Persisting changes in sales due to global pandemic challenges
Bibliographic record
Abstract
AbstractThe global health pandemic triggered many challenges for businesses and society, forcing organizations and salespeople alike to pivot, alter their sales strategies, accelerate their digital transformation, and adjust to a 'new norm' going forward. Since some of the changes wrought by the pandemic are likely to persist into the post pandemic era, we asked the questions, how has personal selling and sales management been transformed? What have we learned? And where do we go from here? We identified trends, which we categorized into six broader themes, including sales strategy, sales force design, technology, leadership, salesperson wellness, and customer engagement. Each broader theme includes multiple future research questions on sub-topics such as internationalization, risk management, sales enablement, artificial intelligence, motivation, ethics, mental health concerns, buyer-seller relationships, and more. We first begin by highlighting current research in the field and end with these future research directions to inspire ongoing investigations that will inform and transform both scholarship and practice.Keywords: Future Research DirectionsPersisting Changes in SellingSales StrategySales Force DesignTechnology (A.I.)LeadershipSalesperson WellnessCustomer Engagement Disclosure statementNo potential conflict of interest was reported by the authors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".