The role of market intelligence in enhancing regional buyer-supplier relationships in agri-food SMEs
Bibliographic record
Abstract
In an increasingly competitive retail landscape, blighted by cost inflation, shifting supermarket strategies and evolving buying structures, small to medium-sized enterprises (SMEs) are more vulnerable than ever to the hazards of retailer processes, such as range rationalisation and product de-listing. Recent research reveals that the longevity of buyer-supplier relationships no longer assures security of retail product listings, rather, the best means of managing channels is through an espousal of market focus (Golgeci et al., 2021).This is of particular concern to regional SMEs, who lack access and expertise to deploy formal market intelligence (MI) and often rely on the familiarity and colloquial nature of their retail buyer relationship to ensure financial stability (Malagueño et al., 2019). There is limited empirical research that explores buyer-supplier relationships from a regional perspective, highlighting the nuances in regional buyer behaviour and the resources SMEs require to support their permanence in retail supermarket sector. Drawing on extant Resource-based View (RBV) literature, our study explores the role of MI in enhancing regional SME channel management capabilities and the buyer supplier relationship. To facilitate this, a longitudinal case-based study of seven agri-food SMEs was conducted to monitor the development of channel management capabilities pre, during and post MI provision. Semi-structured interviews were also conducted with seven supermarket retail buyers. Interview transcripts were thematically analysed using QSR NVivo 12 to identify pertinent themes in the datasets. Post-MI provision, we observed an increase in SME retail contract acquisitions, lucrative price promotional changes, and improved sales performance. Thus, we suggest that the adoption of digitally driven, MI resources provide regional SMEs with a pathway to leverage their power through improved channel management capabilities. This is based upon the condition of an increased learning orientation, driving supplier preparedness, confidence in communication and increased value-add. In modifying behaviours, suppliers facilitated their buyers and enhanced the buying experience. Our research contributes to the channel and marketing capabilities literature by providing new insight into how MI and recent worldwide environmental forces have altered the longstanding buyer-supplier power asymmetry. It answers calls by Morgan (2012) to explore how market intelligence resources are utilised to enhance SME capabilities. Consequently, we contribute to the limited body of literature pertaining to MI usage in SMEs and assess the effectiveness of MI as a marketing resource in the SME context (Kozlenkova et al., 2014). This responds to theoretical criticism of the RBV that little is known about how firm resources are isolated and transformed into impactful capabilities (Corredoira & McDermott, 2020).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".