Knowledge, attitude and practice towards blockchain potential for digital marketing: scale development and validation
Bibliographic record
Abstract
Purpose This study aims to explore blockchain potential for digital marketing (BlkChn-Mk-KAP) by developing and validating a measurement model for assessing the constructs of knowledge, attitude and practice (KAP) related to blockchain technology in digital marketing. Design/methodology/approach A four-study process was used. The first study reviewed the literature to develop a pool of possible measurement items. Using exploratory factor analysis and reliability assessments, Study 2 (n = 162) investigated the dimensionality of the items developed in Study 1. The factorial structure from Study 2 was validated in Study 3 (n = 204), and the measurement model invariance was assessed using covariance-based structural equation modelling (CB-SEM). Finally, in Study 4 (n = 203), the predictive validity of the BlkChn-Mk-KAP was tested using a CB-SEM approach, testing its constructs correlations with the perceived usefulness of blockchain for digital marketing. Findings The findings indicate that the BlkChn-Mk-KAP measurement model comprises three-dimensional multi-item scales: knowledge, attitude and practice. Research limitations/implications This study introduces a promising BlkChn-Mk-KAP model to examine blockchain’s role in digital marketing. The authors acknowledge the sampling limitation in this research. To enhance the generalisability of the findings, future research should expand to different groups, including generation, gender and age. In addition, further exploration of the explicit links between blockchain knowledge, attitudes and subsequent digital marketing performance is warranted. Practical implications Educating employees about blockchain technology’s unique features can shape favourable attitudes and stimulate the utilisation of blockchain-enabled technologies in digital marketing practice. BlkChn-Mk-KAP can offer a reliable and valid instrument to benchmark marketers’ KAP of blockchain-powered digital marketing as they implement blockchain technology to gain a competitive advantage. Social implications This study helps to adopt sustainable practices ensuring the wellbeing of the key stakeholders. Originality/value This research introduces the first validated conceptualisation and measurement model, BlkChn-Mk-KAP, to evaluate blockchain KAPs among digital marketing professionals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".