Bibliographic record
Abstract
Purpose The purpose of this paper is to propose that sales managers use mobile technologies in the working environment to communicate and supportively monitor sales person performance. Design/methodology/approach A model of supervisor monitoring using mobile technologies is conceptualized that specifies the types of behaviours that promote high‐quality working relationships, how mobile technologies increase the likelihood of work‐to‐nonwork role spill‐over that may damage the relationship and why perceptions of supervisor fairness are critical. The paper concludes by presenting strategies for testing hypotheses and for researching mobile technology use by sales managers using qualitative and quantitative methods. Findings Mobile technology use, supervisory monitoring, and relationship development co‐exist in the current workplace. This research heightens awareness of how work‐to‐nonwork spillover may influence important outcomes of mobile technology usage. Perceptions of quality supervisor‐employee relationships are important to retaining and motivating employees. As the workforce ages and skilled workers become more scarce, it is expected that this theoretical examination and ensuing future research will be interesting and important to the twenty‐first century manager. Originality/value This paper aligns research in the areas of leadership, monitoring and ubiquitous or mobile technologies. Previous leadership researches have questioned whether or not the use of different electronic monitoring tools affects the leader's ability to influence others. However, few researchers have examined performance‐based monitoring using mobile technologies, although mobile technologies make it easier for sales managers to monitor non‐traditional work arrangements (i.e. off‐site or contracted work). Furthermore, past research has been inconsistent in explaining how employees view information‐gathering or monitoring by their managers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".