The extent of technology usage and salespeople: an exploratory investigation
Bibliographic record
Abstract
Purpose Increasingly, salespeople adopt, or are being asked to adopt, and use a variety of technologies to increase their selling productivity and efficiency. Given this trend, many researchers have begun to explore the question of sales force adoption of technology. However, little work has been done to consider what happens once this technology is adopted. The purpose of this paper is to report two studies that investigated if and why salespeople had different technology usage and if the extent of usage had an impact on their performance. Design/methodology/approach First, a qualitative study was performed to gain insights about extent of technology usage and the reasons that may explain differences. In order to test some of the research propositions that emerged from the qualitative study, an empirical study was conducted with 130 salespeople. Findings Innovativeness was found to be helpful in distinguishing between different technology usage levels across various technologies (internet, e‐mail, intranet, etc.). Results also suggest three potential antecedents of technology use, as well as a potential moderator of the usage to performance relationship. Originality/value This paper provides a research agenda for studying this important area. Further, the practicing manager will gain insight into some variables that help predict usage extent, and may provide better ideas on implementing and managing the use of technology by their sales force.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".