Corporate listening: unlocking insights from VOC, VOE and VOS for mutual benefits
Bibliographic record
Abstract
Purpose Comparatively, while the voice of customers, employees, and other stakeholders have been identified as key components of corporate and marketing communication, little attention has been paid to how organizations listen to, make sense of, and use the information provided. The research reported in this article examined how a multinational corporation and its subsidiaries listen to their customers, employees, and other stakeholders and explored how corporate listening can be improved for mutual benefits. Design/methodology/approach This article reports participatory action research within a multinational corporation operating in Europe, Canada and Australia, which set out to become a “listening organization” to improve its relationships and performance. The research was informed by interviews, observation, content analysis of relevant documents, and critical reflection. Findings This analysis illustrates the need for and benefits of looking beyond statistical data to analyze textual, aural and visual data available from call centers, open-end survey comments, complaints, correspondence, social media and other sources, and it identifies methods, tools and technologies for ethical insightful corporate listening. Research limitations/implications This article advocates a “turn” from a focus on voice to focus on listening, noting that expression of the voice of customers, employees and other stakeholders has no value to them or organizations without active listening. Originality/value This paper reports an in-depth study of corporate listening to multiple stakeholders and identifies opportunities for increased insights and understanding that can lead to tangible benefits for both organizations and their stakeholders.
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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".