Salespeople and teams as stakeholder and knowledge managers: a service-ecosystem, co-creation, crossing-points perspective on key outcomes
Bibliographic record
Abstract
Purpose This paper aims to explore the increasing importance of the intraorganizational dimension of the sales role (IDSR) based on service-ecosystem theory. Specifically, it examines how firms can improve interactions both internally and with external actors and stakeholders to both create and sustain advantageous “thin crossing points” (Hartmann et al. 2018). Academic research on sales ecosystems has yet to fully harness the rich insights and potential afforded by the crossing-point perspective. Design/methodology/approach After developing and unpacking the paper’s guiding conceptual framework (Figure 1), the authors focus on crossing points and the diversity of interactions between the contemporary sales force and its many stakeholders. They examine the sales literature, identify opportunities for thinning sales crossing points and propose dozens of research questions and needs. Findings The paper examines the importance of improving interactions both within and outside the vendor firm to thin crossing points, further develops the concept of the “sales ecosystem” and contributes a series of important research questions for future examination. Research limitations/implications The paper focuses on applying “thick” and “thin” crossing points, a key element of Hartman et al. (2018). The primary limitation of the paper is that it focuses solely on the crossing-points perspective and does not consider other applications of Hartman et al. (2018). Practical implications This work informs managers of the need to improve interactions both within and outside the firm by thinning crossing points. Improving relationships with stakeholders will improve many vendor firm and customer outcomes, including performance. Originality/value Integrating findings from the literature, the authors propose a conceptual framework to encompass the entire diversity of idiosyncratic interactions as well as long-term relationships the sales force experiences. They discuss the strategic importance of thinning crossing points as well as the competitive disadvantages, even peril, “thick” crossing points create. They propose an ambitious research agenda based on dozens of questions to drive further examination of the IDSR from a sales-ecosystem perspective.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".