The Role of Digitalisation Technologies in Enhancing Supply Chain Performance in the Service Industry: Identifying the Current Research Gaps
Bibliographic record
Abstract
Digitalisation technologies are proving to be effective in many different situations across the service industry. Technologies such as AI, blockchain, big data, IoT, IoE, 6G, digital twin, and cobots are showing positive effects when implemented in different types of companies. This research highlights the important role of digitalisation technologies in enhancing supply chain performance within the service industry. Although these technologies are being adopted across sectors, limited research examines their specific impact on service supply chains. A comprehensive search of three academic databases identified 429 peer-reviewed studies published since 2019. PRISMA was implemented, resulting in a final dataset of 66 studies. The findings reveal a positive correlation between digital technologies and supply chain performance. While there has been significant progress in digital technologies, a unified and accessible framework to systematically integrate all eight technologies with service supply chains remains absent. This article highlights the potential of such a framework to serve as a strategic tool for organisations pursuing digital transformation in complex service environments. It also proposes a preliminary model that links the type of firm, high-level processes, metrics, and digitalisation technologies that improve supply chain performance in the service industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".