Navigating across the uncertainty: investigating the impact of buyer firms' digital transformation on operational efficiency
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Extensive literature and business consultants assert that digital transformation (DT) substantially enhances firm business operations, while there are significant counterarguments suggesting that DT may squander resources and fall short of delivering the anticipated benefits. Additionally, the impact of uncertainties arising from the buyer–supplier relationship has been largely overlooked. Drawing upon information processing theory (IPT), we propose to decipher the relationship between DT and operational efficiency through the buyer–supplier perspective, and further examine how uncertainties at the task, source and supply network levels moderate this relationship by influencing information processing capabilities. Design/methodology/approach Using secondary data derived from Chinese A-share listed firms, our study evaluated a total of 257 listed buyer firms with 892 firm-year observations. Findings The findings reveal that DT positively influences operational efficiency, with this effect being moderated by buyers’ technological resources and supplier dependency (SD). Interestingly, the supplier digitalisation level and buyer–supplier distance (BSD) do not significantly moderate this relationship. Originality/value This study contributes to technology literature by empirically investigating the actual impacts of DT on operational efficiency and identifying how various uncertainties at different levels can be managed for improved performance. The distinctive application of IPT offers a novel perspective on addressing these uncertainties in technological advancements. Moreover, this research provides valuable practical insights for firms on effective digitalisation process and offers guidance to policymakers in supporting DT initiatives.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it