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Record W4411686103 · doi:10.1080/08853134.2025.2515837

DEI in B2B selling: a systematic review and research agenda

2025· review· en· W4411686103 on OpenAlexaff
Roberto Mora Cortez, Bruno Lussier, Deva Rangarajan, Ashwin J. Baliga, Ann Højbjerg Clarke

Bibliographic record

VenueJournal of Personal Selling and Sales Management · 2025
Typereview
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsBusinessMarketingPsychologyAdvertising

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.391
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.323
GPT teacher head0.509
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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