Salesforce control system in high‐tech contexts: do environment and industry matter?
Bibliographic record
Abstract
Purpose Aims to examine the influence exerted by two key actors – sales person and sales manager – on the salesforce control system used by high‐tech industries. First, since these industries evolve in a highly turbulent environment, external conditions can be expected to modify the relationships between the antecedents and the control system. Also seeks to study the differences in two industries' reactions to an uncertain environment. Design/methodology/approach The investigation was empirical, and a total of 200 managers were surveyed across two industries (the computer and the electrical/electronic industries) in the province of Quebec, Canada. Findings Results suggest that both sales person and sales manager characteristics predict the control system. Second, environment uncertainty and technological turbulence modify the relationships between most of the antecedents and the control system. Third, few differences were found in how the two industries respond to a turbulent environment. The results cannot be generalized, mainly because of the geographical context of this research, meaning that their use is somewhat limited for managers in other regions. The findings nonetheless give managers insight into which characteristics influence the control system and how environment influences the relationships investigated. Originality/value Contributes to the marketing literature since it is the only study other than Krafft's that measures the direct effects that the two key actors exert on the control system. This research is also the first to empirically show the indirect effects attributable to environment. The results are exploratory and should be replicated in other contexts to improve their external validity.
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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 | 0.002 | 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".