A diffusion of innovation (DOI) analysis of 3D food printing adoption among food sector early adopters
Bibliographic record
Abstract
Purpose Research on 3DFP has focused on technical developments and consumer acceptance, but the practical implications of adopting this technology from industry perspectives across food sectors are underexplored. This study aimed to uncover factors influencing 3DFP adoption and the prospects of this technology by interviewing food businesses using 3DFP in different food sectors around the globe. Design/methodology/approach The Diffusion of Innovation (DOI) model, a process-oriented adoption approach, was utilized to understand the technological, adopter and social factors influencing 3DFP adoption in food businesses. Findings Decisions to adopt 3DFP hinged on technology compatibility with business needs, adopters’ innovation and technology interests and perceived public interest. Early adoption cases revealed 3DFP benefits over conventional technologies in improved product design, customization, food versatility, convenience and sustainability. Interconnected barriers to adoption included high investment costs, limited technology capacity, negative consumer perception and a low adoption rate by large companies. Proposed barrier solutions by businesses encompassed improving technology scalability and leveraging food and technology familiarity alongside alternative technology labelling to increase visibility and interest by large companies and consumers. Originality/value To the best of the authors’ knowledge, this is the first study assessing the determinants of early adopting 3DFP, covering perspectives from different food sectors and using the DOI model. The study’s insights are valuable for food industry stakeholders: policymakers, industry leaders, food businesses and researchers. It can guide subsequent adopters’ decision-making and inform future research on technical, social and business aspects to enhance adoption in the promissory food sectors for 3DFP such as protein alternatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".