Technology use in B2B sales: examining the extant literature and identifying future research opportunities using morphological analysis
Bibliographic record
Abstract
Understanding the impact of technology use on the business-to-business (B2B) sales profession has been one of the priorities for scholars for over 20 years. While the extant sales literature has focused mainly on stand-alone technologies like customer relationship management and social media, few studies have taken a holistic approach to understand how technology has transformed the sales function and the corresponding impact this change has had on the salesperson and sales organizational level. Employing morphological analysis (MA), we conduct an extensive review of the last two decades of B2B sales research to highlight emergent research topics on how technology use is continuing to influence B2B selling and identify research gaps that still need to be addressed by sales scholars. We characterize the literature on technology use in B2B sales in terms of 6 ‘dimensions’ and 22 ‘variants’ and represent it as an MA framework. Using this framework, we identify 49 research gaps that can inform future research on technology use in B2B sales. These gaps were prioritized using inputs from academics and practitioners and 10 gaps rated high by both groups were identified. We conclude with theoretical and practical implications of our research.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".