DEI in B2B selling: a systematic review and research agenda
Bibliographic record
Abstract
Diversity, equity, and inclusion (DEI) has emerged as a central component of a thriving workplace culture, enhancing employee well-being, creativity and innovation, decision-making, and overall performance. Despite its growing importance, the human aspect in a business-to-business (B2B) sales context, specifically the DEI component, is still insufficiently explored. To address this gap, this paper emphasizes the critical role of DEI in B2B sales organizations—not only in promoting a positive workplace culture but also in harnessing the innovative potential of diverse teams. To achieve this, we conduct a systematic literature review that synthesizes existing research on DEI in B2B sales while identifying key research gaps. Drawing on 56 B2B sales articles, we propose a comprehensive framework and a targeted research agenda. Our analysis reveals four key sub-domains: (1) hiring, (2) sales management practices, (3) sales approach and customer interactions, and (4) turnover. Each of these sub-domains highlights critical areas for further investigation. Ultimately, our findings aim to enhance both the academic and practical understanding of DEI’s impact on B2B sales. In doing so, we provide actionable insights to help leaders effectively integrate DEI practices into their organizations, fostering more pleasant and successful work environments.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".