Future business and the role of purchasing and supply management: Opportunities for ‘business-not-as-usual’ PSM research
Bibliographic record
Abstract
The raison d'être for this article is simple: traditional ways of researching, theorizing, and practicing purchasing and supply management (PSM) are no longer sufficient to ‘meet the moment’. Scholars need to advance a “business-not-as-usual” footing approach to their work, if they are to make a meaningful contribution to addressing the current and future emergencies, as highlighted by recent extreme weather and the COVID-19 pandemic. Yet, what can this, or should this, mean for a field rooted in traditional business thinking? This article builds on the Journal of Purchasing and Supply Management's (JPSM) 25th Anniversary Special Issue editorial (2019); members of the JPSM's editorial team advance their unique perspectives on what “business-not-as-usual” means for PSM. Specifically, we advocate both thinking much more widely, in scope and ambition, than we currently do, and simultaneously building our ability to comprehend supply chains in a more nuanced and granular way. We explore whether the bias toward positivist work has omitted potentially interesting findings, and viewpoints. This leads to a call to re-think how we approach our work: should the key criteria always be to focus on theory development or testing? Should academics “think bigger”? Turning to specific research themes, illustrations of how our current thinking can be challenged or broadened by addressing the circular economy, and role of purchasing and innovation. Specifically, the focus on the PSM function as an intrapreneur within the larger organization, and the role of innovation and technology in PSM work. Taken together, we hope the ideas and arguments presented here will inform and inspire ambitious and novel approaches to PSM research with significant and enduring impact on the transformation of business.
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.035 | 0.029 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".