Building synergies between operations culture, operational routines, and supplier monitoring: implications for buyer performance
Bibliographic record
Abstract
Purpose Pressured by various stakeholder groups to improve the sustainability performance of their emerging economy suppliers, multinational firms continue to expand their supplier monitoring. Leveraging the strategy literature on alliances and the buyer-supplier relationship management literature, the authors propose that a buyer firm's efforts to proactively develop cultural sensitivity and operations cognizance to understand the operational culture and routines of its suppliers can ameliorate some shortcomings of supplier monitoring, thereby improving the performance of the buyer firm. Design/methodology/approach Using primary survey data from a sample of US manufacturing firms, combined with secondary data of supplier monitoring and financial performance, this research examines the relationship between supplier monitoring, cultural sensitivity, operations cognizance, and buyer firm performance. Findings Supplier monitoring was associated with positive but diminishing returns for financial and sustainability performance for the buyer. Second, increasing cultural sensitivity and operations cognizance for suppliers in emerging economies were associated with improved buyer performance. Finally, the synergistic use of supplier monitoring and operations cognizance was associated with improved buyer firm financial performance. Originality/value While the buyer-supplier relationship literature has mainly treated organizational differences between dyadic supply chain partners as exogenous to the context in which their relationship evolves, the authors posit that buyer firms' efforts to understand such differences can affect the value of buyer-directed interactions, such as supplier monitoring. This research adds to the theoretical understanding of the process of developing relational mechanisms with emerging economy suppliers. In particular, efforts of buyer firms to better understand the operational culture and routines of their suppliers can complement monitoring and are associated with a positive impact on performance.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".