Governance Role of Digital Technologies and Backshoring
Bibliographic record
Abstract
Over the past decade, international business researchers have increasingly focused on the role of digital technologies and their potential effects on the configuration of global value chains (GVCs). This chapter explores the governance role of digital technologies in firm relocation decisions by conducting a systematic literature review of 37 articles published between 2011 and May 2024. The findings reveal that existing studies tend to examine the relationship between only a few specific digital technologies and reshoring, highlighting a gap in comprehensive research on the subject. While some studies identify correlations between digitalization and reshoring, others attribute reshoring decisions to factors unrelated to digitalization. However, these factors are also influenced by digitalization, which permeates nearly every aspect of business operations, often leading to indirect impacts on reshoring. There is a clear need for further research employing mathematical models that analyze binary relationships by grouping various digital technologies and reshoring outcomes into distinct sets. Such an approach would integrate existing literature with empirical studies to better map these relationships. Meanwhile, managers should recognize digitalization as a critical factor to consider in reshoring decisions, even if its influence may not always be immediately apparent.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".