Bibliographic record
Abstract
Abstract Text analysis is increasingly used for consumer and marketing insight. But while work has shed light on what firms should say to customers, when to say those things (e.g., within an advertisement or sales interaction) is less clear. Service employees, for example, could adopt a certain speaking style at a conversation's start, end, or throughout. When might specific language features be beneficial? This article introduces a novel approach to address this question. To demonstrate its potential, we apply it to warm and competent language. Prior research suggests that an affective (i.e., warm) speaking approach leads customers to think employees are less competent, so a cognitive (competent) style should be prioritized. In contrast, our theorizing, analysis of hundreds of real service conversations from two firms across thousands of conversational moments (N = 23,958), and four experiments (total N = 1,589) offer a more nuanced perspective. Customers are more satisfied when employees use both cognitive and affective language but at separate, specific times. Ancillary analyses show how this method can be applied to other language features. Taken together, this work offers a method to explore when language matters, sheds new light on the warmth/competence trade-off, and highlights ways to improve the customer experience.
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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".