Acceptance Framework for Collaborative Robots in Traditional Crafts and Handmade in Small Businesses: An Integrated Model
Bibliographic record
Abstract
Although, the deployment of collaborative robots (CoBot) in support of traditional manual roles can provide significant opportunities for increased efficiency in general, however, this introduction also represents massive changes in the design and the management of the work.This paper focuses on the acceptance-related factors that affect the introduction of a movement-enabled robot in traditional crafts and handmade products in small businesses.It's crucial to conduct thorough evaluations to ensure that systems adequately fulfil these special users' requirements and information processing needs within the defined scope to determine how to improve its acceptance.The area of end users for acceptance evaluation of CoBots technology has a lack of knowledge to examine some features of the robotic technology/system in the content of traditional crafts and handmade products in small businesses.This paper proposes a novel evaluation model to evaluate user acceptance of a movement-enabled robot system.It outlines the theoretical foundation driving the construction of the factors model.Technology-to-Performance Chain (TPC) model and The Unified Theory of Acceptance and Use of Technology (UTAUT) model were used for the framework for the classification of properties of adaptations and human-centred requirements that are necessary for introduction.We then apply the framework to traditional crafts and handmade products in small businesses that can inform the acceptance of these new robots to improve and ultimately a smooth adaptation of such a system.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".