Procedural Justice, not Absorptive Capacity, Matters in Multinational Enterprise ICT Transfers
Bibliographic record
Abstract
This paper empirically tests the effectiveness of information and communications technology (ICT) knowledge transfer and adoption in the multinational enterprise (MNE). This is an issue of critical importance to contemporary MNE functioning (Yamin and Sincovics, 2007; Yamin, Tsai and Holm, 2011). In contrast to mainstream thinking, our research supports the proposition that perceptions of procedural fairness, rather than absorptive capacity, determine effectiveness, especially in cases of high tacit knowledge transfers. We collected data from senior ICT representatives in 86 Canadian subsidiaries of foreign owned MNEs. Each of these subsidiaries recently experienced a significant ICT transfer imposed by the parent organization. We found support for our main propositions: Procedural justice significantly predicted successful ICT transfer and adoption, while absorptive capacity was not significant. These findings were consistent even when knowledge tacitness was high. The perceived success of the ICT transfer as well as its adoption varied widely across these firms. The potential reasons for this divergence in effectiveness are manifold, but our findings suggest that in situations of substantial knowledge tacitness, a higher level of procedural fairness, rather than more absorptive capacity, is critical to effective transfer and adoption.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".