Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of product management as a set of organizational capabilities. It aims to investigate product management as a set of boundary spanning capabilities, by empirically relating these to firm performance. Design/methodology/approach A measurement instrument is developed and validated based on the extant product management literature. Using a heterogeneous sample of 63 Atlantic Canadian SMEs in the manufacturing and professional/technical services sectors, data are collected to test the survey instrument and establish preliminary construct validity. Findings Both firm performance and product management measures demonstrate internal consistency. Several product management sub‐constructs demonstrated reliability and in some cases validity, substantiating the product management literature. These included product pricing, sales support and forecasting. Research limitations/implications This research builds upon the literature and indicates that a relationship exists between product management capability and firm performance. This leads to the conclusion that product management, as a set of boundary spanning firm capabilities, warrants future research with a larger more homogeneous population. Limitations include geographic bias, treating the population as homogeneous and lack of relationship to established firm orientations. Practical/implications This research may have practical significance and managerial implications, based on the relationship between product management capabilities and firm performance. This could lead to an increased understanding of how to allocate scarce resources in order to improve performance. Originality/value The paper introduces the concept of boundary spanning, product management capabilities and their relationship to firm performance, by providing preliminarily validation of a measurement scale for product management capabilities of small to medium‐sized enterprises.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".