A conceptual model for the adoption of autonomous robots in supply chain and logistics industry
Bibliographic record
Abstract
The arrival of the era of robots and autonomous machines is undisputable. It is anticipated that the future business environment will be characterized by a variety of intelligent systems and autonomous robots. In 2017, the International Federation of Robotics reported that momentum gained by robotic technologies is strong and that the sales volumes of both service and industrial robots is expected to grow. Building on this projection, the present study proposes a set of prerequisites or key determinants for the adoption of autonomous robots in the supply chain and logistics industry: technological context (i.e., relative advantage, complexity, and cost), organizational context (i.e., management support, financial support and employee competence) and environmental context (i.e., competitive pressure, customer pressure and vendor support). The study adapts a quantitative research design and uses an online survey to collect the needed data to test the conceptual framework and hypotheses proposed. Part of the study results confirms the association between the cost of digital technologies and the adoption of autonomous robots. However, the study found no evidence that the perceived relative advantage positively impacts supply chain and logistics firms’ adoption of autonomous robots. The study offers some managerial advices to supply chain mangers and marketers of the digital technologies and tools that can be applied in the supply chain setting.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".