Bibliographic record
Abstract
Purpose Drawing on the resource‐based view of the firm and the dynamic capabilities perspective, this paper sets out to argue that salespeople's selling skills and their inter‐ and intra‐unit collaborative skills are valuable, rare, socially complex, and inimitable knowledge‐based resources embedded in the human and social capital of field sales units (FSUs). Salespeople's selling and collaborative skills, both directly and interactively, should help field sales units generate greater economic rents. This paper also aims to explore the effect of salespeople's selling and collaborative skills on the level of total compensation through the mediating role of sales unit performance. Design/methodology/approach The data were obtained from a sample of managers of FSUs in 102 large Canadian organizations. The proposed model and its hypotheses were tested using hierarchical moderated regression analysis. Findings Collaborative skills, but not selling skills, are directly related to FSU performance; the effect of selling skills on FSU performance is strengthened by the complementary role of collaborative skills; and selling skills and collaborative skills both individually and interactively result in the payment of higher compensation to salespeople as a result of their enhanced performance. Research limitations/implications Salespeople's selling skills and collaborative skills (both directly and interactively) not only enable the FSU to generate higher levels of performance, but they also increase individual salespeople's compensation. Practical implications It is necessary for managers to acknowledge the role of knowledge‐based resources in building/developing organizational dynamic capabilities. Originality/value This is one of the few studies that explores the strategic role of salespeople in creating a competitive advantage and links the sales management literature to the literature on the RBV of the firm and social capital/human capital theory.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".