Salesforce responsive roles in turbulent times: case studies in agility selling
Bibliographic record
Abstract
Purpose Salespeople are at the forefront of the external environment where they act as the first responders to critical events and their resulting business turbulence. How the salesforce responds to turbulence is, therefore, of great interest both theoretically and in practice. The paper aims to rekindle interest in agility selling, which is the most adequate behavioral sales model to exploit environmental uncertainty. Design/methodology/approach An organizational autoethnography complemented with data from in-depth interviews with key salespeople involved in turbulence resulted in the development of eight case studies. Findings Salespeople use agility selling through four possible responsive roles. They amplify, innovate, cooperate or mitigate turbulence to exploit its ensuing opportunity or minimize its negative effect for both the supplier and the customer. The article enhances the agility selling model by putting three core abilities in the forefront: (1) forecasting turbulence from critical events, (2) responding to changes quickly and adequately and (3) exploiting changes as opportunities. Research limitations/implications The article argues that critical events are the cause of the turbulence that the salesforce must deal with before it hits the dyad. Agility selling represents an untapped research opportunity in business-to-business sales, and sales management, as well as within the overall agile organization. Practical implications Sales organizations would greatly benefit in implementing training of agility selling’s core abilities because responsiveness is a valuable tool for salespeople in times of turbulence. Originality/value The study is the first to empirically demonstrate the existence of agility selling.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".