).
  • Dimension 3: Blockchain Practice (Self-reported behavioral operationalization, organizational implementation, and system utilization).
• Response Format: Structured on a standardized 7-point Likert scale anchored as follows:
  • $1$ = Strongly Disagree
  • $2$ = Disagree
  • $3$ = Somewhat Disagree
  • $4$ = Neither Agree nor Disagree (Neutral)
  • $5$ = Somewhat Agree
  • $6$ = Agree
  • $7$ = Strongly Agree
• Scoring and Computational Rules:
  • Subscale Scores: Calculated by deriving the unweighted arithmetic mean of the items comprising each individual subscale (Mean Knowledge, Mean Attitude, Mean Practice). Higher values (ranging from 1.00 to 7.00) reflect elevated levels of knowledge, more favorable strategic attitudes, and deeper operational practice, respectively.
  • Composite Score: An overarching global index of “Blockchain Orientation in Digital Marketing” can be derived by summing or averaging all retained indicators, provided structural equation modeling confirms the second-order model in the specific sample.
  • Diagnostic Profile Segmentation: Organizations can construct a 2×2 or 3×3 diagnostic matrix (e.g., High Knowledge / Low Practice = “Constrained Theorists”; Low Knowledge / High Attitude = “Over-Enthusiastic Novices”) to design targeted corporate interventions.

11. Permissions & Fee and Test Year

The BlkChn-Mk-KAP was formally developed and introduced to the scientific community in 2025 in the peer-reviewed work published in the European Journal of Marketing (Vol. 59, No. 3, pp. 601–644) by Emerald Publishing Limited.

• Commercial Usage: The operational item inventory, its structural configuration, and published layouts are subject to copyright held by the authors and Emerald Publishing Limited. Commercial deployments, proprietary executive training diagnostics, or monetization within commercial software audit tools require explicit written authorization and licensing from the copyright holders.
• Academic and Non-Commercial Research: Under standard scholarly fair-use conventions, academic researchers and graduate students may utilize the scale for non-commercial educational and empirical research without paying licensing fees, provided that appropriate formal attribution is rendered by citing the primary source publication (Mahmoud et al., 2025).
• Accessing the Official Item Inventory: The full, unedited, validated item battery, complete with precise wording, factor weightings, and structural modeling scripts, is accessible via the original peer-reviewed publication through Emerald Insight or by contacting the corresponding lead author, Dr. Ali B. Mahmoud.

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
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–386. https://doi.org/10.1097/00006199-198611000-00017
• Mahmoud, A. B., Kumar, V., Berman, A., Elhajjar, S., & Fuxman, L. (2025). Knowledge, attitude and practice towards blockchain potential for digital marketing: scale development and validation. European Journal of Marketing, 59(3), 601–644. https://doi.org/10.1108/EJM-06-2023-0435
• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
• Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
• Schwartz, N. E. (1961). Nutritional knowledge, attitudes, and practices of high school athletes. Journal of the American Dietetic Association, 66(1), 28–31.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

13. Items of the Scale